SegMetrics — Articles

Attribution Is Not a Panopticon: Keith Perhac on the Unscripted SEO Podcast

Keith Perhac — Fri, 23 Oct 2026 09:00:00 GMT

SegMetrics founder Keith Perhac was a guest on the Unscripted SEO Podcast with host Jeremy Rivera. They talked about marketing attribution, chat-first SaaS and what AI is doing to SEO and paid ads. Read Jeremy’s recap of the episode on Unscripted SEO, or watch the full conversation below.

Key takeaways

Attribution is not a panopticon. With five devices and a dozen ad channels, Keith stops trying to map everything and asks two separate questions instead: where did people first find me (first touch), and what made them convert (last touch).
The messy middle still matters, but only when it answers a decision. Look at it when you are deciding whether Twitter, a blog article or your alternative-to pages deserve more effort.
Chat is becoming the user interface for data-led SaaS. Keith asks his questions through Claude over an MCP server and only logs into SegMetrics for the Monday morning dashboards, but he insists on trust, but verify, with the report shown in the chat.
AI is an average machine. Marketing moves on the weird, crazy, fun idea, so the founder still has to bring the new inspiration.
The website is now the end of the funnel. Awareness happens on social, ads, LLMs, Reddit and Trustpilot, and AI is hiding referral data, so traffic that started in an LLM can look like direct.
Point an AI analyst at your ad accounts. A buried expanded-audiences setting cost SegMetrics fifteen hundred dollars in a Mahjong game in the Philippines, and three agencies over eight years never saw it.

From agency tool to SaaS

Jeremy: Hello, I’m Jeremy Rivera, your Unscripted SEO podcast host. I’m here with Keith Perhac, founder of SegMetrics, who’s going to introduce both himself and his SaaS and say: what is it that you do here, Keith?

Keith: What is it? I’m a people person. So I’m Keith Perhac. I am the founder of SegMetrics, which is a SaaS for digital marketers who want to be able to put a dollar value on any piece of the funnel. So how much is it worth if someone joins a webinar? How much is it worth if a copywriter joins a webinar? How much is it worth if a copywriter from Facebook joins a webinar, and looking at that lifetime value?

It’s a lot of attribution, it’s a lot of marketing stuff. Absolutely love it. Been doing it for 15-ish years at this point. And my background is I ran a digital agency before we built SegMetrics. So we were a CRO agency, conversion rate optimization, and we built SegMetrics to be our internal tool. And then it became really popular and we were like, let’s go all into SaaS. And so here we are.

Jeremy: A familiar story. My background was with Raven Tools. They brought me over from Homes.com, and they had been an agency. They had developed tools to do monthly reporting better, simply and more visually, and turned it into a SaaS.

Keith: We used Raven Tools back at the agency. Yeah.

Jeremy: I think that that’s a necessary component to understand the pain point, and how much a person is willing to pay out of their payroll or their other budgets or their workflow to not have to do that thing. And usually it’s some ridiculous aspect of the job that we’re assigned as digital marketers.

Keith: Yeah, the reporting’s the worst part, right? There’s two pieces that every agency, I think, schleps on to juniors or contractors, and that’s the reporting side and that’s the budget management side. Because they are the worst parts of the job. Everything else around it is great, but those are the two worst parts. So having a tool that takes that five hours of reporting and turns it down into twenty seconds or a minute is so valuable, right?

Attribution is not a panopticon: first touch, last touch and the messy middle

Jeremy: Okay, so attribution is such a curse word for me.

Keith: Yes. I thought it’d be easy when we started. So, sweet summer child.

Jeremy: Sweet summer child indeed, sir. How do you take on that conundrum of attribution? Because it has so many facets. For those that are listening, think of attribution as being able to tag or tie specific actions or outcomes to their originating sources of that digital activity, whatever it may be. It sounds simple, but then anybody who has logged into GA4 knows that Google is intentionally trying to make things harder for us.

So you have poisoned wells, you have UX and UI decisions that are inscrutable at best, you have decisions to portion off and say, we can’t allow you to track that because of privacy, GDPR, European legislation. Where do you begin? How do you unravel attribution?

Keith: I think the first thing people need to understand about attribution is that it’s not a panopticon. Back in the day, because I’m an old man at this point, you didn’t have that many touch points. People didn’t have that many devices. They had their computer, maybe they had a phone, but they weren’t really doing anything on that phone, right? And people came in from an ad, maybe they’d browse the site for a while, and then they’d sign up for your list and maybe they’d convert down the road, right?

Maybe they would have two different ads that they interacted with, but it was very low stakes, right? Now I have five devices sitting on my desk right now, right? We are on social media, we are on Google search, we’re on AI now, we’re on Facebook ads, Google ads, Reddit ads, YouTube ads, LinkedIn ads. There are so many touch points that a person goes through now that it is impossible.

People always talked about the multi-touch attribution and this beautiful, what influenced what? And it’s like, they all influenced everything at this point. There is no way to make sense of that data. So when you’re talking about attribution, you have to think about attribution in: what is the question I am trying to answer? And usually that’s two things. That is, where did people first find out about me? And that’s first touch attribution.

So what was the first click that someone had when someone came in and started doing all this stuff? And that’s what I want to know, because that’s how I acquire people. Then, what is the last thing they did before they opted in or purchased? What was that last touch attribution? And we have all these different attribution models, but those are the two that I really look at as: what am I trying to optimize for?

Because I’m trying to optimize for people finding me and people converting in a way that I want them to convert. And so if I focus on those two things as separate questions, then I can start to ask, okay, what are the first touch and last touch that connect and get me towards there? And then what are the influencers in between? What are the blog posts people like? Do people enjoy my Twitter account? Does that matter at all?

I can see that, but the main thing I’m trying to answer is: where do people come from? How do I get them to convert? And so then I can look at those and pare down all this, just noise at this point, to what is actually important for my business.

Jeremy: I do like the concept of splitting apart first touch attribution and last touch attribution, and just accepting that there’s some sort of weird messy middle that’s going. Maybe the first touch is on AI, but then they take it to Google, and then they email it to their wife, and their wife then does her Copilot search, and then comes back and emails him back again for him to directly land on it.

And so you’re like, my website’s doing awesome, I got direct traffic. Maybe, but…

Keith: It’s not, right. But I think you’re exactly right. That messy middle is important for the influence side of things. But whenever you’re looking at any data, especially attribution: what is the decision you’re gonna make from it? If I know what my first and last are, where do people come from, what causes them to convert, I can make decisions off that.

But the messy middle, unless I’m like, do I wanna focus more on Twitter? Okay, I’ll look at the messy middle and see if people are engaging with Twitter. How is this blog article doing? Then I can look at that. How are my alternative-to pages doing? So then I can start making decisions on the influence, even if they’re not first or last. And I think that’s important, but it’s not as crunchy as first and last, to me anyways.

Chat is the new user interface for SaaS

Jeremy: Here’s a question for you and SegMetrics. What is your operating product management viewpoint on integrating with Claude Code or with GPT? Because I’m so deeply steeped in Claude Code day in, day out, to get this podcast done more effectively, that almost all of my SaaS subscriptions, I really don’t log into them anymore.

They’re almost more data sources. And that feels like a betrayal of the early product manager me, of how much time and energy I spent as a product manager thinking about the UX, thinking about the UI. This widget here needs this little signage, so it makes sense that we need better onboarding. But if you’re just getting the function and data over to Claude Code or GPT to process and answer the questions, is that the future of SaaS, just data transformation?

Or is it just an extra middle burden in between, because not everybody is one hundred percent deep into Claude Code? They’ve got their toes dipped into the water.

Keith: Yeah. I have many thoughts about this. So I think that AI, or let’s just call it chat… I think that chat is the new user interface for a lot of SaaS.

I think that there are things that are always going to need some amount of UI, or some of the little fiddly bits. I can’t imagine using Photoshop through a chat lens, right? Okay, now move your mouse a little bit here and click here. But I think that a lot of data-led SaaS companies, so things like project management tools like Asana or Shortcut or any of that, as well as analytic tools like SegMetrics, become an engine for the AI to function.

And the main interface that people are gonna have with that software is through chat. We have an MCP server that we’ve been working on forever, and it works really well. And at this point, I don’t log into SegMetrics except to look at the Monday morning dashboards, to share with the team. Every question I ask, I ask through Claude.

We have a data layer that makes sure that the data is correct when it comes out, and double checks it and does all this stuff, so that I know it’s accurate data. But the main interface that I’m using to see, hey, how’s my webinar doing this week? is all through Claude.

Asking Claude instead of logging in

Keith: And there’s a couple of reasons for that. One is that data analytics itself is super complicated, right? We have very specific filters. We were talking about attribution models, and making sure that the attribution model is correct and that the filters are correct and all these things. And then looking at secondary questions. Okay, I found where people are coming from. Then are they copywriters or developers or designers? What’s their industry?

All these different questions I can ask take a lot of time for a human, and it just goes burr and goes through for Claude. Claude can make those secondary questions when it would take us another 10 minutes to do so. It can do it automatically. It runs in the background and it chirps when we’re ready, having all those reports together. Because Claude, I think, is not faster, but it is doing it without me having to do anything.

And so that’s the power I see in it, and that’s where I think a lot of this is going. It’s just like, I just need to know this information. Okay, now I have the information. I’m confident in it, because I can check the numbers and confirm. And now I want to go do something with that. So: these are the ads that aren’t performing, turn them off. These ads are performing, increase budget by 20%, put up new ad creatives.

All that stuff now happens in Claude, so I don’t have to deal with the messy UI of all these different systems. And it just all works in one system. And that’s the value to me.

Trust, but verify, and confirm fatigue

Jeremy: That makes sense. What’s the downside? Because everything that gets optimized, there’s a trade-off. So what in that system are we trading off to get that capability?

Keith: I think we’re trading off two things. So one, we’re trading off trust. And that’s why I’m a big proponent of trust, but verify. And it’s one reason why, when SegMetrics gives an answer to Claude, it always shows the report in the Claude chat, so you can see exactly what numbers Claude is working off of.

And I think the second one is less software and more just how we interact with Claude. What was it called? It was confirm fatigue. Where you keep pressing enter because you’re like, yes, Claude, do that. Yes, Claude, do that. Yes, Claude, do that. And we stop thinking. And I think that is a problem when Claude is giving us so many good ideas and so many points. We stop thinking critically about some of them.

But that’s not necessarily a SaaS problem, that’s more of a societal problem. But that’s a big one, I think, to be honest.

Jeremy: Yeah, I myself run Claude Code on dangerously skip permissions. I like to live dangerously. But even with that, there are still times that I still have to confirm. And then occasionally I regret just how far I let it go.

Keith: Yeah. The scariest thing Claude ever says is, “I was blocked by the sandbox, so I went around the sandbox.” I was like, no, don’t do that, Claude. Don’t do that.

Jeremy: It is societally, yeah, a larger concern. I know it came up in Ross Simmons’ interview with Lisa Raehsler. They were talking about PPC, and he was concerned because there’s fewer interns that are suffering. And the fewer interns that are suffering means there’s fewer people in the next generation who would become full-fledged entrepreneurs, or pick up the lead and become the next PPC person.

And the number of entry-level places being replaced by Claude, on the one hand, that’s more optimal from a business hiring perspective. But it’s definitely so much about how our society is so short-sighted about this topic.

Keith: It’s short-sighted.

Jeremy: But I don’t know if anybody can afford not to be short-sighted.

Japan, America and AI as an average machine

Keith: I agree. And I think it’s a difficult position. And I think it’s interesting. So a little bit of my background: my entire adult life was working in Japan. So I worked as a salaryman for eight, nine years and then started my own business there. And I’ve been looking at the way that America and Japan are using AI differently, especially in business. And in Japan, it seems to be, hey, we’re not firing anyone. Just everyone use AI and figure out how we’re going to make everyone more productive and be better in the company and be able to move faster, blah blah blah.

And America really seems to be like, man, we can get rid of everyone. We just want to be like George Jetson pushing the button to start the factory at the beginning of the day, and then that’s it. And I see both sides of it. Especially for a small business, if I don’t have to hire twenty people and I can still get out the same marketing, that’s a huge boon for me.

But yeah, it’s something I wrestle with every day. Because also, AI is averages, right? AI is not human. And you don’t have to be an expert to come up with a weird, crazy, fun idea, right? And especially in marketing, that’s what moves things forward, is not the average. You can’t do the same thing as everyone else and succeed in marketing. You have to do something different.

And because AI is an average machine, there has to be something that brings in that new idea. And for us right now, that’s us. That’s the founder, that’s the person running it. But eventually we get stuck in our own ideas as well. And so, okay, what is bringing in that new inspiration, and that new… I can’t think of the word… ingesting new life into our marketing and our ideas and our business?

And not to get way too philosophical, but I think that’s one of the things that we’re going to have to start to wrestle with: that everything’s just going to become average.

Jeremy: Yeah. I’ve had that conversation with my friend Michael McDougald of Right Thing, of what is the origin of AI slop? Is it actually the AI, or is it people just getting lazy and taking the easiest route? Not thinking it through, just the shortest prompt with the least checking on it, the go-go-go mentality that you pointed out.

I’ve studied Japan quite a bit, and there’s also a much more patient culture in general. So I’m not surprised to hear that their adaptation is more like what Brett Schklar, my previous interviewee, said: The only reason you should be scared of AI taking your job is if your job is literally just executing tasks. Because it’s not taking your jobs, it’s taking your tasks.

But if your entire job is just tasks, well then yeah, the writing’s on the wall.

SOPs, task software and AI agents running the project

Jeremy: Which is ironic to me, because in a certain way a lot of the previous generation of product management and project management and agency work has always been a push towards, well, let’s turn this into a list of tasks. We’re going to find an SOP, and the SOP will be repeatable, and you won’t deviate from it, and you do this SOP. It’s my job to think of them and you do them.

So then you basically turned your employees into task management tools, task executioners.

Keith: I will push back slightly, that American companies, I think, are very bad at SOPs. The number of companies I’ve worked with that have good SOPs that people actually follow is very low. But it’s the superpower.

Jeremy: I don’t say they’re good at coming up with them or good at doing them. But it ironically is the natural outcome of being given an Asana. That’s the natural structure of that product management software, is to break stuff into groups and then to assign tasks and to check them off. So three-fourths of my agency work has just been figuring out, okay, well this one’s on Monday.com and this one’s on Trello and this one uses Discord.

But it all just follows the same to-do functionality. Here’s the task, do the task, check it off, when’s it due?

Keith: Yep. I mean, in the SaaS community there was always a thing that our biggest competitor is not other software, it’s Excel. Right? Because everything is just tasks. It’s just lines that you are doing. And I would agree: three-fourths of SaaS companies, I think, that are not B2C, social media kind of things, are some sort of task management, task delegation, just pushing things from left to right.

And that, I think, is the biggest thing that all goes away. I was talking with one of my founder friends yesterday, and he was showing me his system. He built a task management system for his AI agents. And all it does is it collects all the tasks that come in from Help Scout and Stripe and Sentry and bugs and all this stuff, and puts them in a long list.

It has an AI that goes through and manages, says, yeah, we’re gonna do this. We’ve only had one of those in like three months, we’re not gonna do that. This is the prioritization. And then it has other AI agents that are just looking and saying, okay, I’m gonna pick up this one. Should I do this or not? And then they’ll go do it and they’ll come back and say, hey, approve this or not. It’s like they’re running that whole project management process.

And I’m like, well, there goes Asana, right? There goes Asana, and there goes the project manager who is running Asana for your company. And man, so much goes away at that point. And now I’m depressing myself.

Zero-click and the brand channel

Jeremy: It comes and goes in cycles. The excitement and depression comes and goes in cycles. I’d say when ChatGPT was spiking in its huge popularity was the worst turn for SEOs, because all of our budget was getting assigned to these grifters and LinkedIn folks saying GEO’s the future, LLM SEO forever. Come on, CEO, go ditch that SEO campaign, give me all your money. And they win.

Keith: That’s quieted down. That’s not a thing anymore.

Jeremy: But now, a year later, they’re having to prove out that channel, and it’s not a performance channel, it’s a brand channel, in my opinion. And a couple other people, I think, have chimed in, like Rand Fishkin pointing out…

Keith: Yep. Amanda Natividad and zero-click marketing and all that. Yep.

Jeremy: Yeah. Treating LLM surfaces as a performance channel, it’s just not working the same. But how do you adjust your expectations on the conversion side when you have systemic change now? SEO has less power in certain ways, because we don’t command as much search volume. It gets fractured, it gets splintered. I was just looking for an open source database tool, and honestly everything that they did was by the book and really good.

Articles showing the differences between different open source platforms, putting out different listicles and views like that. And they just have a thirty-four percent loss of clicks, not because they actually lost that much ranking, but it just got subsumed. There’s less clicks. And we looked at some of these queries, and okay, you did technically lose ranking. You went from second to third, or first to third.

But we look at these, and it’s cited here in the LLM, and then below the fold it’s number two or three as the article. And the other one that’s showing up is Reddit. So you’re really not going to push Reddit out of the way, and you’re not going to displace the LLM. I can’t recommend investing more in backlinks, or rewriting that article to try to rank higher. It’s just as high as it is.

And there’s a thirty-four, fifty percent loss in that organic traffic that was driving people on through the funnel and doing the downloads. So is that just the state of how we have to accept things, or is there some piece of it that I’m missing?

Hidden referrers and the website as the end of the funnel

Keith: No, I think there’s a lot going on. So one is, it reminds me of when Google took away keywords from search results. I remember when you clicked on a link in Google, it would show the search query on your page, so you could track how people got to your site. And they took that away because they wanted to put that in Google Ads. And the entire marketing industry screamed.

And most people now don’t even remember that that existed, right? We just got used to it. And the two things that are interesting right now, that are going on behind the scenes with AI and search, is that first of all, AI stopped sending referral information. So when you visit a site and you click on a link and you go to a website, it sends what site you came from. AI is now hiding that from the website.

So normally when you click on a link in Google, you go to the site, it says, hey, this person came from Google. Now they’re hiding it, so it looks like direct. So that’s just mean. That’s just kicking someone where they’re down, to be honest. But the other one that’s really interesting is that there is a lawsuit going on where, when you are searching in AI for something, it is actually doing a Google search behind the scenes and getting the information from the Google search to give you.

They don’t have their own search engine. And Google noticed this and said, well, we’re gonna start poisoning these. And so if an AI bot searches Google and gets the information, it’s then poisoned with false information to send back to you. It’s a whole thing. We as marketers are resilient. We’ve gotten over worse before. We’ll get over this, but it is frustrating as heck why it happens, is really my view of it.

We’re going to figure this out. It’s going to calm down. But I think one of the things Rand and Amanda really said about this is this zero-click marketing thing. For so long, we were convinced, or it was the case, that the site was us. Everything else was peripheral to the site: our social media, our ads, our blah blah blah. Everything came back to the site.

And now it’s different, where it’s no longer just the site. The value of the site has gone down. It’s not an awareness play. It’s the end of the funnel. That conversion play is your website. And the awareness is social media. It’s ads, it’s LLMs, it’s all this stuff around it, that you now need to curate just as importantly as you curated SEO before.

You still have to have the keywords. You still have to have the right things. The SEO has not gone away. It’s just changed where it’s being leveraged. And as someone who hates social media, that’s a huge pill for me to swallow. But I kind of agree. That’s where I see the conversations happening, and that’s what LLMs are referencing now. So when people say, hey, is SegMetrics good? It’s looking not only at my website, but also at Trustpilot, and it’s looking at social media and Reddit and all these places to come up with that answer.

And so we have a lot larger surface area now to manage, for how we show up to the world.

You can’t hire enough SEOs to change the opinion of the internet

Jeremy: Matt Brooks of SEOteric put it as: you can’t hire enough SEOs to change the opinion of the internet. But that is literally now your job on GEO, on LLM SEO, is influencing it. It’s either a heyday for digital PR or a call to action for SEOs to expand past their preconceived rules and boundaries, to embrace organic as multi-surface, multimodal, multi-website and beyond.

And also tying it back to the product itself. So the best product marketing you can do is fixing that bug that’s poisoning fifty percent of the people who are like, well, I hate it, and it’s supposed to do this and it doesn’t. If your software is supposed to do something and it doesn’t, it’s no longer a customer support ticket that’s just going to quietly sit, and somebody is going to maybe not use the tool or lose it.

They’re going to blast you on Reddit. And if they blast you on Reddit, that’s discoverable in search, and that’s now a search impact.

Keith: And because AI wants to be average, you can have a billion glowing reviews and one bad review, and it’ll say, well, a lot of people like it, but there were some people who say that they kicked my dog, or whatever, right? And then that shows up in those LLM results. Interesting story about LLM results. We were doing some influencer outreach, trying to get some influencers who are using our product to talk about us, and I needed something to manage them, right?

And I asked Claude, okay, I need an influencer management tool. What do you recommend? And it went burr and it gave me three things. And I looked at the first one and it looked good. But then I started looking at it, and they misspelled some of their customers’ names. And some of the businesses are wrong. And there’s a typo over here. And I said to Claude, Claude, the site looks so sketchy. Why did you recommend this as your number one?

And it’s like, that’s on me. It has all the things that I look for as an LLM to recommend the site. And so I recommended it highly. And it said, looking at it again, you’re exactly right. This is sketchy AF. You should not use it. So yeah. So there’s SEO, and there’s SEO for bots now.

SEO for bots: wallets, voice agents and APIs

Jeremy: Yeah. One of my interviewees does optimization for big e-commerce, because there are now bots that have wallets. And so the old chestnut of should you write for bots or humans? Well, humans are going to spend the money. Not for long. It’s growing, especially in repeatable restaurant industries. They’re automating inventory, and as people sell stuff they’re scanning the QR, and then they’re finding e-commerce vendors who are at the best price.

The bot is optimized to go find the best price, for quality, within parameters, and purchase it. And so no longer does the wisdom of just write for humans necessarily hold up. Although one must keep in mind that most bots are programmed with criteria based off of what they think a human might want. So I’m not saying abandon all hope or abandon good writing, but do it with more nuance. Consider the impact of AI on your ecosystem.

In a lot of ways it’s good. I mean, 68% of 1,824 local businesses never responded within 24 hours, according to the Ignitvio Lead Response Study. That changes as more businesses adopt third-party call pickup or immediate callbacks with an AI voice agent that is scary good enough to actually hold a conversation with, in most cases now.

I’ve had conversations with AI where afterwards I’m like, that was AI. I just needed something simple, and it answers the questions, and there’s ers and ums and gaps. And like, shoot, I just talked to a robot. But it got the job done.

Keith: That’s creepy. I don’t like that. And going to your point about the robots buying things, I think this is why it’s important for businesses to have APIs. Right? It used to be like API was really an enterprise-level, developer-only thing. Not anymore. Because now even people who are non-technical say, hey Claude, I need to go update my users in Kit. And it will go.

It looks at the documentation that they have published on the site, it connects to the API, and it starts doing the stuff. And if bots are not able to use your service through a system like that, then as a business, you’re not supporting what’s going to be, I think, the largest use case of people using your software, and automations using your software, later on.

Eating his own dog food: the Mahjong game retargeting leak

Jeremy: What’s your marketing mix yourself? Where are you eating your own dog food? A lesson that you learned by analyzing your own attribution network and actually applied. And if you haven’t, you can be honest and say I haven’t, but I’d be surprised if you hadn’t found something and made some change. What’s that look like for you?

Keith: We’re very big into it. So I’ll start with what we’re not doing. So we don’t use AI for writing, and we don’t use generative AI for images right now. We do have some on the blog. I don’t like them, which is one reason I don’t use them. Because the quality is not great. We have tried everything to get generative writing and generative art to look good. It never works. You can always tell it’s AI.

So we do not do that. It’s all handwritten. We work on it. We use AI for editing and stuff like that. But okay, let me talk about the application of some of that. We’ve worked with a number of ad agencies over the years. We were working with this one recently, and we had a retargeting campaign going, and it just wasn’t providing value. And we’re like, the numbers look good. We get reports every week.

And finally there was this one report I saw, and said, hey, you had, I think it was, three hundred thousand retargeting clicks last week. I was like, three hundred thousand retargeting? That’s HubSpot-level people coming in a week. There’s just no way we have that many people being retargeted to. And I was like, okay, you know what? Claude, go. So we have it hooked up to SegMetrics, which is also hooked up to all the ad platforms. I was like, just go figure out why.

And it went through this, and it’s like, you have these settings where the ads should be displayed and all this stuff, blah, blah, blah. But there were a couple of settings deeper down that were overriding it. And so we had spent fifteen hundred dollars on those little mobile ads in a Mahjong game in the Philippines. And I was like, we did who with the what now?

And that was not the only one. At some point there was an extra setting that had gotten expanded audiences on, not on the main ad, but somewhere deeper down. And that had influenced the other ads. And this is what I was talking about with complication. Just one ad platform is so complex. We had had three agencies work with us over like eight years. None of them ever saw this. It got turned on at some point, and I was just beside myself.

So okay, now we’re running all of them through Claude. We’re having Claude do the analysis. We’re having Claude make the changes. We have it double check. I go in three times a week. Claude pings me and says, hey, here’s how it’s going, here’s what I recommend. I push back, I say, really? Do you think so? And we do the whole kit and caboodle, the whole rigmarole, and we come up with something good.

And now we’re getting actual freaking leads for really affordable prices in the industries that we want. And I’m like, we’ve been struggling with this forever. And it’s no one’s fault, but marketing is so complicated right now that it is impossible to see every single facet of the technology, what you have running, and all that stuff. And so being able to get that insight into it is really important to me.

And then going into what do we do with generative AI. I build out ads and stuff based on our websites, using HTML. Claude builds it out to say, okay, this is the landing page, this is the copy we want. It has all of my headshots, it has all of these designs that we’ve built over the years, and it puts them together, it animates them. It’s amazing. And then I’m like, and now publish them. And it’s like, well, I need this in these 50 different sizes. Okay, make them in those 50 different sizes and then publish them.

And I published 30 ads over two platforms, with I think fifty or sixty different sizes each, in motion and static versions, and it took like twenty minutes for Claude to do. Whereas when I was creating those images by hand and rearranging each one, it would take forever. And I have a contact sheet. It gives me an entire sheet of all of the ads that are running, what they look like, what they’re gonna look like at different sizes on different things.

And I just scroll through and I go, yep, yep, yep, yes, yes, nope, nope, nope, change this. And then it just goes. And yeah, it’s freeing. It’s honestly freeing, because that was the part I hated about the marketing. I’ve done the creative part. I know what the ads wanna say, I know what the imagery is, I know how I want to do it. I just now have to build out eight hundred different versions of this ad and then upload them one at a time to Google, and then Facebook, and then LinkedIn.

150 integration pages and SEO on steroids

Jeremy: Yeah, there is an aspect of it that is quite freeing. I’ve felt this way a long time, and it’s come up in a couple of interviews. There was a project where I was hired to rewrite meta titles and descriptions by hand for two hundred pages. And I was very happy when I had the original idea: hey, you need to rewrite your meta titles and descriptions to fit each page properly. And they’re like, yeah, do it.

I’m like, okay. But we want you to do it personally. I’m like, okay, you sure? Yeah, do it. I’m like, okay, it’s gonna be this much. Got it, you’re budgeted. And it was the worst week of my life.

Keith: Yeah, but there’s no amount of money that makes me want to do that.

Jeremy: Just sitting there, pulling up each individual page, feeling like, okay, at a certain point, am I repeating myself? Am I not repeating myself enough? These are the rules that I set down. Am I cheating them? Should I make that more creative? Just the double guessing. And now I’m like, okay, I need unique titles and descriptions that match the intent of this page, following these rules here. And I have a prompt already set for meta titles, and I can get that done through a prompt.

And since I’ve got my WordPress site already connected through application passwords and REST access, I don’t even have to log into my WordPress site to edit meta titles and descriptions. So I can take it from concept, I need to optimize this, to prompt and execution so much faster. It feels like SEO on steroids.

Keith: This was one thing we wanted to do. So we have 130 integrations, and I’ve always wanted to do the Zapier strategy of integration-plus-integration pages. So for example, we have an integration with Kit and we have an integration with Stripe. And I always wanted to have integrate Kit with Stripe using SegMetrics, get the data together.

And 130 to the 130th power is like, I think it’s like half a billion pages. It’s just some inordinate number, right? And it’s like, man, I can do it programmatically, but then all the pages have the same content, which means that Google’s going to ding you. I need similar content, but slightly different. It can be the same thing, just worded in a different way, so that I don’t get dinged from the Google duplicate content thing.

And now I can just do it, right? I think we started with 150. So we built out 150 of the combination pages. They look great. They are ranking well on Google, because people are searching for these things. But it was not something that I could feasibly do before we had this automation layer going. I could do it from a technical standpoint, but then there was the creative side that was the issue.

What’s next: SegMetrics.ai and the doing layer

Jeremy: To wrap up this interview: what’s next on that product management sheet for you? What are you guys bringing to market next? Any resources you put out recently that you’d point my listeners to, either about attribution or how to apply analytics? Anything that you built that’s helpful for the community, give a shout out. I’ll make sure that it gets linked in the show notes.

This episode will be up on unscriptedseo.com, jeremyriveraseo.com, seoarcade.com, with those SOPs that we talked about. I turn each of my interviews, extract what are the top SOPs, and put it into a library. So if you’ve got resources to add to that, they’ll be linked either in the SOP or in the recap articles.

So give a shout out for what you’re doing, what your plans are, what you have built, and how you can help people.

Keith: That’s awesome. That’s so good. Yeah. So I think the biggest thing we’re doing right now is we just launched SegMetrics.ai. Our normal webpage is SegMetrics.io, but we launched .ai. And this is really taking SegMetrics and turning it into that engine for people to be able to use it in their marketing. And so it is not just that knowledge base, but it is also the doing layer.

And that’s what we’re launching this week, next week. Which is not just, hey, I can ask questions, but actually: okay, now launch these ads. Now do this stuff. Now edit my people in Kit, right? That doing layer. We’re also teaching people on that SegMetrics.ai website how to build out the marketing engine for their business. So that you’re not logging into all these systems, so that you have your daily reports come in, you have your weekly updates, you are able to control stuff through Slack or whatever chat you’re using, so that your marketing becomes proactive, not reactive.

And so we’re doing a lot of education and teaching and helping people set that up in their own business. So that’s really exciting for me.

Jeremy: Fantastic. Thanks so much for sharing your views on attribution and AI. Keith Perhac of SegMetrics, thanks for stopping by.

Keith: Thanks so much for having me, man. That’s been great.

Listen to the full episode on Unscripted SEO.

Ian Garlic: How top clients vet you on YouTube (Episode 19)

Ryan Johnson — Thu, 15 Oct 2026 16:00:00 GMT

Your next big client is already checking you out. You just can’t see them.

Before they sign up, book a call, or reach out, high-value buyers do their homework. They search your name plus “reviews” or “legit.” They watch your YouTube videos on a Sunday night. They check out your About page.

They don’t like, comment, or subscribe — so none of it shows up in your funnel.

That’s the invisible pipeline. And most businesses aren’t built for it.

In this episode of Data Beats Opinion, SegMetrics founder Keith Perhac talks with Ian Garlic, founder of Video Case Story, who helps attorneys, coaches, and other experts land bigger clients with YouTube. They dig into why time with your client is a better metric than views, the videos you need everywhere buyers check you out, why simple, repeated content beats clever content, and how to treat YouTube like your website as AI reshapes search.

If you sell a high-value service and your videos feel like they’re disappearing into the void, this one’s for you.

Watch the full episode →

Meet Ian Garlic

Ian Garlic is the founder of Video Case Story, where he helps experts and trusted authorities — from attorneys and dentists to clients like Gino Wickman — land bigger clients using YouTube. His approach starts with customer stories, then turns them into videos that show prospects exactly how you work and why you’re different, placed everywhere buyers go to check you out.

Connect with Ian on LinkedIn or at Video Case Story.

Show Notes

[00:00] Meet Ian Garlic
[02:28] How AI is changing video and search
[04:06] Why search is moving to YouTube and Reddit
[08:17] Time with your client is the number one metric
[08:55] The two minute malpractice study
[09:58] Curation is the new trust
[12:26] Optimizing for views you don’t want
[14:58] Where to start with video
[16:50] The biggest video mistakes experts make
[18:00] Why simple, repeated content wins
[22:34] How repetition affects AI search
[25:42] The fish in the barrel strategy
[27:52] Reviewing the SegMetrics search results
[31:31] Deconstructing a webinar for YouTube
[32:53] Do you need visuals or B-roll?
[34:45] Why AI design all looks the same
[36:28] Keith’s AI data analysis fail
[38:16] Treat YouTube like your website
[42:48] Running high intent ads to videos
[44:30] Ad costs and generic AI content
[48:25] Where to find Ian Garlic

About Data Beats Opinion

Data Beats Opinion is SegMetrics’ monthly podcast exploring what’s actually working in growth and marketing today. Host Keith Perhac, founder of SegMetrics, sits down with domain experts to uncover practical strategies you can implement — not just theory.

Try SegMetrics Free

Want to see what is and isn’t working across all your growth channels — including outbound sales, paid ads, deal pipelines and content marketing? SegMetrics provides full-funnel, cross-platform, lifetime attribution so you can see what’s actually driving revenue, not just clicks. And you can test-drive it for free.

Start your 14-day free trial today.

Data beats opinion. Growth beats guesswork. 🚀

Transcript: Ian Garlic – Ep 19

Every marketer knows they should be doing more video. So they start a YouTube channel, they post some content, and they wait, and nothing happens. So they post some more. Still nothing. And eventually they just kind of give up.

They’re convinced that video just doesn’t work for their kind of business. But what if the problem isn’t the video? What if it’s that you’re optimizing for the wrong audience entirely? You’re chasing views from people who will never buy, while the clients who would pay you are quietly poking around YouTube on Sunday night looking for someone they can trust.

In this episode of Data Beats Opinion, we’re talking with Ian Garlic, founder of Video Case Story, who has built video strategies for attorneys, coaches, and some of the biggest names in online business. We’re digging into why the invisible pipeline is more powerful than any funnel you’ve ever built, the two-minute data point that explains why your best customers find you before they even reach out, and why, in a world flooded with AI-generated slop, showing up as a real human on video might be the highest-leverage marketing move left. Let’s start the show.

Keith: Ian, thank you so much for joining us. We had some good conversation before we hit record, so keep that good conversation rolling, man.

Ian Garlic: We always do. We always have awesome conversations. It’s like, we might as well just record ‘em all.

Keith: Yeah, exactly. Well, last time we got on the phone for something — I don’t remember what — it was just a quick, like, 10-minute call. We were on the phone for like an hour and a half, just talking about all this stuff. It just does not stop. So, excited to have you. For people who don’t know, give me the ten-second pitch, ya?

Ian Garlic: We help experts, you know, trusted authorities, land big clients. From attorneys to dentists to — you know, Gino Wickman’s one of our clients — land big clients using video, YouTube, what I call the invisible pipeline, which, you know, is scary to people, and pre-sold clients, because we get those videos in front of them. And the thing I always say is, we’re optimizing for the number one metric, which is time with your client. And so for an expert, that’s what they have to be doing: getting more and more and more.

Now, I think all the stuff you do is really important, and that comes secondary, because if you can’t get in front of them with the right message… That’s what we help people do, is find the message.

Keith: Exactly.

Ian Garlic: And we start by collecting customer stories and listening to your customer, ‘cause they have all the answers. So that’s why we’re called videocasestory.com.

Keith: Awesome. Very cool. Yeah, and we jumped right into this. So we were talking about how AI is changing video, how AI is changing search, how AI is changing discoverability, even, with some of this stuff. Do you find that AI has come in and kind of devalued or unvalued… How do you think that’s playing out right now in the world, right?

Ian Garlic: I mean, well, it has definitely increased the value of video. You know, YouTube is a big source of a lot of the data for ChatGPT, et cetera, so that’s important. But also, YouTube has metrics that those AIs look at, like views. It can see the number of views; it can see percent viewed. If it gets into the API, it gets percent viewed. Likes, comments, all that stuff. And then ranking in Google too. And so it’s definitely changed. Video’s become more important. And I also think another thing that people are seeing — and I’ve talked to website companies too — is that pure website visits are way down.

Keith: Oh yeah. Oh…

Ian Garlic: Because AI’s kind of taking that away. But people still wanna see you, hear you, and they’d rather go someplace familiar, like YouTube, or other platforms now.

And then if you talk about time on site, you know, YouTube’s time is actually going up while everything else is going down. People are spending more time on YouTube. So I think there’s this massive, massive shift because of AI, and video, yeah, is more important.

Keith: I think it’s fascinating, because one of the things that SparkToro is talking about right now is zero-click marketing, and this idea that your homepage is not just your homepage anymore. It’s really all of these places that you are. And like you’re saying, people search for a solution, or even they search for your brand name — the number one things that come up are gonna be AI and video. A lot of the top, before-the-fold search results now are YouTube, and people are going to YouTube. YouTube and Reddit are now the two largest search engines for solution-based searches, right? That I’m looking for a problem to solve, which is mind-blowing, right?

Ian Garlic: Yeah, it’s—

Keith: Exactly right. That’s where people are going for this information now.

Ian Garlic: Well, you know, and I’ll put a — I’ve got a free asset. I’ll give it to your audience. videocasestory.com — we’ll make it /keith. I’ve got a calculator, and it’s got the list of all the places that we try… because I always say it’s YouTube, ‘cause YouTube ranks, right? You should have YouTube; you should be driving YouTube.

And there’s like 40 different places. And also we have this whole list of searches that people do when they’re investigating you. And, you know, so few people optimize for that — that fish — when they’re ready to buy from you. They do all these crazy funnels, and then they don’t think about the searches or all the other places, like you were saying, that people were going to.

And it’s so important to think that way. You have to think about how the buying process has massively changed over the past 15 years, and then in the past two years it’s even more massively changed, and we’ve moved away from this whole Yellow Pages philosophy. Like, I have a problem, let me go look up the solution, the person for the problem. And people are still optimizing for that. And — this is just a side note — everyone’s calling it answer engine optimization. I’m like, no, it’s problem solving. And like you were saying, solution — it solves problems. It knows problems before you know you have them.

Keith: Yeah, it’s interesting, because it’s not that… people are saying, oh, search is going away. No — I think search is getting more powerful. It’s happening more because we have more targeted… What’s going away is the old-school SEO, where you can game the system in that way. You can still game the system through AEO and stuff like that. But the old idea of, I’m gonna have these blog articles that rank high, and then people are gonna come and do all this stuff — that’s going away.

But the idea that we’re searching for the solution… And it’s interesting that you phrase it this way, because when we get into our head about what our customers are doing and we try to think about it that way, we put ourselves into this fake way of searching, and [think] people are asking these questions on AI, and then they’re going straight to our site, and then they’re gonna buy. And that’s not happening. The first part’s happening. Like, I just did some research for some hosting. It’s super geeky, dumb, nothing to do with work. But I tried to find something, right? And the first thing I did is go into AI and say, okay, what are my options, right? What do I got there? I said, B, B, B, B, B, B. Okay. So then I go to YouTube, and I’m like, okay, let’s look at some of the options that were put up there. And then YouTube’s suggesting other things. I’m looking at those, and then I narrow it down to like two or three, right?

Then I go to those websites, and I’m looking at those websites and going through the feature list. At no point did I go to any of their SEO-marketed pages. But I was going through their feature list. It’s like, here’s what I need to do to be able to do this. It wasn’t, I landed on the page and signed up. I’m doing my deep research. I’m a qualified lead at that point. [I’m doing] my deep research to see if this is the product I want and it overcomes my problems and objections.

Ian Garlic: Exactly. It’s: how does this work, right? And FAQs. And also, do I like the company? You know, the About Us page is still important. People are going to it. It’s like, well, where is this company? Who are these people? Is it gonna be around in a few years?

Keith: Yeah.

Ian Garlic: Yeah.

Keith: Yeah, and what builds that trust more, I think, than a website is video. It’s the actual customer talking, and… yeah.

Ian Garlic: Well, the reason I say the number one metric is time is twofold. First of all, it’s the mere exposure effect, which I also call the Oprah effect. It’s like, the more we see someone’s face, hear their voice, the more we trust them. It’s ingrained in humans. And it’s weird, ‘cause it affects me, and I know it.

Like, I watch my clients’ videos, and I’m like, I feel like I see them all the time. And then I see them, and I’m all friendly, and I’m like, oh yeah, they don’t see me.

Keith: They don’t know who I am yet.

Ian Garlic: Yeah. And, you know, this is why I hear from all of our clients, they’re like, people just come in. If they watch our YouTube video, they just come in like I’m their best friend, and [an] authority. And so that’s a big one. But also — this is interesting, ‘cause I love Malcolm Gladwell — a long time ago, he wrote about the biggest differentiator between doctors that get malpractice lawsuits and doctors that don’t.

Do you know what it is? I gave you a hint.

Keith: [Time] to the client, to the patient.

Ian Garlic: Time with patient. So it’s time with patient. It’s not quality of care, it’s not their education. It is time with patient. Now, here’s the interesting thing. I looked up the study, ‘cause I like to confirm my data. And guess what the time difference was?

Keith: I’m gonna say five to 10x.

Ian Garlic: Two minutes.

Keith: What?

Ian Garlic: 13.2 minutes to 15. The doctors that don’t get sued spent 15 minutes on average with their patients. The doctors that do get sued, 13 minutes on average.

Keith: That’s crazy.

Ian Garlic: It’s crazy. So it’s this powerful effect, and that’s why we want to optimize for it. And like you said, I think in the world of AI… I think social media is gonna be kind of dying, because it’s like bots talking to bots. And—

Keith: I think… I really do. I felt this with Amazon, and I think it’s going to be exacerbated by AI video and AI comments and all these bots and stuff. But I really feel like Amazon’s just filled with junk right now. It’s just complete junk. We bought a flashlight, and my wife sends me this thing, and it was this cheap Chinese thing that was being sold for a lot of money, right? I’m like, this is obviously a Chinese knockoff. Let’s go look. And so what I did is I went to Wirecutter; I went to all these places. I went to places that I trust to give me that information. And so I believe that curation is the new trust factor, right?

Ian Garlic: Yes.

Keith: [We still] need social media and stuff, but it’s not going to be the fire hose that social media was. It’s going to be people building trust communities within that. And it’s always going to be — and I hate to use the words “parasocial relationship,” ‘cause it makes it sound so horrible — but it’s people trusting people, watching someone, like you’re saying, over and over again, building that trust.

And that’s where social media and business growth and stuff, I think, will stay. But the general fire hose of, “Hey, here’s a random bot on social media,” is just gonna [crosstalk] it all. Yeah.

Ian Garlic: Yeah. No, I agree completely. I mean, we still have to have the online communities. I think those are gonna massively grow. But your usual feed stuff…

Keith: Yeah.

Ian Garlic: I mean, I know I’m on LinkedIn less and less and less, because it’s like, oh my God, this is just all AI stuff I’m seeing.

Keith: It’s bad. I think LinkedIn and Twitter comments are the two places where thoughts go to die, really. Although I just signed up for Bluesky. I’m a little bit [late to] the party, but it’s just as bad. And it’s like you really have to curate. I mean, going back to the curation thing, you really have to curate who you’re listening to and build off of trust signals there.

‘Cause I don’t know if people have ever logged into YouTube without… or gone to YouTube in incognito mode, when you don’t have anything. Have you seen the videos that are on there? It’s horrible.

Ian Garlic: It is horrible.

Keith: It’s… yeah.

Ian Garlic: But that’s the problem with YouTube, though, ‘cause YouTube is optimizing, obviously, for total views. But we were talking about before — so many people are going down that path for their business, when really they only want a thousand people. And you’re optimizing for all the people you don’t want.

Keith: Right.

Ian Garlic: And you end up making MrBeast-style content for your law firm.

Keith: Yeah. And I mean, it’s like traffic back in the day, right? You always wanted more traffic because that’s gonna turn into leads, and that’s just not how it works. And like you’re saying, same with video. You know, I watch a lot of what I’d call just end-user content, right? Podcasts, entertainment content on YouTube, right? They have like a million views, 3 million views, and they were released a month ago, right? And I was looking at marketing videos. So what were the top-ranked marketing videos on YouTube?

Other than this one obviously bot-farm thing, Russell Brunson has the number one marketing video on YouTube. [It’s from] eight years ago, and it’s half a million views.

Ian Garlic: Wow. Yeah.

Keith: That’s it. But those are the right half million people. And if your video’s getting a thousand views and those are the right thousand people, that’s much more valuable than spreading out to everyone on the internet.

Ian Garlic: Well, yeah. And it’s actually… I’ve had a few people on that are — I hate to use this word — but, like, targeting newbies. Like, how to make your first thousand dollars online. And that skews my channel, ‘cause it gets the most views. And so, like, the lowest common denominator. And you look at Alex Hormozi — he’s super smart, but most of his stuff is targeting the person like, “If I wanted to make $10,000 this year, this is what I would do.” And I’m like, man, if I only made $10,000, my wife would kill me.

Keith: Yeah, exactly. Exactly.

Ian Garlic: So yeah, it’s really interesting. And so many people are like, well, I want the likes and comments. But also, the bigger the client you have, the more they’re just gonna show up, and they’re not gonna comment.

I mean, I’ve had people watching my YouTube videos for five years all of a sudden buy something, and they’re like, “I’ve been watching for five years.” I’m like—

Keith: I don’t see a single comment or a…

Ian Garlic: No.

Keith: …or…

Ian Garlic: No. Yeah, it’s really interesting. But I mean, yeah, I think it’s so important that people look at the whole part of what they’re doing, and they can’t just be funnel-focused anymore.

Keith: But how… so I wanna dig into that a little bit. When someone is starting out — so let’s say I don’t have any video presence, I don’t have any of the user videos, the customer videos, anything — how do you then start in that? Or maybe I do have videos, but they’re not doing well, right? How would I go about starting that? How do I start this new channel of video, other than what I think a lot of people feel, which is, I’m doing all this video and just posting it into the void?

Ian Garlic: Yes. So I’m assuming that you’re… are you talking about someone that has an existing business?

Keith: Yeah. Existing business trying to go with a new channel.

Ian Garlic: Gotcha. Yeah. I mean, the number one thing — and this is the number one problem I see with existing businesses and YouTube channels — is: show how you work and show your results. If you do those two things… that’s why case stories work. And you don’t have to just collect your clients — you should listen to your clients, you should interview them — but show how you work and what it means to work with you.

That’s the number one thing. ‘Cause I go to so many channels, and they’re like, “Analyze my channel.” I’m like, I can’t tell exactly what you do on your YouTube channel. You have all these videos. They’re like, “Well, this video has 20,000 views.” I’m like, yeah, but it doesn’t say how I can work with you.

Keith: Right.

Ian Garlic: I know that seems so obvious, but so few people do that. They’re afraid to be… and I’m not saying make a commercial. I’m saying, this is what I do. You know, like, “Hey, when you sign up for SegMetrics, your ultimate result is to double your results from your ads. And this is exactly how we do it,” right? “And I’m gonna show you: when you sign up, this is what we do. And when you talk to us, this is what you do. And when you join our email list, this is what’s gonna happen.”

Keith: Yeah.

Ian Garlic: And then all the questions that they have around buying it, ‘cause they’re buying questions — yeah, get all those filled out and finished.

Keith: Do you think that people get in their head too much when they’re doing this video? Or do you think it’s fear of… I think so. But what’s the biggest hurdle you think that people have?

Ian Garlic: So either they want to get too technical and talk about their technical piece — and I’m like, big clients don’t want to know exactly how the software works, right? They wanna know how it works for them. That’s the one side of it.

The other side of it is a lot of people go and start making commercials, and I’m like, no one wants to watch a commercial on YouTube. I know you see a lot of commercials on YouTube — they’re doing it wrong. Yeah, I mean, those are the two big ones. And just overthinking it, and not talking to the person’s problems, right?

And really understanding their problems and how you solve their problems. Because no one wants another software; no one wants another video. They want to be an authority. They want to land clients. They wanna make sure their ad spend isn’t being blown through the roof in the wrong places.

Keith: Yeah.

Ian Garlic: You know, we have to always talk about those problems.

Keith: This is one thing that I realized — God, it’s only been about a year, a year and a half ago — that whenever we did a presentation or this type of content, we always tried to be very… right? We tried to bring something that no one had heard before, or something very exciting, very expert-level, and it never landed.

And we gave this talk at Craft + Commerce, which is Kit’s conference, and it was just, for us, baseline, right? It was how you look at a funnel, break it up, and then look at the metrics of each of those things that you just broke up, right? And SegMetrics. And it was the highest-ranked workshop at the event. We did it again at MicroConf — again, highest-ranked workshop at the event. And I’m like, this is stuff that I’ve been talking about for years. I consider this just basic. But it’s not, because we’re in this all day.

Ian Garlic: All day.

Keith: [Like] your clients, we’re in this all freaking day. We’re thinking high-level thoughts about this, but everyone’s not at that level. Everyone wants to know, okay, what are the things I need to do to get results? [Not] the most esoteric thing that I’ve seen today.

Ian Garlic: No, I mean, Tony Robbins is a great example of it, because if you listen to some of his stuff — the forward-facing stuff — he just repeats the same stuff over and over and over and over again. But people need to hear it again. Like you said, they’re not doing it all day.

They need to be reminded of it, and the simplest stuff… and keeping it simple works. Because I have the tendency to do it too, ‘cause I’m deep in this. I wanna talk about YouTube SEO and AEO and trust signals and blah, blah, blah. And if someone gets me going on that, I can see them go, “Oh my God, I didn’t mean to ask that.”

But it’s so true. If you do it properly, where it’s accessible to them and they feel transformed, you can win. And I think, you know, taking that presentation now and breaking it up into two, three videos where it’s really easy is a great thing for you to do. And repeat it. Yeah.

Keith: Yeah. You mentioned Tony Robbins. I was talking to Ramit Sethi — same thing. He’s like, at some point you just have to resign yourself to the fact that you’re gonna be repeating yourself over and over and over again for the rest of your life. And you know what? That’s what being a teacher is all about.

I was a teacher for three years. I taught English in Japan. It’s the same thing. I had the same lesson every single day for three years. And it’s like, yeah, I have my lesson plan. I go to six different schools. I’m teaching that same lesson at six different schools. Rinse and repeat. Go again. Start the new year. Do the same thing over again. That’s just your life. And you’re going to do that or you’re not, but that’s what you need to do.

Ian Garlic: Yeah. No, exactly. Exactly. And that’s why I think versions of that stuff on your YouTube channel, versions of that stuff in your YouTube videos, where you’re repeating the idea over and over and over and over again to people… and then when they get indoctrinated and they’re in your community, now you can go deep, and they love that. But going deep to… yeah, I agree 100%.

Keith: Yeah, I was talking to Aaron Francis. He’s big in the PHP community. I don’t think anyone listening to this podcast [knows] who he is, but he was talking about essentially a four-to-one or five-to-one strategy, where he does four or five general posts — kind of top-of-funnel, “Hey, I’m saying the same thing,” or “Here’s the news,” general-interest stuff — and then for every four or five of those, he does one super in-depth, geeky, “Hey, here’s a deep dive,” to get people who are ready to go to the next level more into his ecosystem. So there’s [a lot] of stuff on top that’s essentially, I’m saying the same thing, I’m doing the blah, blah, blah. And then there’s that piece of content that’s like, okay, now you’re ready to come in deep. And essentially [it’s] warming a qualified lead, right? Same thing we’ve always been doing; just the format’s different.

Ian Garlic: Yep. Yep. Exactly. And that’s another thing to think about too. A lot of [it is] the fundamentals. Like, I tell people our whole Core Four YouTube video strategy is a deconstructed webinar, and it’s a lot of the same stuff over and over again. But you get better at it, right?

You get better at saying it. A lot of our clients — you know, we’ve made tons of videos, and they just get better and better at saying it.

Keith: Yeah.

Ian Garlic: What’s really interesting right now is the repetition is actually affecting AI, and AI search. ‘Cause the more [it] sees this stuff in different places, the more AI goes, “Oh, you’re the expert in this.”

Like, I was just looking up what AI thought about me in a certain vertical. And it’s like, “Well, you talk about many other verticals. These people talk about this vertical only, in places.” So that repetition is actually important now to AI search.

Keith: Oh, very much so. It’s interesting, because AI search is essentially SEO 10 years ago, I feel, where it loves listicles, it loves repeated content, it loves all this stuff that we used to do that Google now punishes us for, ‘cause [of] the algorithm. But the AI doesn’t really have an algorithm, right? In the way that we think about it.

I was looking at a new partnership platform, and I was like, “Hey, recommend some stuff.” And it recommended this. It was like, “You really need to use this. This is the best thing out there.” And I go to look at their webpage, and they have their customer’s name spelled wrong. They have typos every[where]. I’m like, what is this? And I pushed back to AI. I was like, “Why did you recommend this site? It looks horrible.” And it’s like, “Oh, they’re just really good at AEO.” It said they’ve been posting all this stuff that AI really likes, “and so I put it at the top of my list.” Yeah—

Ian Garlic: Wow.

Keith: [They were] constantly putting out content that was saying the same thing, that was focusing on that, and just bringing… yeah. And then AI was like, “Oh, they’re an expert in their field,” ‘cause no [crosstalk] this. Like—

Ian Garlic: Oh my God. Yeah, it’s—

Keith: They had all this video, they had all this text, they had all this stuff, they had all these testimonials. Yeah, it was crazy.

Ian Garlic: That’s interesting. Yeah, the case studies — the volume of case studies is a big deal there. So yeah, it’s super interesting how it’s going.

Keith: Yeah, and YouTube is one of the big sources for AI knowledge right now. You’re looking at things like, okay, what are the… because also Google is auto-transcribing all of your videos, and so that all gets fed into the AI. So you have a case study with someone big, or you have multiple case studies all saying similar things.

They’re going to be pushing into the AI, and now that’s huge. Because it’s a much larger trust signal for AI than it is just to have something on the website, because it’s video, and video is always ranked higher. [It] is ranked higher. These things are ranked higher than just standard website stuff.

Ian Garlic: Yeah. Yeah. I’m interested to see what goes on with Reddit, because it feels like it’s so hackable.

Keith: It is so hackable. Oh my God. Yes, I’m in a bunch of marketing subreddits, and I think that every single post and 90% of the comments are AI at this point.

Ian Garlic: Yeah.

Keith: ‘Cause marketers are gonna try to game the marketing system, right? So those are the subreddits that they’re focusing on, and it’s so… yeah.

Ian Garlic: Marketers ruin everything.

Keith: Yeah. We really do. I mean, it’s our lot in life. If there’s money to be had, we’ll make it. Yeah, I wanna talk about something you had mentioned on kind of the intake, and what we were talking about with this kind of YouTube and the fish-in-the-barrel strategy. What is that? How does that work?

Ian Garlic: Well, I mean, I’ve been doing it almost since the dawn of time — a version of it. And it was interesting: I talked to a client, and not only his clients… I’m like, I feel like I see him all the time, because I see his videos pop up, and I talk to him like every 18 months.

You know, we made this original fish in the barrel. So we do the Core Four videos. You get them ranking and put ‘em in different places. It’s been eight years, and he’s like, it all just works. It’s exactly what you said. And so basically it’s owning the time from someone when someone discovers who you are and they’re thinking about you and working with you. All the places that they go, having videos there that are converting videos, like I talked about: how to work with you, the Trusted Path video, your Why Us — I call it the Why Us and not the About Us, ‘cause if I tell people “About Us,” they just blab their story, and I’m like, no. It’s: why are you different?

Keith: Right.

Ian Garlic: Your converting questions: what are the questions that someone asks when they’re buying from you? And your case stories, your customer stories, and having those surrounding people. Those videos surrounding… ads, they’re going down, because we talked about time with website.

Time watching the videos… you know, when you got close to that hosting thing, you started watching longer content, right? You’ll nibble, nibble, nibble, and as you get closer to the buy, you’re gonna watch 10, 15, 20 minutes. This is your opportunity to really optimize for this. And so many people are trying to get the lead and not optimizing for what they call the fish in the barrel. There’s the people there that are swimming around your business, and the bigger clients… like, we’ve had other agencies — we’ve had people landing $100,000 clients because they optimized for that.

So, like — let me… can I share my screen?

So you see here, “SegMetrics reviews.” Second thing, right? Is owning this, right? And this is a great place to have videos ranking — YouTube videos ranking — ‘cause you get ‘em over to your channel. This is someone else’s video. You should have yours ranking, right? Say, like, “Is it legit?”

Keith: Is it…

Ian Garlic: This is a big one. I didn’t know.

Keith: Yeah.

Ian Garlic: “Legit” is a big term that people put in. And obviously the AI searches — this is where you wanna be influencing that. You know, I’d probably try and push this Trustpilot thing down. But [it’s] another place to own videos, right? And then, you know, your name too.

‘Cause you’re going around and you’re speaking, so people… you’re gonna be, like, having case stories, having your About Us page, how to work with you here now. And then going to SegMetrics, right? This is another thing I’m doing, is optimizing for… actually, it doesn’t share all the…

There we go. Lemme see if that changes. Optimizing for what you can’t see, but tabs. Another thing I’m doing is, ‘cause people have 20 tabs open, I wanna optimize for all the searches. And so what I’m doing is putting more YouTube videos, embedding them, and having them open up instead of having them play on the website.

But, you know, definitely I would have here an About Us page. I like the use cases, but we did a study with Leadpages a long time ago, and Resources was actually not clicked. And so… but having a Process tab—

Keith: Yeah.

Ian Garlic: —having a Process tab: how does it work? How do I get set up on this? About — like, your story? ‘Cause, I mean, you have a ton of personality, and people wanna buy from a person.

Keith: [Funny] that you mentioned that, ‘cause we used to have a Why SegMetrics front and center, and we kind of buried it in the use cases section right now.

Ian Garlic: Yeah. I would look at your heat map, because I learned all this from heat [maps]. You know, at one point we had like 300 websites that we hosted, and we had heat maps on all of them. The About Us was the second most used page. Great place to put a YouTube video, ‘cause now they’re gonna spend some time with you…

Keith: Right.

Ian Garlic: …[on that] page.

All those searches that I put on your LinkedIn — you should have a YouTube video, because you can open up the LinkedIn, [the] YouTube video will open up. Again, you’re owning more searches, right? Because they’re gonna look this up. I would have your LinkedIn ranking for this too. But that’s the fish in the barrel.

There’s like 20 different places. When you go through that calculator — go to videocasestory.com/keith — it’ll show you all the different places. And if your average client value is $10,000, and you get one extra client a year from each of these places — like 20 places over the course of four years — that’s $800,000 return on investment.

And so that’s that fish-in-the-barrel strategy. It seems really obvious, but people don’t take it seriously.

Keith: I think people don’t take it seriously because they don’t see immediate results, right? And that’s what I have seen across people that we’ve worked with in the past as well, where they’re like, “Oh, I did this thing for a month and I didn’t get a million dollars. I’m just gonna cut it,” right? And it’s like you’re saying — it’s wider. It’s about stacking those bricks, and like, okay, people are gonna find one of these things at some point, and the more you have, the more chances… it’s increasing your luck surface.

Ian Garlic: Yes. I love that. Yeah.

Keith: Yeah. There was a phrase you used that I really want to pull back on. Oh, what was the name of the fricking phrase? I’m gonna have to go back to the recording once this is live. It was such a good… oh, that’s what it was: deconstructing a webinar. I love that idea. Because I think all of us, as marketers, with all this stuff that we’re doing, we understand the webinar…

Ian Garlic: Mm-hmm.

Keith: …the webinar format. And the idea to deconstruct — it’s like, okay, we’re just gonna chop this thing up, and this is our social content strategy — works so well. I think that really rings true with a lot of people.

Ian Garlic: Yeah. Well, because the bigger the client, the less that sale’s gonna happen during the workday, right? If you’re a coach or a consultant targeting the C-suite, they’re not watching your YouTube videos at noon on a workday, because they’re working.

Keith: Right.

Ian Garlic: But at eight o’clock on Sundays, they’ll poke around, right?

And at night, poke around and watch a little bit, a little bit, a little bit. And this is where you want to have repeated the various versions, the various things that you’re talking about. So when they poke around, eventually they get [it]. And then you just drive ‘em down, so you’re connecting the videos. So they do see the whole webinar, but it’s asynchronous.

Keith: Yeah. Do you find that… I know this is getting into the nitty-gritty—

Ian Garlic: I love it.

Keith: —‘cause [I do a] lot of video now. I love video. I love talking into the camera. One of the things I hate doing, though, is, oh, I gotta show the slides now, and I gotta do the demo video and all this stuff. Do you find that people respond better when there are visuals in addition to the talking head? Or do you see that even just a plain talking head is valuable?

Ian Garlic: We try and add B-roll to everything. I mean, you still need to entertain them a little bit, ‘cause people are looking off left and right, and you want to grab their attention. So I do find visuals [help]. I’m going deeper into it. It is interesting, though, because…

I’ll give you the link to this, but I was doing slides for all these FAQs, and I’m like, “Hey, here’s 20 FAQs. Make me slides for it.” And I built this slide generator, and it started to answer the slides, and it gave all incorrect answers. But they seemed right.

Keith: Right.

Ian Garlic: Right. And I’m like, “Where did you get this from? Did you use my content?” Like, “I used some of your content, and then I just kind of made it up too.” So you gotta be careful with the visuals if you’re not an expert. But I do think… yeah, it’s so easy now. There’s so much you can do with Claude and stuff to make really good visuals.

I think you should take the time to do it. I do think, though, if it’s too AI, it is affecting LinkedIn and the search. I think it’s doing some scans. I know it does scans for the AI watermarks when you upload it.

Keith: Interesting.

Ian Garlic: Yeah, so I’d be careful with AI-generated stuff. Especially… I think YouTube’s gonna start segmenting it out too.

Keith: I think they’ve already started, for fully AI [videos].

Ian Garlic: Yep.

Keith: I think they watermark that, and they’re like, nope, push that stuff down. That’s interesting, though. I think that’s part of the backlash we’re going to see, because… it was interesting. So, you know, Claude released Claude Design, and people [are] building websites out of it. They all look exactly the same.

Ian Garlic: All exactly the same.

Keith: [Every] effing thing. You can instantly tell a Claude [site]. It’s like when Bootstrap came out, and then Tailwind. All the sites look exactly the same for about, I don’t know, maybe a year or two years or something, until people figure out how to make them not look the same.

But I’m looking, and anything that’s, especially, oranges, or that weird marker-underline thing, I’m like, yeah, that’s just AI. [You made] this in 10 [minutes].

Ian Garlic: And the same fonts. And I’ve tried using it. I’m like, “Don’t use those AI fonts.” And it sneaks ‘em in. And I’m like, what are you doing?

Keith: I actually thought it was better before they had Claude Design — and that’s getting into a huge rabbit hole. But, ‘cause I use Claude Code every day. We use it for marketing, we use it for everything, to help improve what we’re doing and to get better ideas and to bring all this stuff out. But when you just toss something in and it’s like, make it… it [comes] out like shit.

Ian Garlic: No, no. Yeah. I mean, I think everyone needs a… I had a client — hopefully he doesn’t listen to this — but he’s like, “I had AI make me a marketing plan.” And then I looked at him… and it basically was a sycophantic, like, “I know you like these words, so I’m gonna put these words up here.”

Keith: Yep.

Ian Garlic: Yeah, it’s bad.

Keith: No, I’ve been curious. I tried the make-a-marketing-plan thing. It’s middling advice, right? It’s the standard, like, post on social, write blog posts. It’s like, yeah, I know, I know. The other thing I tried was with data. And this is my… right now. I’ve never trusted Claude with data, but I decided to try it. I uploaded 800 of our most recent trials, and I was like, “I wanna do an onboarding analysis. How long are people taking to connect their stuff? How long are they taking to get to our major touch points?” I just want to see time differences and all this stuff, and find some insights.

And it made some really pretty graphs. Looks awesome. It’s clickable. It’s so cool. It’s the most awesome thing. And I looked at one of the numbers, like, huh, that’s a weird number, 43. That does not sound like a number that should exist there. “Hey Claude, what’s this number?” And it’s like, “Oh, you uploaded 800 records. I just used the first five.”

Ian Garlic: Oh my God.

Keith: I was like, “You did what? You did what?” So… it never told us. It never looked at that. And it gave us a nice total. It said, “Yeah, 800 records, that’s how many you uploaded. But I only looked at five for the actual analysis.”

Ian Garlic: I know. I always have to be like, “Did you use all the skills?” “No, I kind of guessed based on the skills.” I’m like, oh my God. Yeah, you have to… there’s so much—

Keith: Did it get lazy halfway through and start browsing Reddit? Like, what’s going on here, man?

Ian Garlic: It’s kind of like a semi-lazy, super-smart employee. Like, you’re really smart when you do the stuff, but—

Keith: You are the laziest person I know.

Ian Garlic: “I’m gonna lie to you authoritatively.”

Keith: Exactly. Exactly. I mean, they’re just so good at it. [It] just wants to please us, right?

Ian Garlic: Yeah. Yeah.

Keith: So, man, what do you feel is next for video, and how we present ourselves, and building that relationship? Do you think it’s different than it was six months ago, with how AI and search and everything has changed? Or do you feel like it’s still the same thing, and it’s always gonna be that same bread-and-butter kind of stuff?

Ian Garlic: The big difference I see is — while we said, you know, you shouldn’t worry about the metrics — videos rank more with more views. And I think running ads to the videos to get views is affecting AI. I think more so than ever, you have to treat YouTube like your website. And it’s an argument I have with a lot of my clients.

Like, “I don’t wanna send people to YouTube, ‘cause there’s other people on there, and they’ll go other places.” I’m like, there’s not one single person watching this right now that has only one tab open. I mean, Keith, I don’t know — like, my mom doesn’t watch my podcast.

I dunno if you’re like… but there’s not one single person watching like that. So I think sending more people to YouTube as your website is probably one of the most important things you can do. But besides that, no, I think, yeah, avoiding AI. Don’t let AI write the scripts for you.

Keith: Right. It’s interesting, because I feel I’ve heard that a lot too. It’s like, “Oh, but my competitor’s on YouTube.” It’s like, are they bigger than you? Because if they’re bigger than you, then you’re taking their audience; you’re building on the work they did for your audience. Are you bigger than them? Then it doesn’t matter, right?

For me, you know, we were talking about people trusting people and that community, and one of the benefits, I think, around that is when you watch a bunch of, let’s say, online course videos, right? And people…

Ian Garlic: Mm-hmm.

Keith: …that. And you just watched this one person, and now this new person — your client — starts popping up, and you’re like, “Oh, that’s interesting too.”

And that would not have been there [if] they weren’t already watching this. So you’re kind of getting… and maybe I’m completely off track on this, but I feel like with the YouTube recommendation engine, you get into other people’s audiences to a degree, because when you finish one video, they’re recommending other ones that are like it.

Ian Garlic: 100%. I mean, when you send traffic to YouTube, YouTube knows who’s going to that video. And if you’re making… I think, you know, the quality of the videos — and by quality I mean content — ‘cause everyone’s like, “How do you rank a YouTube video?” ‘cause we have them ranking. But you need to understand… it’s so much more intention.

Keith: Mm-hmm.

Ian Garlic: [Who’s] watching it, and when [they’re] watching it, and how you’re driving traffic there, than ever before. Because the opportunity’s there. And I think that’s the thing: it’s amplified, not changed.

Keith: Yeah.

Ian Garlic: Because, yeah, like you said, if you’re sending people there and they watch two minutes of your video, three minutes of your video, which is half of it, they’re gonna start recommending that video to other people that are in the same situation, that have the same problems, ‘cause they want more people to watch that video.

Keith: Right. I mean, at the end of the day, that’s all YouTube wants: for people to watch more video and to never leave the site. So the more that they can promote content… And it’s interesting, because even Google search — there was a study done by SparkToro that, I believe, it’s 54%, or maybe it’s 70%.

It’s a huge number. A huge number of Google searches never leave a Google property, meaning that they’re going into Gemini, they’re going into YouTube, they’re going into all these places that Google still owns, as part of that search. Interesting. Google’s whole thing is they want to keep you — and all these social platforms, they want to keep you right there, watching as much content as humanly possible. And if that means recommending someone else who you want to get in there, then it’s a great play.

Ian Garlic: Yeah, exactly. Exactly.

Keith: So I wanna touch on something, ‘cause you had talked about doing ads to the YouTube videos. Do you find that ads or boosted videos are better? Because I know a lot of people are just like, every video I put up, I put $100, $200 just to boost it.

Ian Garlic: No.

Keith: Do you… or how do you—

Ian Garlic: I do high-intent keyword ads.

Keith: Okay.

Ian Garlic: I mean, it’s the second most used search engine, and it has Google’s data. Why not send people looking for that problem to your video?

Keith: Yeah. Yeah. Hmm.

Ian Garlic: And it’s funny, because we have clients in sensitive areas, and one of my clients — his neighbor is like, “Oh, I saw your YouTube video on my TV.” And he’s like, “Oh…” He’s like, “You did something bad.”

Keith: [You must] have been searching for that.

Ian Garlic: Yeah, exactly. He’s like, “You searched for something, and you’re in trouble.”

Keith: Yeah. Yeah.

Ian Garlic: But yeah, I think, ‘cause you don’t want just viewers — you want some intent, right?

Keith: Yeah.

Ian Garlic: And we have one running right now for an agency, with high, high, high-intent keywords, and they’re at .003 cents a view with 80% view time, ‘cause the intent’s there. And this is what’s really good about it: because the intent’s there and you get the view time, Google and YouTube will actually start ranking the videos for you.

Keith: Mm, mm, mm.

Ian Garlic: So yeah, it works.

Keith: That’s a brave new world. And it’s interesting, because, you know, traditional ads are really having a hard time. Cost is going up. Retargeting’s more expensive than cold ads at this point, which is insanity.

Ian Garlic: That’s crazy.

Keith: Yeah. I know some people are doing really well on it, but a lot of people are just like… especially in the B2B space, anything that’s going after any sort of prosumer or business level, the ad price is just insane.

It’s just so much money at this point. It’s like, okay, we’re looking to get ROI in six months, not a week like it used to be, right? Or God forbid you try to have a self-liquidating offer at this point, right? So it’s fascinating.

Ian Garlic: I think a lot of those people relied too much on it automatically doing it for them, and so they didn’t spend enough time really understanding the content that they made. You know, I’m in a couple of mastermind groups with amazing copywriters from back in the day that did, you know, millions of dollars of direct mail.

And they had to spend the time nailing it down, because you didn’t have a second chance with it. And now, with Facebook ads, it’s like, “Oh, I should put up more garbage.”

Keith: This is the thing that gets to me. I’ve gone to a couple of marketing conferences recently where I feel like they’ve lost the lead a little bit, because they keep saying, “Oh, we’re just going to have all this AI stuff out there. AI is doing all my ads.” And so everything’s generic, right?

Ian Garlic: Yep.

Keith: [They’re] like, “Well, we’re just pushing it to more people.” I’m like, there’s a lot of people in the world, right? [But] especially when you’re a high-level coach, or you’re a high-level creator, or you’re trying to sell something that’s not 10 bucks, there is a limit to the number of people. Some of our ads get a hundred thousand impressions, half a million impressions, a million impressions. That’s a lot of people.

Ian Garlic: Yes.

Keith: [If] we keep [going], what is the damage to our brand to push out schlock? We’re just thinking that there’s always new people that are going to convert.

Ian Garlic: Yeah. Well, and if you think about it, AI — like we’re talking about the websites, right? It doesn’t make new stuff up. It makes up stuff, but it’s not original. And so it is just bringing in what it sees out there and what might be working. So we have it in this death spiral of everything looking the same.

And I think that’s the opportunity: to not be doing it, and do something completely different, and what works, like you said, and make it not look AI. ‘Cause here’s the thing: just because the ad didn’t work, like you said, doesn’t mean it didn’t do damage to your brand.

And when I see AI over and over and over again, I’m like, you’re getting lazy, and you’re insulting me.

Keith: Yeah, I feel the same way when I see — especially on LinkedIn posts, right? I’m scrolling LinkedIn and I see the same cookie-cutter posts and the same AI-generated images. Because it’s fascinating. I feel it’s kinda like the IKEA effect, right? The first time you see this come out, you’re like, “Holy crap. I just put in a prompt and this thing came back, and this is awesome-looking.” And then you don’t realize, as you’re scrolling LinkedIn, that it looks like every other post out there, and you’re like, “Wait a minute. What I thought looked so fricking cool is just this cookie-cutter, off-the-shelf thing.” And as soon as you realize that that’s AI, then your value of those posts and that content just drops like a rock.

Ian Garlic: Yep. Yep. Yeah. And you feel insulted.

Keith: Yeah. You really do. You really do. You’re like, come on, you didn’t spend the time to come up with an original idea here. You’re just pushing stuff into the void.

Ian Garlic: Exactly.

Keith: Ian, awesome, man. I always love talking to you. It’s so great.

Ian Garlic: It is fun.

Keith: You have so much good stuff to talk about. Where can people find you? What should they come to you for?

Ian Garlic: videocasestory.com. Like I said, videocasestory.com/keith. I’ll have the fish in the barrel… analyze it. I mean, it’s probably the most… if you have a high-ticket, high-value service, it is probably the most important thing you can do, and no one does it.

You can go there, and then also follow me on YouTube. You know, look up Ian Garlic — I’m the only one out there. And if you wanna connect with me on LinkedIn, tell me you saw me here, and whatever I can do to help, let me know.

Keith: Awesome, man. Thank you so much for joining us.

Ian Garlic: Thank you.

Keith: As always, and see you then.

Multi-Touch Attribution: How It Works (With Real Examples)

Denise Cullom — Thu, 15 Oct 2026 09:00:00 GMT

Multi-touch attribution is a way to measure marketing that gives credit for a sale to every touchpoint a buyer interacted with, not just the first or last one. If someone saw your Facebook ad, watched a YouTube video, opened three emails, and then bought, multi-touch attribution shows how much each of those steps contributed to the revenue.

That is the short answer. The rest of this guide shows how it works, which model to use, and what it looks like on a real sale.

Last-Touch vs. Multi-Touch Attribution

Most marketers start with single-touch attribution. It gives 100% of the credit to one touchpoint.

First-touch attribution credits the channel that brought the lead in.
Last-touch attribution credits the channel that happened right before the purchase.

Both are simple. Both lie.

Think about how people buy a $997 course or a coaching program. Nobody clicks one ad and pays. They opt in, watch a webinar, read emails for two weeks, look you up on Google, and then buy. Last-touch gives all the credit to that Google search. Your Facebook ads look worthless. You cut them. Leads dry up a month later and nobody knows why. Last-touch attribution rarely holds up for this kind of buying path.

First-touch has the opposite problem. It ignores the emails and content that actually closed the sale.

Ad platforms make this worse. Facebook, Google, and YouTube each count the sale as their own. Add up what every platform reports and you often get more sales than you actually made. (Here is why Facebook’s numbers are mostly guesswork.)

Multi-touch attribution fixes this by looking at the whole path.

How Does Multi-Touch Attribution Work?

Multi-touch attribution works in three steps.

Track every touchpoint per person. Each time a lead clicks an ad, opts in, opens an email, or visits a page, the system records it against that person. UTM parameters, tracking pixels, and your email and CRM data all feed this record.
Connect the touchpoints to revenue. When the person buys, the purchase from your cart or payment processor (Stripe, ClickFunnels, and so on) is matched to the same person. Now you have a full path from first click to final payment.
Apply a model to split the credit. The attribution model decides how much of the sale each touchpoint gets. Different models answer different questions, which is why most teams look at more than one.

The hard part is step one. If tracking breaks at any point, such as a lead switching devices or an ad platform losing the click ID, the path has a hole in it. The model can only split credit across the touchpoints it can see.

The 5 Most Common Multi-Touch Attribution Models

Here is what each model does and the question it answers.

Model	How it splits credit	Best question it answers
Linear	Equal credit to every touch	Which channels show up in buying journeys at all?
Time decay	More credit to touches closer to the sale	What is closing deals right now?
U-shaped (position-based)	40% first, 40% last, 20% split across the middle	What creates leads, and what converts them?
W-shaped	30% first, 30% lead creation, 30% last, 10% the rest	What drives each funnel stage?
Data-driven	Credit based on statistical patterns across many paths	What actually changes the odds of a sale?

Data-driven models need a lot of conversions to work. Most course creators, coaches, and small SaaS teams get better answers from the first four.

For a deeper look at single-touch and multi-touch options side by side, see the 6 marketing attribution models explained.

Multi-Touch Attribution Example: One $997 Course Sale, Five Answers

Here is one buyer’s path to a $997 course.

Day 1: Clicks a Facebook ad and registers for a webinar
Day 9: Watches a YouTube video from the same creator
Day 14: Clicks a link in a sales email
Day 16: Searches the creator’s name on Google and buys

Same sale. Watch how the credit moves.

Touchpoint	First touch	Last touch	Linear	Time decay*	U-shaped
Facebook ad	$997	$0	$249.25	$89	$398.80
YouTube video	$0	$0	$249.25	$196	$99.70
Sales email	$0	$0	$249.25	$321	$99.70
Google search	$0	$997	$249.25	$391	$398.80

*Time decay uses a 7-day half-life. A touch 7 days before the sale gets half the weight of a touch on the day of the sale.

Look at the Facebook ad. Under last-touch it earned $0. Under U-shaped it earned almost $400. If you judged Facebook by last-touch alone, you would kill the channel that started this whole sale.

Now look at the Google search. Under last-touch it gets all $997. But the buyer searched your name. They already knew you. That search was the door they walked through, not the reason they came.

This is the whole point of multi-touch attribution. No single model is “true.” Each one gives you a different lens. When a channel looks strong in every model, it is strong. When it only looks good in one, dig deeper.

Which Multi-Touch Attribution Model Should You Use?

Match the model to the decision you are making.

Deciding where to spend ad money for new leads? Look at first-touch and U-shaped. They show what fills the top of the funnel.
Fixing a launch or sales sequence? Look at time decay. It shows what pushes people over the line.
Reporting to a partner or boss? Use linear or U-shaped. They are easy to explain and hard to game.
Running long funnels with webinars or challenges? Use W-shaped if your tool supports it. It credits the opt-in, which is often the most important moment in an info-product funnel.

Then compare. Pull the same report under two or three models. The channels that hold up across all of them are your real winners.

Also check your attribution window, which is how far back the model looks for touchpoints. A 7-day window will miss that Day 1 Facebook ad entirely. For high-ticket offers with long nurture sequences, you usually need 30 to 90 days. Here is how attribution windows work.

What You Need to Set Up Multi-Touch Attribution

You need four things working together.

Consistent UTM parameters on every ad, email, and social link. Messy UTMs mean messy data. Start with UTM Essentials or build clean links with the free UTM builder.
A tracking script on your site, opt-in pages, and checkout so you can follow each person across visits.
Connections to your tools. Your ad platforms, email platform (ActiveCampaign, Kit, and others), and cart or payment processor all hold part of the path.
Identity matching that ties anonymous clicks to a real lead once they opt in. Without this, every touch before the opt-in disappears.

You can try to build this yourself in spreadsheets. Most teams give up once they hit identity matching across devices and tools. That is the gap attribution software like SegMetrics fills. It connects your ads, email, and cart, then follows each lead from first click to final purchase so you can see revenue by channel under any attribution model.

Can AI Do Multi-Touch Attribution for You?

Partly. AI tools like ChatGPT and Claude are good at reasoning about marketing data. Ask one why your cost per lead went up and it can give you smart theories.

But AI cannot see a click that was never recorded. It does not know that a buyer from Tuesday first found you through an ad three weeks ago. That memory has to come from tracking. (Here is why AI can’t replace attribution.)

The best setup pairs the two. Your attribution data holds the full path. AI reads it and answers questions in plain English. SegMetrics does this through its MCP server, which lets you ask Claude or other AI assistants questions about your real attribution data.

Want to see your own funnel under every model? Book a demo and we will walk through it with your data.

Frequently Asked Questions

What is multi-touch attribution in simple terms?

Multi-touch attribution gives credit for a sale to every marketing touchpoint a buyer interacted with, such as ads, emails, and videos. Instead of crediting only the first or last click, it splits the revenue across the full path so you can see what each channel actually contributed.

What is the difference between last-touch and multi-touch attribution?

Last-touch attribution gives 100% of the credit to the final touchpoint before a sale. Multi-touch attribution splits credit across every touchpoint in the path. Last-touch is simpler, but it hides the channels that start or nurture sales.

Which multi-touch attribution model is best?

No model is best for every question. Linear shows which channels appear in buying paths. Time decay shows what closes sales. U-shaped shows what creates and converts leads. Most marketers compare two or three models and trust channels that perform well across all of them.

Is multi-touch attribution worth it for small businesses?

Yes, if your buyers take more than one step before purchasing. Course creators, coaches, and agencies with webinars, email sequences, or launches almost always have multi-step paths. Without multi-touch attribution, they tend to cut the ads that start sales.

Why do my ad platforms report more sales than I actually made?

Each ad platform counts any sale it touched as its own conversion. If a buyer clicked a Facebook ad and a Google ad, both platforms claim the sale. Multi-touch attribution tracks the buyer once and splits the credit, so the totals match your real revenue.

The Best Marketing Attribution Tool for Coaches, Course Creators and Info-Product Businesses (2026)

Denise Cullom — Tue, 06 Oct 2026 09:00:00 GMT

Short answer: SegMetrics is the best marketing attribution tool for coaches, course creators and info-product businesses. It connects your ad platforms to the email tool, CRM and checkout you already use (Kajabi, Keap, ActiveCampaign, Kit, ThriveCart, SamCart, Stripe and 100+ more), so you can see which ad, lead magnet, webinar and email sequence produced each sale, including sales that happen weeks after the first click. Plans start at $57 a month with a 14-day free trial.

If you sell a Shopify store, look at Triple Whale or Northbeam. If you run a B2B sales team, look at HockeyStack or Dreamdata. If you sell courses, coaching, memberships or high-ticket programs through a funnel, keep reading.

Why do coaches and course creators need a different attribution tool?

Most “best attribution tool” lists sort tools into three groups: ecommerce, B2B, and lead generation. Info-product businesses fit none of them cleanly.

Your funnel usually looks like this:

Someone clicks an ad or finds your content.
They opt in for a lead magnet, challenge or webinar.
They get an email sequence for days or weeks.
They buy a low-ticket offer, a course, or book a sales call.
Some of them upgrade, renew, or buy again months later.

That shape breaks most attribution tools in three places:

The sale is far from the click. The purchase often happens 7 to 60 days after the opt-in. Ad platform reporting and short cookie windows lose most of that.
The lead lives in your email tool, not your store. Ecommerce tools are built around a Shopify order. Your customer record lives in Kajabi, Keap, ActiveCampaign, Kit or HighLevel, and the order happens in a separate cart.
The first purchase is not the real value. A $27 tripwire buyer who later joins a $2,000 program is your best customer. A tool that stops at the first order will tell you to cut the campaign that found them.

What should an attribution tool for info products track?

Use this as a checklist when you compare tools:

Need	Why it matters for info products
Native email and CRM connection	Ties every lead to a person, not a browser session
Separate checkout connection	Matches ThriveCart, SamCart, Stripe or Kajabi orders back to the lead
Long attribution windows	Credits the ad that found a lead who bought 45 days later
Lead-to-sale funnel reports	Shows opt-in rate, webinar show rate and close rate by source
Lifetime value by source	Finds which channels bring buyers who come back and upgrade
Webinar and call tracking	Credits webinars and sales calls, not just the last click
Cost per lead and ROAS from your own data	Checks Facebook and Google numbers against real revenue

How do the main attribution tools compare for course creators and coaches?

Tool	Built mainly for	Connects to email and CRM tools	Tracks lifetime value	Fit for info products
SegMetrics	Coaches, course creators, info-product and subscription businesses	Yes, natively (Kajabi, Keap, ActiveCampaign, Kit, HighLevel, Ontraport and more)	Yes, by source, campaign and segment	Best fit
Hyros	High-ticket and paid-ads-heavy offers	Partly, mostly through links and its own tracking	Yes	Good for paid ads, less detail on the email funnel; demo required before you can buy
Wicked Reports	Ecommerce and subscription brands	Some CRM connections	Yes	Workable, but built around ecommerce
Cometly	Paid media buyers	Limited	Partly	Strong on ad tracking, light on the email and nurture steps
Triple Whale	Shopify stores	No, Shopify-first	Yes, for Shopify orders	Poor fit unless you sell on Shopify
Northbeam	Large DTC ecommerce brands	No	Yes	Poor fit, priced for large ad budgets
GA4	Websites in general	No	No	Free, but loses people across devices and long delays

Compare tools head to head: SegMetrics vs Hyros, SegMetrics vs Wicked Reports, SegMetrics vs Cometly, SegMetrics vs Triple Whale.

Why is SegMetrics the best fit for info-product businesses?

SegMetrics was built for funnel businesses, so it starts from the contact, not the order.

It reads your email tool directly. Tags, lists, form fills and automations in ActiveCampaign, Keap, Kit, Kajabi and HighLevel become funnel steps you can report on.
It matches orders from any cart. ThriveCart, SamCart, Stripe, Teachable, Thinkific and others are tied back to the lead record.
It credits the whole journey. Choose first touch, last touch or multi-touch attribution models and see which ads and lead magnets produce buyers, not just leads.
It shows each person’s path. Contact journey reporting shows every touchpoint from first click to purchase for any lead.
It tracks what happens after the first sale. Lifetime value tracking shows which sources bring buyers who upgrade, renew and refer.
It keeps tracking when browsers block cookies. Server-side tracking and fingerprint tracking keep matching leads across devices.

See how it works for your business type: coaches, course creators, info-product sellers and webinar creators.

How much does marketing attribution cost for a course creator?

SegMetrics pricing is based on the number of contacts in your list:

Launch: from $57 a month
Grow: from $197 a month
Scale: from $397 a month
Enterprise: custom pricing

Every plan starts with a 14-day free trial. Annual billing takes two months off. See the pricing page for the price at your list size.

How do I set up attribution for my course or coaching funnel?

Connect your ad accounts (Facebook, Google, YouTube, TikTok) so spend and clicks come in.
Connect your email tool or CRM so every lead is a known person.
Connect your checkout so orders match back to leads.
Add the tracking snippet to your landing pages and checkout pages.
Build one funnel report: ad click to opt-in to webinar or call to purchase, broken down by source.
Wait one full sales cycle before you cut a campaign. If your average buyer takes 30 days, judge ads on 30-day revenue, not day-one ROAS.

Frequently asked questions

What is the best attribution tool for Kajabi?

SegMetrics connects directly to Kajabi and matches Kajabi contacts and offers back to the ads and emails that produced them. Kajabi’s own reports show sales but not which ad or lead magnet started the journey.

What is the best attribution tool for ActiveCampaign or Keap users?

SegMetrics. It reads ActiveCampaign and Keap tags, lists and automations directly, so you can report on each step of your nurture sequence, not just the ad click.

Can I track which Facebook ads lead to course sales weeks later?

Yes. SegMetrics stores the first click against the contact, so when that person buys 30 or 60 days later the sale is still credited to the ad that found them. Facebook Ads Manager usually stops counting after 7 days.

Is Hyros or SegMetrics better for coaches?

Hyros focuses on paid ad tracking for high-ticket offers. SegMetrics covers the ads plus the email, webinar and call steps in between, starts at $57 a month, and can be tried for 14 days without a sales call. Read the full SegMetrics vs Hyros comparison and the Hyros pricing breakdown.

Is GA4 enough for a course business?

GA4 is free and fine for site traffic. It does not connect to your email tool or cart, loses people who switch devices, and cannot show lifetime value by source, so most course creators outgrow it once they spend on ads.

Do I need attribution software if I only sell one course?

If you spend money on ads, yes. Without it you are trusting each ad platform to grade its own work. Even a single-offer business usually finds at least one campaign that looks profitable in Ads Manager and loses money in reality. Here is how to tell if your ads are actually profitable.

The bottom line

Pick the attribution tool that matches how you sell. Shopify stores should look at Triple Whale or Northbeam. B2B sales teams should look at HockeyStack or Dreamdata. Coaches, course creators and info-product businesses should use SegMetrics, because it is the tool built around email funnels, separate checkouts, long buying cycles and lifetime value.

Start your 14-day free trial or book a demo to see your own funnel in SegMetrics.

What Are the Best AI Marketing Tools? A 2026 Guide by Use Case

Denise Cullom — Fri, 02 Oct 2026 15:00:00 GMT

The best AI marketing tools in 2026 are Claude and ChatGPT for writing, Surfer SEO and Ahrefs for search, Perplexity for research, Descript and Predis.ai for video and social, AdCreative.ai and Albert.ai for paid ads, HubSpot and ActiveCampaign for automation, and SegMetrics for attribution. No single tool does it all well. The strongest teams pick one specialist per job and connect them to shared revenue data.

Why it matters: That last part matters more than most guides admit. AI tools for marketing can write, design, and optimize. They cannot tell you which of those efforts made money unless you give them the data.

Best AI Marketing Tools at a Glance

Marketing job	Best AI tool	Why it wins
Long-form content and copy	Claude	Natural, on-brand writing and strong reasoning
General-purpose drafting	ChatGPT	Broad features, custom GPTs, huge ecosystem
Brand-governed content at scale	Jasper	Brand voice controls built for teams
SEO content optimization	Surfer SEO	Real-time scoring against top-ranking pages
SEO and AI search visibility	Ahrefs	Backlinks, keywords, and AI mention tracking
Market research	Perplexity	Fast answers with cited sources
Social content	Predis.ai	Posts, graphics, and reels from one prompt
Video and podcast editing	Descript	Edit video by editing the transcript
Ad creative	AdCreative.ai	Generates and scores ad variations
Autonomous ad buying	Albert.ai	Manages bids and budgets across channels
CRM and marketing automation	HubSpot Marketing Hub	AI built into the CRM and email tools
Email automation for small teams	ActiveCampaign	Deep automation with AI assists
Attribution and revenue analytics	SegMetrics	Ties every lead and sale to its source, and feeds that data to your AI

How Should You Choose AI Marketing Tools?

Recommendation: Start with the job, not the tool. Ask what task eats the most hours or loses the most money. Then find the specialist that solves it.

All-in-one platforms are tempting. One login. One bill. But broad tools tend to do many things at an average level. A specialist built for one job usually does that job far better.

Use four questions to judge any marketing AI tool:

Does it save real time on a task you do every week? A tool you use once a quarter is not worth the learning curve.
Does it connect to your data? AI is only as smart as what it can see. A tool that reads your CRM, ad accounts, or analytics beats one that works blind.
Can you check its work? Look for citations, previews, and approval steps. AI makes mistakes. You need to catch them before customers do.
Can you measure its impact on revenue? If you cannot tie a tool’s output to leads and sales, you are guessing at its value.

The fourth question is where most stacks break. We’ll come back to it.

What Are the Best AI Content Marketing Tools?

Claude

Claude, made by Anthropic, is the top pick for many marketers who write long-form content. It produces prose that sounds human. It follows style guides closely. It handles nuance well, which matters for positioning work and thought leadership.

Projects let you store brand guidelines, product docs, and past content in one workspace, so every chat starts with context. Artifacts let you build and edit documents, landing page drafts, and simple tools right in the chat.

Claude also connects to outside tools through the Model Context Protocol (MCP). That means it can pull live data from your CRM, analytics, or project tools instead of relying on what you paste in.

Best for: Blog posts, AEO articles, email sequences, sales pages, positioning, and brainstorming.

ChatGPT

ChatGPT, from OpenAI, is the most widely used AI assistant. Its strength is range. It writes, analyzes files, generates images, browses the web, and runs custom GPTs built for specific tasks.

Many marketers use both. They draft and refine in Claude, then use ChatGPT for image generation or quick data work. Others build custom GPTs for repeat tasks like ad variations or social captions.

Best for: Mixed workloads, image generation, and teams that want one assistant for many jobs.

Jasper

Jasper is built for marketing teams, not individuals. Its core strength is control. Brand Voice settings, style rules, and approved knowledge keep output consistent across many writers and campaigns.

Jasper includes templates for ads, blog posts, product descriptions, and common copy frameworks. It suits larger teams that publish a high volume of content and need guardrails.

Best for: Enterprise and mid-market teams with strict brand standards.

What Are the Best AI Tools for SEO and AEO?

Search has split in two. Classic SEO gets you ranked in Google’s blue links. Answer engine optimization (AEO) gets you cited inside AI answers from ChatGPT, Perplexity, Claude, and Google’s AI Mode. You need tools for both.

Surfer SEO

Surfer analyzes the pages that rank for your target keyword. It then scores your draft as you write. It flags missing terms, suggests headings, and recommends length and structure.

It plugs into Google Docs and WordPress, so writers can optimize without leaving their editor. Surfer works best as a final pass on a draft, not as the writer itself.

Best for: Content teams that publish to rank in traditional search.

Ahrefs

Ahrefs is the standard for keyword research, backlink analysis, and competitor tracking. It now goes further. Its Brand Radar tracks how often your brand shows up in AI-generated answers, which pages AI tools cite, and how your share of voice compares to competitors.

That makes Ahrefs one of the few tools that measures AEO directly. You can see which questions trigger AI answers in your category and whether you appear.

Best for: SEO strategy, competitive research, and tracking AI search visibility.

Manifold MCP (the Cheapest Ahrefs and Semrush Alternative)

Manifold hosts its server at mcp.manifoldmcp.com/mcp. It connects with OAuth or an API key and works in Claude, ChatGPT, and Cursor.

It covers rankings, keyword research, backlinks, and traffic estimates. It also tracks how ChatGPT, Claude, Gemini, Perplexity, and AI Overviews mention your brand and which pages they cite. On top of that it pulls posts and comments from TikTok, Instagram, YouTube, LinkedIn, and Reddit, plus competitors’ live ads.

Why it matters: The difference is price. It has a generous free tier, and paid plans start from $20 per month. Ahrefs starts at $129 and Semrush at $139.

How Do You Optimize Content for AI Answers?

AI engines pull from content that answers questions fast and clearly. A few habits help:

Put a direct answer in the first two or three sentences.
Use headers phrased as real questions people ask.
Include comparison tables and clear lists.
Name specific tools, numbers, and facts.
Add an FAQ section with schema markup.
Publish original data that no one else has.

Pro Tip: Original data is the biggest lever. AI tools cite sources that add something new. A rewrite of what already ranks gives them no reason to pick you.

For a deeper walkthrough, see our guides on how to rank in Google AI Overviews and AI Overview tracking.

What Are the Best AI Tools for Market Research and Competitive Analysis?

Perplexity

Perplexity is an AI search engine. You ask a question. It searches the web and returns an answer with citations for each claim. You can click through and check every source.

Marketers use it to size markets, scan competitor messaging, gather stats for content, and check facts. It replaces hours of tab-hopping with a few minutes of reading.

Best for: Fast research where you need to verify sources.

Claude and ChatGPT Research Modes

Both Claude and ChatGPT offer deep research features. They run many searches, read dozens of pages, and write a structured report. These take longer than Perplexity but go deeper. Use them for competitive landscapes, buyer research, and category analysis.

Call Transcription and Analysis

The research tool most teams overlook is their own customer calls. Tools like Grain, and models like OpenAI’s Whisper, turn sales and support calls into searchable transcripts. Feed those to Claude or ChatGPT and you can pull objections, phrases customers use, and feature requests across hundreds of conversations.

Why it matters: This is boring work. It is also some of the highest-value AI work a marketer can do. Your customers’ own words beat any keyword tool for writing copy that converts.

Best for: Messaging, positioning, and finding content topics your buyers actually care about.

Predis.ai

Predis.ai creates social content from a single prompt. Describe a product or idea and it produces captions, graphics, carousels, and short videos. It includes a scheduler and publishes to multiple channels from one dashboard.

It works well for small teams and solo marketers who need a steady stream of posts without a designer.

Best for: High-volume social posting on a small budget.

Descript

Descript lets you edit audio and video by editing a text transcript. Delete a sentence in the text and it disappears from the video. Remove filler words with one click. Studio Sound cleans up background noise. Overdub can clone your voice to fix small mistakes without re-recording.

Descript is a strong tool for repurposing. Record one podcast or webinar. Cut it into clips, social videos, and blog material.

Best for: Podcasters, course creators, and anyone turning long video into short content.

Canva

Canva’s Magic Studio adds AI design tools to the platform most marketers already use. It can generate images, resize designs for every channel, write copy, and apply brand kits. If your team lives in Canva, its AI features are the easiest win.

Best for: On-brand graphics for teams without a full-time designer.

What Are the Best AI Tools for Paid Advertising?

AdCreative.ai

AdCreative.ai generates large batches of ad creatives and scores each one with a prediction model trained on ad performance. You get banners, social ads, and copy variations in minutes. It connects to Meta, Google, and LinkedIn ad accounts.

The value is speed of testing. More variations means you find winners faster.

Pro Tip: Confirm a winner is real before you scale it. Our A/B split test significance calculator helps.

Best for: Performance marketers who test creative at scale.

Albert.ai

Albert is an autonomous AI that runs digital ad campaigns. It manages bids, shifts budgets across channels, and tests audiences around the clock. It suits larger advertisers with complex, multi-channel spend.

Best for: Enterprise teams with large cross-channel budgets.

Native Platform AI

Meta Advantage+ and Google Performance Max both use AI to automate targeting, placement, and bidding. They are free to use and often perform well.

Limitations: There’s a catch. Each platform grades its own homework. Meta credits Meta. Google credits Google. Both claim the same sale. If you add up what each platform reports, you will often count more revenue than you actually earned. You need an outside source of truth to know which platform really drove the sale.

What Are the Best AI Email Marketing and Automation Tools?

HubSpot Marketing Hub

HubSpot builds AI into its CRM and marketing tools. It can draft emails, build workflows, score leads, summarize records, and help predict churn. Because it all sits on one customer database, the AI sees the full contact history.

Best for: Mid-size and growing teams that want CRM and marketing in one system.

ActiveCampaign

ActiveCampaign offers deep email automation at a price small businesses can manage. Its AI features help write emails, suggest automations, and segment contacts. It is popular with course creators, coaches, and ecommerce brands that run complex funnels.

Best for: Small teams with advanced automation needs.

Zapier

Zapier connects thousands of apps. Its AI features let you build automations by describing them in plain English. It also offers an MCP server, so AI assistants like Claude can trigger actions across your other tools.

Best for: Gluing your stack together without code.

Comparing CRMs too? See the best CRMs for lead tracking ROI.

What Are the Best AI Marketing Analytics and Attribution Tools?

This is the category most “best AI marketing tools” lists skip. It is also the one that decides whether the rest of your stack is working.

Every tool above can produce more content, more ads, and more emails. None of them can tell you which of those efforts made money. That requires attribution: tracking each lead from first click to final sale.

Why it matters: AI can reason about your marketing. It cannot remember the click. Ask ChatGPT or Claude which campaign drove your best customers and it has nothing to go on. It never saw the ad click, the opt-in, the email open, or the purchase. Without that history, AI analysis is a guess dressed up as an answer.

SegMetrics

Full disclosure: we make SegMetrics. Here is what it does and where it fits.

SegMetrics is a marketing attribution and analytics platform. It connects your ad platforms, CRM, email tool, and payment processor. It then tracks every lead across every touchpoint, from first visit to purchase and beyond. You see which ads, emails, funnels, and channels produce revenue, not just clicks.

Two features make it an AI tool in its own right:

The SegMetrics MCP server connects your attribution data to AI assistants like Claude. You can ask plain-English questions such as “Which traffic source brought in the highest-value customers last quarter?” and get answers grounded in your real data.
SegMetrics AI Builder (in beta) builds SegMetrics reports from natural-language requests. Describe the report you want and it builds it.

The result: your AI assistant stops guessing. It reasons over the actual path each customer took.

Pro Tip: Not sure what to ask your AI about your marketing? The AI Marketing Analyst Prompt Pack gives you ready-to-use prompts for ChatGPT and Claude, pulled from our library of 174 attribution prompts. Each one is graded by how well AI can answer it from a basic data export. You’ll see which questions your AI can handle today, and which ones need real attribution data to get right. Get the free prompt pack.

Best for: Course creators, coaches, agencies, and digital marketers who need to know which marketing produces revenue, and who want their AI tools to know it too.

Want to see where your data breaks down? Start a 14-day free trial and see your full lead history in SegMetrics, including data from before you signed up. Comparing options? See our full guide to the best marketing attribution tools for 2026 or how SegMetrics stacks up as a Hyros alternative.

Google Analytics 4

GA4 is free and widely used. It includes some AI-driven insights and predictive metrics. It tracks website behavior well. It struggles to connect that behavior to revenue that happens later, in a CRM or payment system, especially for businesses with long sales cycles or recurring payments. We cover the gaps in detail in Sick of GA4?

Best for: Website traffic analysis and teams just getting started.

What Are the Best AI Marketing Tools for Small Business?

You do not need twelve tools. Most small teams can cover the core jobs with five or six.

For a course creator or coach: Claude for content and emails. Descript for video and podcast repurposing. Canva for graphics. ActiveCampaign for email automation. SegMetrics for attribution, connected to Claude through MCP. Running ads? Start with these ad platforms for coaching businesses.

For a marketing agency: Claude or ChatGPT for client content. Ahrefs for SEO and AI visibility reporting. AdCreative.ai for ad testing. Zapier to connect client tools. SegMetrics to prove ROI to clients with revenue data, not vanity metrics. See how it compares to AgencyAnalytics.

For a lean ecommerce brand: ChatGPT for product copy and images. Predis.ai for social. Meta Advantage+ for ads. HubSpot or ActiveCampaign for email. SegMetrics to see which channels drive repeat buyers, not just first orders.

Ask yourself: Notice the pattern. Each stack has one tool that measures results. Without it, can you tell which of the other tools earn their cost?

Where Do AI Marketing Tools Fall Short?

AI tools are powerful. They also have clear limits. Know them before you build your stack.

They make things up. AI writing tools can invent stats, quotes, and sources. Always check facts, especially in published content. Tools with citations, like Perplexity, make this easier.

They sound generic without guidance. Out of the box, AI copy often reads like everyone else’s. Feed it your brand guide, past winning content, and customer language. The output improves fast.

They work blind without your data. An AI assistant that cannot see your CRM, ad accounts, or revenue data can only give general advice. Connected tools give specific, useful answers.

Platform AI favors the platform. Ad platforms optimize toward what they can measure and credit themselves for conversions. An independent attribution source keeps them honest.

They do not replace judgment. AI speeds up execution. It does not know your market, your margins, or your customers the way you do. Use it to move faster, not to stop thinking.

How Do You Measure the ROI of AI Marketing Tools?

Measure each tool against the job it was hired to do.

For time-saving tools like Claude, Descript, or Zapier, track hours saved per week. Multiply by your hourly cost. Compare to the subscription.

For revenue-driving tools like AdCreative.ai, Albert, or your email platform, track the leads and sales their output produces. This requires attribution. You need to connect a specific ad, email, or funnel to the revenue it generated.

For visibility tools like Surfer and Ahrefs, track rankings, AI citations, organic traffic, and, most important, the leads and revenue that traffic brings in. Our guide to content marketing reporting covers the metrics that matter.

Why it matters: The common thread is revenue data. Time saved is nice. Revenue earned is the point. If you cannot tie a tool’s output to sales, you cannot know if it pays for itself. A marketing KPI dashboard keeps those numbers in one view.

Frequently Asked Questions

What are the best AI tools for marketing?

The best AI tools for marketing are Claude and ChatGPT for content, Surfer SEO and Ahrefs for search, Perplexity for research, Descript and Predis.ai for video and social, AdCreative.ai for ads, HubSpot and ActiveCampaign for automation, and SegMetrics for attribution. Pick one specialist per job and connect them to your revenue data.

Is Claude or ChatGPT better for marketing?

Many marketers prefer Claude for long-form writing and on-brand copy because its output sounds more natural and follows style rules closely. ChatGPT offers a wider feature set, including image generation and custom GPTs. Plenty of teams use both: Claude for writing, ChatGPT for images and quick tasks.

What is the best all-in-one AI marketing tool?

HubSpot Marketing Hub comes closest, with AI built into its CRM, email, and content tools. Still, most teams get better results from specialist tools for each job, connected through shared data and tools like Zapier.

Are there free AI marketing tools?

Yes. ChatGPT, Claude, and Perplexity all offer free plans. Canva has a free tier with some AI features. Google Analytics 4, Meta Advantage+, and Google Performance Max cost nothing to use beyond your ad spend. Free plans have usage limits, so busy teams usually upgrade.

What are the best AI tools for marketing agencies?

Agencies get the most from Claude or ChatGPT for client content, Ahrefs for SEO and AI visibility reporting, AdCreative.ai for creative testing, Zapier for connecting client tools, and SegMetrics for proving client ROI with revenue data.

Can AI replace a marketing team?

No. AI can draft, design, test, and automate much faster than people. It cannot set strategy, understand your customers deeply, or make judgment calls about your brand. The best results come from skilled marketers using AI to do more with less.

Which AI tool shows which marketing actually drives revenue?

An attribution platform. SegMetrics tracks each lead from first click to purchase across ads, email, and funnels, then shows which sources produce revenue. Its MCP server lets AI assistants like Claude answer questions using that data.

What is MCP and why does it matter for marketers?

MCP stands for Model Context Protocol. It is an open standard that lets AI assistants connect to outside tools and data. For marketers, it means Claude or other assistants can read your CRM, analytics, or attribution data directly. You get answers based on your real numbers instead of generic advice.

What AI tools help with AEO?

Ahrefs tracks how often your brand appears in AI answers and which pages AI tools cite. Claude and ChatGPT help write content structured for AI answers. Perplexity lets you test how AI engines answer questions in your category today. SegMetrics also offers AI search tracking to see which AI-referred visitors turn into revenue.

How many AI marketing tools should a small business use?

Start with three to five. Cover writing, one main channel such as email or social, and measurement. Add tools only when a clear bottleneck appears.

The Bottom Line

The best AI marketing tools are specialists. Claude and ChatGPT for writing. Surfer and Ahrefs for search. Perplexity for research. Descript, Predis.ai, and Canva for content. AdCreative.ai and Albert for ads. HubSpot and ActiveCampaign for automation.

But tools that create more marketing only help if you know which marketing works. Build your stack around a source of truth for revenue. Then connect it to your AI. That’s how you stop guessing and start knowing which tools, channels, and campaigns earn their keep.

Start your 14-day free trial, grab the free AI Marketing Analyst Prompt Pack, or see SegMetrics pricing.

Can AI Replace Marketing Attribution? No. Here's Why.

Denise Cullom — Wed, 30 Sep 2026 15:00:00 GMT

Every marketer has tried it by now. You export a spreadsheet. You drop it into ChatGPT or Claude. You ask, “Which channel drives my best customers?”

The answer comes back fast. It sounds smart. It has bullet points and percentages.

It is also probably wrong.

AI can reason about your marketing. It cannot remember the click. And without the click, there is no attribution. There is only a guess dressed up as analysis.

This article explains what AI does well, what it cannot do, and why attribution software like SegMetrics is not something AI replaces. It is something AI needs.

What Marketing Attribution Actually Requires

Attribution answers one question: what caused this sale?

To answer it, you need a record of every touch a customer made before they paid. The ad they clicked. The page they landed on. The email they opened. The webinar they watched. The day they bought.

That record has to be captured as it happens. It has to be tied to one person. And it has to connect to revenue.

Why it matters: Attribution is three jobs. Capture, connect, credit. AI does none of the first two. And it cannot do the third without them.

Reason 1: AI Was Not There When the Click Happened

A language model does not sit on your website. It does not read UTM parameters. It does not see the referrer. It does not log the first page a visitor hits.

That data exists for a moment. Then it is gone unless something captures it.

Attribution software captures it. SegMetrics records the source of every lead at the moment they arrive, then keeps that record for the life of the customer.

Ask an AI to figure out where a customer came from six months later, and it has nothing to work with. It cannot go back in time. No prompt fixes a click that was never recorded.

Reason 2: AI Cannot Stitch Identity Across Your Tools

Your customer does not live in one system.

They click a Facebook ad. They opt in through a form. They land in ActiveCampaign or Kit. They buy through Stripe or ClickFunnels. They book a call in Calendly.

Each tool knows a piece of the story. None of them knows the whole thing.

Attribution means joining those pieces into one person with one timeline. That takes direct integrations, matching logic, and a database built for it. SegMetrics does this continuously, across every connected tool, for every contact.

An AI chatbot sees whatever file you paste in. It does not have live connections to your CRM, your payment processor, and your ad accounts. It cannot match the anonymous visitor from March to the buyer in July.

Reason 3: Exports and Uploads Break at Scale

The workaround is to export everything and let AI sort it out. It fails for a few reasons.

Files get big. Context windows have limits. The model reads part of your data and summarizes the rest.

Exports are lossy. Most platforms don’t export the fields you need to join records. Timestamps come in different formats. Emails don’t match. Order IDs live in one system and not the other.

Exports go stale. The data is outdated the moment you download it. You’d need to repeat the whole process every week to keep your answers current.

Attribution is not a one-time analysis. It is a running system. A chat window is not a running system.

Reason 4: AI Fills Gaps With Confident Guesses

When data is missing, a good analyst says, “I don’t know.”

A language model often doesn’t. It fills the gap with something plausible. It will give you a cost per lead, a return on ad spend, a top channel. The numbers will look real. They may have no basis in your actual data.

Limitations: In marketing, a confident guess is expensive. A made-up ROAS figure can move your budget. A wrong top channel can kill your best campaign.

Attribution software does not guess. If a sale has no tracked source, SegMetrics shows it as unattributed. That honesty is the point. You can’t fix a gap you can’t see.

Reason 5: AI Inherits the Bias of Ad Platform Data

Ask AI to read your Facebook and Google dashboards and it will report what those dashboards say.

The problem is that ad platforms grade their own homework. Each one tends to claim credit for the same conversion. Add up the numbers and you often “earn” more revenue than you made.

Data-Driven Insight: Say you made $50,000 in real revenue this month. Facebook reports $38,000 in conversions. Google reports $27,000. Add them up and your ad platforms claim $65,000. That’s 130% of what you actually earned. An AI reading those dashboards will take both numbers at face value.

AI has no independent record to check against. It repeats the inflated numbers.

SegMetrics tracks conversions from your side, tied to actual revenue in your payment system. One sale. One customer. Credit split by the model you choose.

Reason 6: Attribution Needs Consistent Answers

Ask an AI the same question twice. You may get two different answers.

That’s fine for brainstorming. It’s a problem for reporting. Your team needs to see the same number on Monday that they saw on Friday. Your CFO needs to trust the math.

Attribution models are defined rules. First touch. Last touch. Linear. Time decay. The same inputs give the same outputs every time. That’s what makes them useful for decisions.

See the Difference: One Customer, Two Views

Here’s a typical journey for a course buyer. The names and numbers are illustrative, but the pattern is one most marketers will recognize.

Meet Sarah.

April 3: Sarah clicks a Facebook ad and lands on your blog. She leaves.
April 9: She comes back through an organic Google search. She downloads your free guide and joins your email list.
Over the next two weeks: She gets six emails. She opens four. She clicks one invite and registers for your webinar.
April 24: She watches the webinar live. She doesn’t buy.
April 27: She searches your brand name, clicks a Google ad, and buys your $997 course through Stripe.

What AI sees from your exports

Your Stripe export shows an email, a date, and $997. Your Google Ads report claims the sale. Your Facebook report may claim it too, if the conversion falls inside its attribution window. Your email platform shows a contact with some opens.

Nothing ties these together. Ask ChatGPT which channel drove the sale, and it will likely say Google Ads. That’s the only file with a clear conversion attached.

What SegMetrics sees

One person. Five touches. One $997 sale.

First touch: Facebook ad
Lead source: Organic search
Nurture: Email and webinar
Last touch: Branded Google search

Now you can see the real story. The Google ad didn’t create Sarah. It caught her on the way to the checkout. Facebook started the relationship. Your webinar did the selling.

Ask yourself: If you cut the Facebook campaign because “Google gets the sales,” what happens to the top of your funnel? It shuts off.

That is the gap between guessing and knowing.

AI Assistant vs. Attribution Software

Capability	ChatGPT or Claude	SegMetrics
Captures clicks and UTMs live	❌ No	✅ Yes
Records original lead source	❌ No	✅ Yes
Connects one customer across your tools	❌ No	✅ Yes
Works from live, current data	⚠️ Only if connected	✅ Yes
Ties touches to actual revenue	⚠️ Only what you upload	✅ Yes
Shows unattributed sales honestly	⚠️ May guess	✅ Yes
Same question, same answer	⚠️ Not guaranteed	✅ Yes
Explains trends in plain English	✅ Yes	✅ Yes, via MCP

The pattern is clear. AI is strong at interpretation. Attribution software is strong at the record. You need both, in that order: record first, then interpret.

What AI Does Well

None of this means AI is useless for marketing. It’s excellent at the parts that come after the data exists.

AI is good at spotting patterns in clean data. It is good at explaining trends in plain English. It is good at asking follow-up questions, drafting reports, and suggesting next tests.

Pro Tip: The key word is clean. AI is a great analyst. It is a terrible data collector. Give it tracked data, not exports.

The Right Answer: AI on Top of Attribution Data

The best setup is not AI or attribution. It’s AI powered by attribution.

SegMetrics captures every touch, stitches every customer, and ties it all to revenue. Then AI can work with data that is complete, current, and true.

SegMetrics makes this direct. The SegMetrics MCP server connects AI assistants like Claude to your live attribution data. You ask a question in plain English. The AI answers from your real numbers, not a stale export and not a guess.

Inside the app, SegMetrics AI Builder turns natural-language questions into reports. You describe what you want to see. It builds the report from your tracked data.

That’s the difference. AI brings the reasoning. SegMetrics brings the memory.

Frequently Asked Questions

Can ChatGPT do marketing attribution?

No. ChatGPT can analyze data you give it, but it cannot track clicks, capture lead sources, or connect customer activity across your tools. Attribution requires software that records touches as they happen.

Will AI replace marketing attribution tools?

Not likely. AI needs accurate data to give accurate answers. Attribution tools produce that data. The two work best together.

Why can’t I just upload my data to Claude or ChatGPT?

You can, but results break down fast. Exports are incomplete, go stale quickly, often lack the fields needed to match records, and large files exceed what the model can read in full. The AI may also fill gaps with invented numbers.

Why do my ad platforms report more revenue than I actually made?

Each platform counts conversions using its own tracking and attribution window. When a customer touches more than one platform, each may claim the full sale. Attribution software tracks from your side, so each sale is counted once.

What is the best way to use AI for marketing analytics?

Connect AI to a trusted attribution source. With the SegMetrics MCP server, AI assistants can query your live attribution data directly, so answers come from real tracked revenue.

Does SegMetrics use AI?

Yes. SegMetrics AI Builder uses AI to build reports from plain-language questions, and the MCP server lets AI assistants answer questions using your SegMetrics data.

The Bottom Line

AI is changing how marketers analyze results. It is not changing the need for good data.

If you want AI to tell you what’s working, first give it something true to work with. That starts with tracking every click, every lead, and every dollar.

AI can reason about your marketing. SegMetrics remembers the click.

Start your SegMetrics trial and give your AI real data to work with.

How to Tell If Your Ads Are Actually Profitable (Beyond Facebook Reporting)

Denise Cullom — Thu, 24 Sep 2026 09:00:00 GMT

How do I know if my ads are actually profitable?

Short answer

You know your ads are profitable when you can connect ad spend to real revenue from your payment processor or CRM, not to conversions reported by the ad platform. Then compare that revenue to what you spent over a realistic time window. If you're judging profitability from Facebook or Google's dashboards alone, you're seeing each platform's version of events, and it almost always overstates results.

If you’ve ever looked at a Facebook Ads dashboard showing a 4x ROAS while your bank account tells a different story, you’re not imagining things. This is one of the most common frustrations in digital marketing. It’s also fixable.

Why can’t I tell if my ads are making money?

Most businesses can’t tell whether their ads are profitable because their ad data and their revenue data live in separate systems that don’t talk to each other.

Your ad platforms know who clicked. Your CRM or checkout knows who bought. Without something connecting those two records at the contact level, you’re left guessing. You can trust the ad platform’s numbers, or you can stare at a spreadsheet trying to line up dates. Contact-level journey reporting closes that gap by tying every click, opt-in, and purchase to the same person.

A few other things make this harder:

Long sales cycles. If someone clicks an ad today but buys 45 days later after a webinar and three emails, most platform reporting will never connect that sale to the ad. This is especially common for course creators and infoproduct businesses with long nurture sequences.
Multiple touchpoints. A customer might see a Facebook ad, search your brand on Google, and click an email before buying. Which one gets credit?
Privacy changes. Since Apple’s App Tracking Transparency rollout, ad platforms can observe fewer conversions directly. They fill the gaps with modeled estimates.

Why doesn’t Facebook ad reporting match my revenue?

Facebook (Meta) ad reporting rarely matches actual revenue because Meta uses its own attribution rules, includes modeled conversions, and counts sales that other channels also claim.

Here’s what’s happening behind the scenes.

View-through attribution inflates results. By default, Meta credits a conversion to your ad if someone clicked within a set window or simply viewed the ad within a shorter window. Someone who scrolled past your ad and bought a day later because a friend recommended you still counts as an ad conversion.

Every platform claims the same sale. Facebook, Google, TikTok, and your email tool each report conversions independently. Add up what each platform says it generated, and you’ll often get more revenue than your business actually made. This is called double counting, and it’s one of the biggest reasons “my ads person paints one picture, but I’m not seeing it on the bottom line.” We break down this “greedy attribution” problem in detail in Why Facebook’s Attribution Data Is Mostly Guesswork.

Modeled conversions are estimates. When the pixel can’t observe a conversion directly, Meta estimates it statistically. These numbers are useful for the algorithm, but they aren’t a ledger of real transactions.

The platform grades its own homework

Ad platforms are incentivized to show strong performance. That doesn't mean the data is fake, but it does mean you need an independent second set of eyes.

What is ad attribution?

Ad attribution is the process of assigning credit for a sale or lead to the marketing touchpoints that influenced it. Good attribution answers the question “Which ads, campaigns, and channels actually produced revenue?” and does it using your real sales data.

Common attribution models

Model	How it assigns credit	Best for
First touch	100% to the first interaction	Understanding what generates awareness and new leads
Last touch	100% to the final interaction before purchase	Understanding what closes the sale
Linear	Equal credit to every touchpoint	Seeing the full journey without bias
Position-based (U-shaped)	Most credit to first and last touch, the rest split between the middle touchpoints	Balancing acquisition and conversion
Time decay	More credit to touchpoints closer to the sale	Short sales cycles and promotions

No single model is “correct.” The real value comes from comparing models side by side. An ad that looks weak on last touch might be your best lead generator on first touch. SegMetrics lets you switch between attribution models on the fly, including First Visit, Opt-in, Purchase, and a linear model that credits every touchpoint equally. See how each attribution model works for a full breakdown.

How do I calculate true ROAS?

True ROAS (return on ad spend) is calculated by dividing actual revenue from customers acquired through an ad by what you spent on that ad. The revenue should come from your payment processor or CRM, not the ad platform.

True ROAS = Verified Revenue from Ad-Acquired Customers ÷ Ad Spend

To get there, you need three things:

Clean tracking on every ad. Consistent UTM parameters on every link, so each lead carries its source with it. Our guide to tracking links and URL builders walks through the setup.
Contact-level matching. Each lead’s first click needs to connect to that same person’s eventual purchase, even weeks later.
Revenue from the source of truth. Pull purchase data from Stripe, your cart, or your CRM, including refunds, upsells, and repeat purchases.

Once you’re measuring correctly, here’s how to improve your ROAS.

Should I measure ROAS on first purchase or lifetime value?

Measure both. First-purchase ROAS tells you whether an ad pays back quickly. LTV-based ROAS tells you whether it’s worth scaling. An ad with a 0.8x first-purchase ROAS can be one of your most profitable campaigns if those customers go on to buy again. An ad with a 3x ROAS on a one-time discount offer might attract customers who never return. Lifetime value tracking ties every future payment back to the ad that brought the customer in.

Checking ROAS at 30, 60, and 90 days after acquisition gives you a much clearer picture than a single snapshot. SegMetrics’ Optimization Reports show this by channel, campaign, and funnel step.

How do I know which ads to scale and which to pull back?

Scale ads that produce customers at a profitable cost based on verified revenue and lifetime value. Pull back ads that only look good inside the ad platform’s reporting.

A simple decision framework

Scale: Verified ROAS is above your break-even point, and customers from this ad have strong 60 to 90 day value.
Optimize: The ad generates plenty of leads but few sales. The ad may be fine, and the problem may be further down the funnel.
Watch: Early results are promising but the sample is small. Give it enough spend and time to cover your typical sales cycle.
Cut: The platform reports strong results, but you can't find matching revenue in your own sales data.

That last category is where most wasted ad spend hides. To set your thresholds, compare customer acquisition cost against lifetime value using these marketing metrics that drive higher ROI.

Which ad platform performs best for my business?

The best-performing ad platform is the one that produces the most verified revenue per dollar spent, measured with the same attribution rules across every platform.

Comparing platforms using their own dashboards is like comparing students who each graded their own test. Facebook uses Facebook’s attribution window. Google uses Google’s. To compare fairly, you need one neutral system applying the same model to every channel.

When businesses do this comparison consistently, they often find the platform they were about to cut was quietly starting most customer journeys. They also find that the platform with the best-looking dashboard was taking credit for sales that would have happened anyway. If Google is part of your mix, these 12 key levers for optimizing Google Ads can help once you know what it’s truly worth.

How do I track attribution for a product launch?

To track attribution for a launch, set up tracking before launch day. Tag every ad, email, affiliate link, and social post with consistent UTMs. Make sure every lead is captured with their source at opt-in. Then connect those leads to purchases when the cart opens.

A launch attribution checklist

Standardize a UTM naming convention (source, medium, campaign, content)
Tag every paid ad, organic post, email, and affiliate link
Confirm lead source is saved to the contact record at opt-in
Connect your payment processor so purchases link back to contacts
Decide your attribution models in advance (first touch for lead gen, last touch for cart close)
Build a live dashboard for launch week so you can shift budget in real time (here's how to build a marketing KPI dashboard in 5 steps)

Launches move fast. If you wait until after the cart closes to figure out attribution, you’ve lost your chance to move budget toward what’s working while it matters.

Do I need more advanced attribution?

You likely need more advanced attribution if any of the following are true:

You run ads on two or more platforms
Your sales cycle is longer than a few days
You sell through webinars, calls, trials, or email sequences
Your ad platform numbers don't match your actual revenue
You can't answer "what's the lifetime value of customers from this campaign?"

Advanced marketing attribution software connects your ad platforms, CRM, email tool, and payment processor. It then shows the full customer journey from first click to final purchase, independent of what any single ad platform claims. Compare your options in The Best Marketing Attribution Tools for 2026, or see how SegMetrics stacks up as an alternative to Hyros.

How SegMetrics helps you know if your ads are profitable

SegMetrics connects your ad accounts, CRM, and payment data so you can see which ads, campaigns, and channels are producing real revenue, including repeat purchases and lifetime value. Instead of trusting each platform’s version of the truth, you get one independent view of your ad performance. It’s built for marketers and agencies who need to decide what to scale, what to cut, and how to prove it. See pricing and start a 14-day free trial.

Frequently asked questions

Is Facebook ads reporting accurate?

Facebook ads reporting is directionally useful but not a reliable measure of actual revenue. It includes view-through conversions, modeled estimates, and sales other channels may also claim. For profitability decisions, verify results against your own sales data.

What is a good ROAS?

A good ROAS is any figure above your break-even point, which depends on your margins. A business with 80% margins can profit at a much lower ROAS than one with 20% margins. Calculate break-even ROAS as 1 divided by your profit margin.

What’s the difference between ROAS and ROI?

ROAS measures revenue divided by ad spend. ROI measures profit divided by total investment, accounting for product costs, fees, and other expenses. ROAS tells you whether ads drive sales. ROI tells you whether those sales make money.

Why do my ad platforms report more sales than I actually made?

Each platform reports conversions independently, using its own attribution rules. When a customer interacts with several platforms before buying, each one may claim the full sale. The result is double or triple counting.

How long should I wait before judging an ad’s performance?

Wait at least as long as your typical sales cycle. If most customers buy two to three weeks after opting in, judging an ad after three days will undervalue it.

What is multi-touch attribution?

Multi-touch attribution spreads credit for a sale across multiple marketing interactions instead of giving it all to one. It helps you see how channels work together, such as a Facebook ad that starts a journey and an email that closes it. Learn more about how attribution works in SegMetrics.

The 6 marketing attribution models explained (and how to choose the right one)

Keith Perhac — Wed, 23 Sep 2026 09:00:00 GMT

A marketing attribution model is a rule for deciding which touchpoints get credit for a sale. The six main models are first-touch, last-touch, linear, time-decay, U-shaped, and W-shaped. Single-touch models hand all the credit to one touchpoint. Multi-touch models split it across several.

Which one should you use? That depends on the question you’re trying to answer.

First, let’s walk through all six types of attribution models — what each one does, how it assigns credit, and when it’s the right tool — then we’ll look at a simple framework for choosing which to use.

What is an attribution model?

Picture a real customer journey. Someone clicks a Facebook ad, browses your website, downloads a lead magnet, reads an email, then attends a webinar. At the end of the webinar, they buy your product.

That’s (at least) five touchpoints leading up to one sale. Which of those touchpoints is responsible for that sale?

An attribution model is how you divide the credit for that sale among those five touchpoints.

Attribution can be divided into two types based on how many touchpoints get credit:

Single-touch models give 100% of the credit to one touchpoint. The first one or the last one.
Multi-touch models spread the credit across several touchpoints, in different proportions.

Neither approach is “right.” They answer different questions. Picking the model that matches your question is what really matters.

Let’s dig into each model.

Single-touch attribution models

Single-touch models are blunt on purpose. One touchpoint gets all the credit. Everything else gets none. That sounds crude, but it’s exactly what you want when you have a question about the start or the end of the journey.

First-touch attribution

How it assigns credit: 100% to the very first touchpoint. AKA, the thing that first brought someone into your world.

First-touch attribution answers one question: “Where do my leads come from?” If someone’s journey started with a podcast interview six months ago, first-touch gives the podcast full credit for every sale that follows, no matter how many emails and ads came later.

Use first-touch attribution when you’re digging into acquisition. It highlights your top of funnel activities that are actually introducing you to future customers.

Last-touch attribution

How it assigns credit: 100% to the last touchpoint before the purchase.

Last-touch attribution (sometimes called last-click) answers the opposite question: “What tipped them over the line?” It’s the model for understanding what closes the sale.

One important asterisk: the literal last touch is often something useless, like your checkout page or a direct visit. So when people say “last-touch,” they almost always mean the last meaningful, non-direct touch, like the final email or ad they clicked before buying. That’s the marketing action worth tracking.

Use it when you want to know which email, ad, or offer does the closing.

Multi-touch attribution models

Multi-touch models split the credit across several touchpoints instead of just one. This gives you a more “complete” picture of how your marketing funnel is operating (but this comes with a big warning, which we’ll discuss).

Linear attribution (full funnel)

How it calculates credit: Every touchpoint in the journey gets an equal share. Four touchpoints, 25% each. Ten touchpoints, 10% each.

Linear attribution is the simplest multi-touch model, and it’s the closest thing to a true “full-funnel” view. Linear treats every step as having mattered. It won’t tell you which touch mattered most, but it’s a clean way to see everything that was involved.

Use it when you want a balanced, no-favorites view of the whole journey.

If you have a long, multi-channel sales cycle, really saying which touchpoints caused a sale may be unrealistic. But linear attribution allows you to look at influence, not just cause. Linear attribution highlights all of the marketing actions which support your prospects along the way (even if they don’t individually close the deal).

Time-decay attribution

How it calculates credit: Touchpoints closer to the purchase get more credit. Earlier ones get less, on a sliding scale.

The logic: the email someone clicked the day they bought probably mattered more than the ad they saw three months earlier. Time-decay attribution bakes this assumption into the model. So in a three-month nurture sequence, the webinar someone attended last week outranks the Facebook ad they clicked back in month one.

Use it when you have longer sales cycles and you believe recent touches do the heavy lifting. This is common in nurture-driven funnels.

Position-based (U-shaped) attribution

How it calculates credit: The first and last touchpoints get the biggest shares (typically 40% each), and the touches in the middle split the remaining 20%.

Position-based says the two moments that matter most are discovery (how they found you) and conversion (what closed them). And the middle is just a supporting, secondary role. Like a U, it’s heavier on both ends. So the Facebook ad that first pulled you in and the webinar that finally closed you each take 40%, while the three steps in between split the remaining 20%.

Use it when you care about both acquisition and closing and don’t want to ignore either end.

W-shaped attribution

How it calculates credit: W-shaped works like U-shaped, but it adds a third heavily-weighted milestone in the middle. This is usually the moment someone becomes a lead. So first touch, lead-creation, and last touch each get a big share (often ~30% each), and the rest split the remainder.

W-shaped is for businesses that treat becoming a lead as a real, credit-worthy event, not just a step on the way. It’s the most complex of the “shaped” models (outside of custom-built attribution models).

Use it when lead creation is a distinct, meaningful stage in your funnel worth measuring on its own.

A quick warning about multi-touch

Multi-touch models look sophisticated and feel advanced… and that's a trap. More complicated attribution models don't mean the answers you get are better or more actionable. Often, multi-touch models just smooth everything into an average that doesn't answer anything.

So focus on the question you want to answer, then pick your attribution model.

What about Google’s data-driven attribution?

If you’ve spent time in GA4 or Google Ads, you’ve seen a model that isn’t in the classic six: data-driven attribution (DDA). It’s worth understanding because it’s now Google’s default.

How it works: Instead of using a fixed rule (like “40% to the first touch”), data-driven attribution uses an algorithm that looks at your account’s actual conversion data and learns how much each touchpoint tends to contribute, then assigns fractional credit based on those patterns. In theory, it’s the most tailored model of all. The credit split comes from your data, not a rule of thumb.

The catch: Google’s data-driven attribution is a black box. You can’t fully see why it credited a touchpoint the way it did. Plus, DDA needs a high volume of conversions to work well, which you may not have. Worst of all, DDA is still Google grading its own homework. It runs inside Google’s ecosystem, on data only Google can see, and it rarely includes your whole funnel.

For most of our audience, data-driven attribution is something to understand rather than rely on. If you can’t trace the number back to real people and real journeys, it’s hard to make a confident decision from it.

What about first engagement attribution?

Some attribution tools provide additional or specialty attribution models. For example, SegMetrics also offers First Engagement Attribution. This is like first-touch attribution, but it prioritizes the first touchpoint within a report’s timeline. In other words, instead of identifying the lifetime first touchpoint, a First Engagement model might identify the first touch point within the last 30 days. This helps you see what marketing actions are driving engagement recently.

Which attribution model should you use?

Never pick an attribution model because it’s fancy. Pick it because it answers your question. So start with the question.

If you want to know…	Use this model
Where my best leads come from	First-touch
What closes the sale	Last-touch
Everything that was involved, weighted equally	Linear
Which recent touches do the closing	Time-decay
Both how they found me and what closed them	Position-based (U-shaped)
How they found me, when they became a lead, and what closed them	W-shaped

Most businesses don’t need all six. In our experience, you really only need three views to run your marketing well:

Where did people come from? → first-touch
Why did they buy? → last-touch
What influenced them along the way? → linear

That’s it!

Master those three questions and you’ve covered about 99% of your use cases.

Can you use more than one model?

Not at once. But you can switch between attribution models to look at your data from different angles. The key is understanding what the attribution model is telling you.

(Note that most platforms – especially ad platforms – lock you into a single model. One of the biggest benefits of marketing attribution software is that you get full control of the attribution models you use.)

The complete guide to marketing attribution walks through how these models fit the bigger picture of marketing attribution. If you’re fuzzy on the basics, start with what marketing attribution is. And remember that your attribution window — how far back you count touches — changes these numbers too.

Same sale, different model: a worked example

Models are abstract until you watch one sale move around. Let’s return to our original customer journey. Say someone buys a $2,000 course after this journey:

Found you through a Facebook ad
Browsed your website
Downloaded your lead magnet
Read an email
Attended your webinar and bought

Watch how the credit — and the story — can change by model:

First-touch: 100% to the ad. Story: “Ads drive our sales.”
Last-touch: 100% to the webinar. Story: “Webinars drive our sales.”
Linear: ~20% to each of the five touches. Story: “The whole funnel drives the sale.”
Time-decay: Most credit to the webinar and email, least to the Facebook ad. Story: “Our emails and webinars drive the business.”
U-shaped: 40% ad, 40% webinar, 20% split among the middle. Story: “Ads and webinars are the heart of our business.”
W-shaped: ~30% Facebook ad, ~30% lead magnet, ~30% webinar. Story: “Our funnel is driven by a few critical moments.”

Same customer. Same $2,000. Six completely different conclusions about where to spend your next dollar.

This is exactly why “which model?” isn’t just a technicality. This question shapes how you understand your marketing.

Understanding attribution models, you can better understand your results. In our example:

Facebook Ads drive awareness
Lead magnets drive leads
Webinars drive sales
The full funnel includes multiple touches…over time…through multiple channels

A good attribution tool will not just help you assign credit, it will also allow you to find the outliers at each stage of your funnel and compare the performance of different segments. This is where attribution meets optimization.

Here’s a breakdown of the 6 core principles of marketing analytics (including how to leverage outliers and segments to understand your data at a deeper level).

Frequently asked questions

What is an attribution model? An attribution model is a rule for deciding which marketing touchpoints get credit for a conversion. Some models give all the credit to a single touchpoint (first-touch or last-touch). Some split credit across the whole journey (linear, time-decay, position-based, W-shaped). The model you choose determines which channels and campaigns look successful.

What are the six attribution models? First-touch (credit to the first touchpoint), last-touch (credit to the last), linear (equal credit to all), time-decay (more credit to recent touches), U-shaped (most credit to first and last), and W-shaped (most credit to first, lead-creation, and last). You’ll also run into Google’s data-driven model, which uses an algorithm instead of a fixed rule.

What’s the difference between first-touch and last-touch attribution? First-touch gives 100% of the credit to the touchpoint that first brought someone in. AKA, “Where did this lead come from?” Last-touch gives 100% to the final touch before the purchase. AKA, “What closed the sale?” First- and last-touch often credit completely different channels. Most importantly, both can be right because they answer different questions.

Which attribution model is best? There’s no single best model! The right one depends on what you want to know. Use first-touch to learn where leads come from, last-touch to learn what closes them, and linear to see what influenced them along the way. Most businesses only need these three.

What is multi-touch attribution? Multi-touch attribution splits credit across several touchpoints in a journey instead of crediting just one. Linear, time-decay, U-shaped, and W-shaped are all multi-touch models. Multi-touch gives a fuller picture, but it can smooth everything into an average that’s hard to act on.

What is data-driven attribution? Data-driven attribution is Google’s default model. Instead of a fixed rule, it uses an algorithm to study your account’s conversion patterns and assign fractional credit to each touchpoint. It’s tailored to your data, but it’s a black box, needs high conversion volume, and only sees the part of the funnel Google can track.

Ready to unlock attribution modeling for your own business?

The fastest way to understand attribution models is to stop reading about them and start seeing it applied to your own data. A good attribution tool lets you toggle between attribution models in real time and see how different questions shape the data.

Start your free 14-day trial to see your funnel under first-touch, last-touch, and full-funnel attribution in a few clicks.

New here? The complete guide to marketing attribution ties it all together. Also check out our guide to cross-device attribution.

Mike Buckbee: How to get found when AI controls search (Episode 15)

Ryan Johnson — Tue, 22 Sep 2026 16:00:00 GMT

“ChatGPT just recommended three of your competitors and skipped you entirely.”

84% of Google searches now end without a single click, and AI chatbots are quietly becoming the new front door to your customers’ research.

So if you don’t show up when your customers ask, you don’t exist.

In this episode of Data Beats Opinion, we’re talking with Mike Buckbee, founder of Knowatoa, one of the first tools built to track how brands show up inside AI search. Mike breaks down why traditional SEO is now a lagging indicator, what actually earns you a spot in an AI’s answer, and how to build a strategy that holds up across Google, ChatGPT, and Claude alike.

Watch the full episode →

Meet Mike Buckbee

Mike Buckbee is the founder of Knowatoa, a platform that tracks how brands show up across AI search tools like ChatGPT, Claude, Gemini, and Perplexity. Before Knowatoa, he spent nine years as Director of Demand Generation at a cybersecurity company and consulted for Y Combinator–backed startups, working at the intersection of software development and marketing. Today he helps companies treat AI visibility as a lagging indicator of their broader content strategy.

Connect with Mike on LinkedIn or learn more at Knowatoa.com.

Show Notes

[00:00] Intro
[02:57] How Google search has changed
[05:25] How Mike got into AI search tracking
[08:43] Why AI search is hard to track
[15:01] Marketers versus developers adopting AI
[19:36] HubSpot’s resignation letter and why you can’t out-SEO this
[22:02] What producers should actually do now
[25:37] Content planning and building leads from AI search
[29:45] What AI search reveals about your brand
[33:57] System prompts, ads, and the future of AI trust
[40:41] What Knowatoa is trying to do
[45:29] Free content and the page count arms race
[49:41] How Knowatoa surfaces citation opportunities

About Data Beats Opinion

Try SegMetrics Free

Start your 14-day free trial today.

Data beats opinion. Growth beats guesswork. 🚀

Transcript: Mike Buckbee – Ep 15

So ChatGPT just recommended three of your competitors and didn’t mention you at all. Google just answered your customer’s question before they even clicked a link. And somewhere out there, a six-year-old Reddit comment is quietly shaping what AI thinks about your business. This is the new reality of search.

And most marketers have no idea how quickly the landscape is changing or how they even stack up. In this episode of Data Beats Opinion, we’re talking with Mike Buckbee, founder of Knowatoa, one of a new wave of tools built specifically to track how your brand shows up across AI search engines. We’re going to get into why SEO, as you’ve known it, is actually a lagging indicator of everything else that you’re doing wrong.

We’re going to talk about what it actually takes to show up when buyers are asking ChatGPT and Claude who they should trust. If you’ve been ignoring AI search, this is your wake-up call.

Let’s start the show.

Keith: So Mike,

Mike: Right.

Keith: Super glad to have you on. Um, it is, yeah, it’s very apropos that you’ve come on now because, uh, the world is changing quickly, and you are kind of at the forefront of all of this AI, AEO, AI search, whatever the hell we’re calling it now. And—

Mike: Yeah.

Keith: Let’s do it. Throw down.

Mike: Um, I was gonna say, I think that’s actually an interesting place to start, which is that there is always this debate: is it SEO, is it AEO, is it GEO — all these acronyms. And, um, where we’ve come to is we just call it search now. That is just search marketing, because it’s such a blend across everything, and every bit of energy you spend trying to parse this out into something that’s gonna apply to everything just definitionally changes depending on who you speak with. It’s better just to consider it search, and—

Keith: Yeah.

Mike: I think that helps ground it. Then, as a marketer, I’m just trying to reach people. I’m just trying to answer the questions people are searching for, wherever they’re at, in a reasonable way — my content in a reasonable way. And our hypothesis is: if you do all those things, you end up in the right spots and you get business, and that’s the way to track this stuff.

Keith: Yeah. It really is interesting in a way, because it is kind of like SEO in the old days, where you were still trying to game an algorithm to a degree. But the things we used to do in SEO that stopped working because of the algorithm are now great for AI, right? And I think a lot of people are worried because they do feel it is something completely new. And while it has a lot of challenges, I think, like you’re saying, the overall strategy is really the same, which is to provide really good content that people — or in this case, AI — want to show to people who ask about it.

Mike: Yeah, and there’s less now than there was a year ago. A year ago, I think there were a lot more conversations about like, “Do I actually need to pay attention to this? Is this a flash in the pan?” If nothing else, we are certainly in a spot where Google has changed a lot of things in the last couple of years — it’s like they’re just ignoring everything else. You’d never know AI existed if you just looked at what Google is doing in terms of how they present search results, what they consider ranking factors, how all of those pieces happen. We’re looking at something where there are fewer — primary links is a nice way to put it—

Keith: Yeah.

Mike: —and there’s a lot more competition for those. I think there’s a lot less emphasis on informational traffic; they’re just sending less of that. And that needs to be a change we make in all of our content plans, our content strategies, and our distribution strategies, to respond to that. And then on top of that, there’s ChatGPT and Claude—

Keith: Right.

Mike: —that are coming in as well, taking, I think, more industry-by-industry chunks out of the overall search volume.

Keith: Yeah, I think so. Amanda at SparkToro just gave a talk at MicroComp this last week — which kind of dates the podcast — but she was talking specifically about two big stats. One was that clicks from a Google search — so Google searches that result in a click to a website — is… wait, what was it?

Searches from Google that don’t result in a click: 84%. I knew I’d get that number eventually. It’s up from 56% two years ago. The other stat she showed was that when you search on Google, the first thing is an ad, the second one is AI or some sort of overview thing, and—

Mike: Yeah.

Keith: Yeah. And then below the fold are the actual results. So now the actual search results from Google are below the fold, which was ostensibly the whole reason for Google, which is—

Mike: Yeah, ten blue links is far, far in the rearview mirror at this point, so.

Keith: Yeah.

Mike: Point being, so.

Keith: Yeah. And so I want to step back one real quick, which is: Mike, how’d you get into this?

Mike: Oh, okay. Um, yeah, so I was director of demand gen at a big cybersecurity company and worked there for, you know, nine years. I’ve done a bunch of other work — worked for YC companies — and it’s always been sort of on the boundary of software development and marketing.

Keith: Mm-hmm.

Mike: Putting together stuff that was very conversion-oriented, doing technical SEO, doing technical SEO content — you know, it’s hard to write about cybersecurity if you’re not a subject matter expert; you come off sounding real weird. So things like that. And, um, very much in the era of the skyscraper technique — very much in the era of “we should just write longer and more comprehensive articles about high-level topics that may be low intent.” We’ll get people to our site with these, we’ll then have some sort of CTA or sweetener, like, “Hey, you want a white paper on this topic? Hey, you want a webinar on this topic?” We’d get them into the funnel that way, and eventually turn them into leads, and a salesperson would pick those up. Very B2B.

Keith: Mm-hmm.

Mike: And that is a strategy we’re seeing become less and less effective now. And, you know, through my own interest in AI as a developer, and looking at what was happening, I was drawn to all these changes and thought there was a lot of opportunity there. So, for myself and a few of our friends, I’d written a script that hit ChatGPT’s API and just returned results about my company and their companies as a CSV.

I passed that out a couple of times, and then a couple weeks later, a couple of the people running essentially post-signup surveys — “Hey, how’d you hear about us?” — were like, “People keep saying they found us on ChatGPT. Can I get an update of that CSV, Mike?” So I’m like, alright, maybe there’s a business here. So that eventually led to having a full, professional SaaS product for this called Knowatoa, which tracks these results across the different services and helps you with your questions. And now it goes so far as to really treat search as a lagging indicator of the rest of your marketing.

That’s a central organizing principle we have, which is: if you’re doing good social media, if you’re doing good content, if you’re doing good distribution, all of that will result in showing up in search—

Keith: And—

Mike: —and that’s where we’re at today.

Keith: I think it’s interesting, because you touched on something there. Let’s refer to it as “search,” because I think that’s really what it is — that’s the intent behind it. We used to call it Google search, or organic search, or whatever; we always just called it search, but it’s the same thing. What’s interesting, though, is that because Google — and I guess DuckDuckGo and other search engines, but mainly Google — had a monopoly on what search was, it was really based on your website, and optimizing your website was the only way to get visible in search.

But AI is not limited to the website, right? Because it’s crawling you. You mentioned social — so now Reddit is a huge influx of data for AI. It crawls Twitter, it crawls all these systems. So it’s interesting, and I’d love your opinion on this: it feels like you can no longer just be on your website, right? It’s not just talking about the stuff you want to talk about on the website — you also have to be on Reddit, you have to be on these social platforms, you have to be everywhere in order for the AI to pick you up better. Or do you disagree with that?

Mike: Um, I think, you know, we have to start with a definition, so we agree on what we’re—

Keith: No.

Mike: —talking about. So much of search is treated as a monolith, where it’s all treated the same. And I think if we’re talking about essentially branded search — people asking direct questions about my company—

Keith: Mm-hmm.

Mike: —a lot of that is still your site. Some of it is on the edges, but for the most part that is your site. If you’re really trying to do stuff where you’re trying to find new people — people who haven’t heard of you, who are maybe problem-aware but not yet aware that you’re the solution to their problem—

Keith: Right.

Mike: —then it’s a different ballgame. And in that respect, I do think distribution matters a lot more. We do see some of these social sites disproportionately represented in the results. The one you didn’t mention was LinkedIn. I’d mention LinkedIn because we see it show up in a lot of results — so there’s actual data about this. But also, there’s a lot of press releases that let you read the tea leaves of where stuff is coming from. OpenAI has this multibillion-dollar, weird relationship with Microsoft. Part of that is they get training data off of LinkedIn, which is owned by Microsoft. A lot of people forget that. And GitHub, which a lot of people—

Keith: Also owned by Microsoft. Yeah.

Mike: —and, you know, so that’s a huge part of it. Those are very easy places for it to just pick up on. And there’s a tension to this, which is that what you think of as a search campaign is not usually what you post to your LinkedIn, is not usually what you post to your Twitter. So I think we’re seeing new patterns come out of this, and new strategies.

Keith: Yeah. And it is such a deep thing. So a lot of this, though, is a problem — you mentioned that when you were doing the CSV thing, people were saying on their feedback forms that they found you through ChatGPT, right? We actually introduced AI search tracking in SegMetrics, which I will say is kind of hit or miss, because a lot of AI results do have links in them that people will click and go to, but that is very rare. What we’re seeing a lot of, I think, is this removal of the ability to see where people came from in any sort of data-analytics-tracking way, because people are getting all of their information through a chatbot that isn’t tracked, that has nothing you can glom onto.

They’re doing all their research — you have no input on that research other than what you’re able to feed into the AI organically and track through Knowatoa. And then people come to a conclusion, and they open up six or seven tabs with the solutions they’ve found, they look at them a little bit, and then they go directly to the site, and they make a decision, right? And that’s — yeah.

Mike: Yeah, I think it might be interesting to talk about the mechanics of why some of the tracking just isn’t there.

Keith: Yeah.

Mike: So some of that is that a lot of times I think people equate search result links with AI citations, and the citations are more — footnotes is maybe a better way to think of them. They’re there for reference, but you’re not really expecting to click on them. So even showing up in a citation isn’t necessarily a sign that you’ve done a good marketing activity. It’s a sign, maybe, that you’re on the right track — it’s a positive, you’d rather be there than not — but in and of itself, getting a citation isn’t the end result we’re trying for.

Keith: Mm-hmm.

Mike: A couple things there. One, especially for very informational things — like, “What’s the average length of a giraffe’s neck?” — there’s not a good follow-on query for that.

Keith: Right.

Mike: There’s not a lot of follow-on to that. And there could be a hundred citations for that. Something we find is that even Google jumps around — sometimes they have ten citations, sometimes they have thirty.

Keith: Right.

Mike: So there’s a volatility to this that doesn’t even speak to the end result. And a lot of times, if you’re using something like ChatGPT or Claude, it’ll give you branded results separate from links. That was actually one of the first things we did — we separated it out so that, for a single term like SegMetrics, we can track segmetrics.com, SegMetrics—

Keith: Right.

Mike: —you know, seg space metrics, in case someone typed it in weird somewhere. All of that can be one entity, SegMetrics, that we track, and go, “Oh, you showed up in these places.” And a lot of that is — there’s friction to get from—

Keith: Right.

Mike: —the research phase to the action phase in a search journey, and so much of the search journey is now done for you by the AIs, or it’s just summarized into one thing, so you don’t need to open up five sites — like an AI overview does. So.

Keith: Yeah, I was looking — we were talking a little bit before we started about Aaron Francis’s new tool, and I was looking for a good, quick code editor that isn’t heavy. And I was like, “Claude, what’s out there in 2026? What have we got?” And it listed up seven or eight. I said, “Okay, what’s good and bad about all of these? I’m on a Mac,” blah, blah, blah. And it listed three or four. I said, “Okay,” and I just put them into Google, went to the first link, which was the thing, and boom, boom, boom — “Okay, this one looks good, download it.” I said, “This is crap,” uninstalled it, installed the second one, and said, “Yes, this works great.”

But at no point was I going through their documentation, or doing what I used to do on a website to show that I was interested in solving this problem. I don’t know where I’m going with that, but it’s just a whole different way of doing this.

Mike: My assumption is most of the people listening to this are pure marketers — not marketer-developers like us. I don’t know if that’s accurate, but, you know, most.

Keith: I’d say ninety-ten. Yeah.

Mike: Yeah, something. And something I’ve found is that, in a lot of ways, I think the software development world — and all the Claude Codes and all the massive changes that have happened in the software world — haven’t happened elsewhere yet. It feels like a very early-adopter phase to me. It just so happened that code was very tractable for all the AI tools, so it’s taken off there, and now, I think, the vast majority of developers are using AI code in some way — even if it’s just sprinkles of it. And we’ve seen a massive drop in the amount of search volume for programming and technical terms, which used to be huge.

Keith: Oh, Stack Overflow is dead. Just — yeah.

Mike: Well, Stack Overflow’s dead, but, you know, we have friends who had React blogs, who had businesses built around these things. There’s a company called Tailwind, which has front-end web components, that’s had a really interesting SEO journey, where people would search Google for documentation on how to use Tailwind, land on their doc site for some things, and there’d be a CTA there — “Hey, get the Pro version of this and we’ll give you these components.” And they’ve had a huge cratering of their traffic and their revenue. And that’s just something that’s happening in our particular niche, so I think we’re much more sensitive to it, but I do think it’s the canary in the coal mine for a lot of other places.

Keith: I think — yeah, I mean, I will push, not really push back, but, you know, I live and breathe in the marketing world, and the amount of adoption for agentic marketing is huge. People are either fully into it or they want to be fully into it — there’s just a lot of excitement. I think it started a little later than with developers, because, like you said, code is easy for AI to understand.

But what I think is interesting is I don’t know how much of the marketing side was based around what you’d consider SEO — traditional, like, “Hey, you have to go find this thing and then opt in and do whatever.” And I could be wrong on this, but I always feel like it’s much more community- and personality-based, right? It’s much more about—

Mike: Um—

Keith: —building an audience and growing through that, and joining other people’s audiences to build your own audience, and that kind of stuff — which is much more than the SaaS or dev-SEO focus. I could be wrong on that, but it’s kind of the feeling I get.

Mike: I think it’s very industry-specific. And I know—

Keith: I—

Mike: —where you’re coming from. I think, from your background, where you worked with people who were in a lot of course stuff and a lot of marketing, selling stuff — that was not the enterprise B2B SaaS world, which in a lot of ways — we had style guides that were basically trying to knock the personality out of anything we wrote, de-emphasizing the author, trying to do stuff like that. An interesting example is HubSpot, who I think — they—

Keith: We will have a talk about that in a second, but yes, go ahead.

Mike: Well, I guess HubSpot used to rank really well for the term “resignation letter.” They’re not a resignation-letter company, but it was like a vaguely professional term, and they’d rank number one and get a lot of traffic for it.

People would land on their site, and they’d be — and the search intent was, “I just want a resignation letter template so I can quit my job at McDonald’s,” or wherever—

Keith: Right.

Mike: —you know. And from an SEO standpoint, they’d have this hyper-optimized page: “What is a resignation letter? Who needs one? Some thoughts on a resignation letter from a recruiter” — because it has to have E-E-A-T for a resignation letter, which is its own whole weirdness. And then you’d click on the page, and it would take you to a form, which would then try to do a progressive input of all your data, and then finally, at the end of it, it would send you the letter. And now there’s an AI overview that just throws you right into a Google Doc, that—

Keith: Mm-hmm.

Mike: —you know. I bring this up mostly from the perspective that you can’t out-SEO these changes. It’s also, I think, good in a lot of ways — the user experience, the search intent, the search experience is much better if I type into Google and get a—

Keith: It is.

Mike: —than the super-optimized, weird SEO games we used to play.

Keith: Yeah.

Mike: I do think this is both an opportunity and a challenge for a content person, to work through these things.

Keith: Yeah, I think — for the end user, it is a better experience, to be honest. I like being able to just not go through all these sites — like you said, recipes are a great one; read eight hundred words of backstory before I get the recipe, right? I don’t want to have to opt in to get some of this. So from my perspective as a consumer, it’s great, but you’re exactly right — what do we use, what do we do as producers of all this stuff? From the tracking side, it reminds me a little bit of when Google took keywords away.

For people who don’t remember: when someone clicked on a Google link, it had the keywords they searched for in that link. So on your website, you could track exactly what keywords people used to find your site, and they took that away because they wanted to sell more ads. That was horrible — when that happened, we were all like, “Dear Lord, what are we gonna do? We don’t have any information anymore.” We’ll get used to this as well, but it’s still like — what do we even do with our marketing sites at that point, right? How do we do lead gen? How do we get people excited?

Mike: Well, I mean, this is a bit of a tangent, but every quarter Google releases something that says, “Hey, we’ve never had more quality queries on Google than we did last quarter — it was great.” And I always think, wow, there’s a lot of nuance in that — what’s considered quality, what do they consider bot traffic, what do they consider — there’s a lot of knobs they can turn.

Keith: Yeah. And who’s judging the quality? Quality for who — for the end user, for the end consumer, for Google?

Mike: Yeah. And so when you start to think about all the clicks and—

Keith: I—

Mike: —all of the traffic, the vast majority of it is navigational.

Keith: Mm-hmm.

Mike: You know, a lot of it is non-follow-up queries — like, “Giraffe, what’s the length of a giraffe’s neck?” Goofy stuff like that. The one that always gets me is people looking for login pages — all this stuff. And when you get down to it, there’s really only about ten, fifteen percent, depending on how you want to count it, of queries that are actually looking for new information, looking for solutions, looking for the answer to problems. And I do think you want to be in those conversations. So that’s really where I put a lot of the emphasis in strategy when I’m talking to people — you need to really think about that “hot” section. What are we going to do for this, versus traffic numbers, versus everything else — where I think the cards are stacked against you in those other categories.

Keith: Yeah.

Mike: Yeah.

Keith: I think there was always — one of the people I worked with phrased it really well. He said you always want to find what is the 2 a.m. question that people are asking about your services or your products — not the version you know as an expert, not the mental version, but “dear God, I woke up at 2 a.m.” He was in the health space, so it was like, “My stomach hurts, what is going on? What is the query you put into Google? What is the search you put in?” And that’s what you optimize for in SEO — that’s what you build the page for, that’s what you build the opt-in for and stuff. And I think those questions still stand — those are still the things you want to optimize for — but the end result of what you do with that is now different. Would you agree?

Mike: Yeah. So, in terms of our own content planning, or when we’re consulting, it’s become a lot more solutions-oriented — it’s almost one step up from the keyword or the actual question, into that buyer’s journey. It starts with, “What is the thing? Oh, this is how it’s actually defined. Here are the other alternatives for it. Okay, what are the problems with these? Okay, now that I know the problems, these don’t apply to me — so give me the filtered list.” And you want to make sure you’re in that.

So if you’re too precise in what you’re trying to do — “We are a solution for this one thing, for these people searching for this” — what you really need to do is make sure that anyone who’s searching for, say, digital marketing, needs to find us, Knowatoa. It’s not just SEOs, it’s not just people doing this, because there are a lot of different aspects of this that now feed into it.

Keith: Mm-hmm.

Mike: And that’s something we’re trying to do — if you’re looking for, “Hey, how do I increase the leads on my site?” — there are a lot of pieces to that. So we want to make sure that, as a general solution, we can be part of that conversation.

Keith: What do you see — I’m just trying to figure out — how do you see people building leads from AI in that case? Like, what’s the offer? I know how to do it on social, I know how to do it with the old SEO strategy, but when AI is answering all the things, what are you able to provide, seeing that maybe they can’t even get to your site? What’s the — I guess, how do you see that evolving now?

Mike: I think there’s a need to establish — a lot more of the guides, and use cases, and solutions — this is very much my SaaS brain coming out—

Keith: Mm-hmm.

Mike: —that’s becoming, I think, much more vital, where — at some level, all the—

Keith: Yeah.

Mike: —they’re just doing pattern recognition, like, “Hey, people asked for this, and this was the next thing that came out.” So really doing a good job of listing all of that — how it works, how it fits in — to get to that higher tier in the search journey, I think, makes a lot of sense.

One of the challenges we face as a company trying to help with this is that a lot of times it’s something very specific to that business, or that industry, or niche. So it’s not an easy thing, like, “Oh yeah, do this LLM-text thing, do this other little tweak.” It still takes a human who understands the industry, who can use these tools in the right way, and craft a narrative — craft a—

Keith: Yep.

Mike: —story of some kind. So, I’m trying not to talk about confidential clients, but there is stuff that fits very cleanly into that, where there are a lot of pieces that didn’t make as much sense before. That’s maybe more — I think we’re talking middle-of-funnel content.

Keith: Yeah.

Mike: That’s not definitional — it’s much more “how do I go from A to B” than it is “what’s an A,” you know—

Keith: Right.

Mike: —generic sort of thinking about it. So certainly I think that’s a hotbed of non-trackable content that makes a lot of sense. And then I think there’s a lot of very much bottom-of-the-funnel stuff, where we get into all the weird “best of” listicles and everything else. But there’s, I think, a spot below that — even below—

Keith: Below the bottom of the funnel.

Mike: Well, “bottom of the funnel” was a marketing term—

Keith: Mm-hmm.

Mike: —and something we don’t think about with search is that there’s a lot of, especially in B2B enterprise, internal sales documentation—

Keith: Yeah.

Mike: —battle cards, like, “How do we stack up against this other company? What do we do in these very precise circumstances?” And a lot of that never ended up on the website — because why would it, you know—

Keith: Yeah.

Mike: —it’s almost too much to have someone wade through all of it for this. We’re definitely seeing a change in the buyer’s journey, where not having that information can disqualify you from a sales perspective before marketing even takes place, which is—

Keith: Right.

Mike: —something to think about. But something that comes up a lot in the B2B space is regulations — like, “Oh, we’re a healthcare organization, we need a HIPAA-compliant email newsletter solution. Give me a list of those.”

Keith: Alright.

Mike: And if you don’t have that cleanly stated on your website, you’re not even going to make the list — you’re not even going to have any journey. And there are a lot of different aspects to that. “Do you do business in California?” You’re like, “Oh, well yeah, of course we do, we’re a SaaS.” Do you say that? Is that something we account for? I think those things are changing too. And the other piece is there’s a real opinion/perception aspect to this — I think people put too much personality into it, like, “Oh, ChatGPT likes my company,” or “ChatGPT doesn’t like my company.” But really it’s the collective opinion of everyone on the internet about your company, and—

Keith: That’s so much worse, like—

Mike: —which gets reflected in the results. And what I bring up is Wells Fargo. Wells Fargo, you know — biggest FTC fine of all time, all sorts of other stuff, where they were opening credit cards without people’s knowledge—

Keith: Yeah.

Mike: —for their bonuses. Not great stuff. If you do a Google search, it comes up with, “Oh yeah, where’s the nearest Wells Fargo ATM, where’s the nearest branch?” But if you ask, “Hey, ChatGPT, should I use my local credit union, or should I use Wells Fargo?” it’s like, “Well, maybe do an ethical survey of Wells Fargo before you work with them.” You’re like, whoa, what is this?

Keith: Yeah.

Mike: When we think about how the AI search results come about—

Keith: Okay.

Mike: —what’s happening is: you type a question into ChatGPT, and ChatGPT has a system prompt that changes oftentimes — like, “Oh, they’ve decided that Sam Altman wants to de-emphasize certain results or opinions, or—”

Keith: We really like Wells Fargo now.

Mike: “We really like Wells Fargo now.” Or, “Hey, anything that’s about these areas, we’re just going to say that’s controversial.” Or a lot of times it’s actually IP protections — like, “Hey, anything from an artist from the last hundred years, don’t make a new image based on—”

Keith: Right.

Mike: —that kind of thing. That’s in the system prompt. So there are things in there, such as, “Hey, don’t give financial advice.” Does that mean don’t include Wells Fargo? It doesn’t say that. So that’s one layer of it. Then there’s the actual model, and the model has all this training data, and maybe it’s picked up new things, and maybe it’s different than what it was before. And then occasionally it does a tool call — which is a technical term for AI stuff — that goes out to an actual web search index and brings up a whole bunch of results, and then it filters that through its perception of your company and your competitors, and it finds a hundred results and gives you five back. So you’re fighting through all the layers of that, below the bottom of the funnel.

Keith: Right.

Keith: And it’s interesting, because you talked about the system prompt and how that can change. And Anthropic, who has Claude, publishes their system prompt, so you can see what changes they’ve made — all that stuff. I don’t believe OpenAI does that — ironically, given they have “open” in the name—

Mike: They leak all the time.

Keith: They get leaked as well, yeah. But one of the things — OpenAI is now playing with the idea of ads in their AI, and I think they’re actually in a beta trial, or alpha trial, I can’t remember right now. I worry — I knew it was going to happen; they’ve been talking about it for a while, and we’ll start tracking it when they release the API. But the interesting thing there is I think having ads in there erodes trust in the advice, just like Google eroded trust in search results because there are so many ads. Whether or not an ad is actually showing up in the results and labeled as an ad, do we know they’re not influencing that on the backend?

And, you know, they used to do those — I think even in Idiocracy they had it, and there are all those cartoons where it’s like you’re talking to a friend and he’s wearing the Pepsi jacket and the Pepsi hat and everything, and it’s like, “You should drink Pepsi.” It kind of turns into that, because we have this kind of human-like, parasocial relationship with these chatbots to a degree, and we’re expecting them to be semi-objective. The idea that once there are ads in there — do we then trust that objectivity at all? Are they essentially ruining the whole thing at that point? That’s a huge, fascinating thing for me — our perception of it once ads enter the mix.

Mike: Yeah, I mean, I think we’re getting into really speculative territory. The thing I think would be amazing is if you asked how long a giraffe’s neck was and it said, “It’s fifty-seven—”

Keith: Pepsi cans.

Mike: “—Pepsi cans,” you know, that it just blends it in—

Keith: I would love that. That would be great.

Mike: Yeah, I’m gonna go patent that idea before you publish this, but—

Keith: All measurements are in Pepsi cans.

Mike: Well, I mean, that’s the kind of weird stuff that could happen — just slowly. Certainly I think there’s an erosion of trust with this, and certainly there are some companies that are a lot more open and predictable about what they’re doing with it. I do think Anthropic has been a lot more forward about the AI governance side — how they train, how they deal with copyright, how they’re trying to balance being respectful of that with also having actual training data.

Keith: Mm-hmm.

Mike: And it’s an interesting situation, so — yeah.

Keith: Yeah.

Mike: But absolutely we’re going to see ads in all sorts of things, and it’s a cycle — like it used to be in the news, you know, the old-timey newscasters would go, “Alright, let’s take a break — let me tell you about the—”

Keith: The core flesh.

Mike: “—of cigarettes. Nine out of ten doctors recommend these.” So—

Keith: Yep.

Mike: —there are always these interesting bounds around trust. But if we’re trying to really think about the future — how familiar are you with Character.AI?

Keith: Character — oh, the ones where you create an avatar and all that stuff. Yeah, yeah, we’ve done a lot of tests into that.

Mike: So do you recall how much Google paid for them?

Keith: No, no.

Mike: It was a billion dollars.

Keith: Holy — more. Alright, alright.

Mike: So, let’s just throw that out there — why would Google pay a billion dollars for something if not because it’s going to be a core part of this stuff in the future? And I do think that is the future. The reason I’m bringing it up now, since you’re asking about the erosion of trust — I think right now we still have a very arm’s-length relationship with AI, both as a society and in my own use. We’re not yet quite to the point of the sci-fi companion that’s always around — even just as, “I’m your marketing companion.” There are arguments for and against that.

We’ve actually named our agents in the application — our content-writing agent is named Connie, and part of that is to try to convey the value and make it easier for people to say, “Oh yeah, I’m giving direction to Connie about the tone of voice I want for my articles. I’m giving direction to Sam about the SEO tasks I want done.” And that’s different from, “The script that’s running, and I sort of vaguely do something with it.”

Keith: Right.

Mike: And I think there’s going to be a lot of the future in that — this is a younger-generation thing, but the generation younger than us has really taken to Character.AI and systems like that, where it’s an interesting mix of role-playing, like acting, and emotional support from a — perception-wise — objective source.

Keith: Like a therapist. Yeah.

Mike: Well, at the very least, if you’re a kid, you can speak to this AI thing, and it’s not going to be all over the—

Keith: Yeah.

Mike: —you know—

Keith: Yeah.

Mike: —or the cafeteria at school the next day. But whatever it is, there’s a lot of branding that happens in that — like, you could talk to the cast of Stranger Things. You’d go in and say, “I—”

Keith: Oh—

Mike: “—want to talk to Steve,” and Steve talks to you like, “How’s it going? You bought a Pepsi lately? They’re great. Back in the eighties.” So, um, but I think those things are coming — the interface is a lot more chosen for you in general, a lot more chat- and voice-driven, and a lot more personality-based. And a lot of the things we have now are going to be separate from that. How Knowatoa is trying to help people prepare for this — it’s kind of in our name — is really about knowledge. Make sure the right knowledge is out there about your company, make sure the right facts are out there, and then distribute them in a smart way. That’s just going to be the underlying context for whatever form this takes in the future.

And as a bonus, I think a lot of the marketing stuff that’s kind of formless — a lot of social media stuff, where people ask, “Oh, should we be bratty, like the Wendy’s Twitter account, or should we do something else?” — no, try to be helpful. Use it as a distribution platform for your content, use it as a springboard for those kinds of things. It’s not these random acts of marketing — it’s directed, it’s based on search, it’s based on real data about what people are trying to find out about you—

Keith: Hmm.

Mike: —and I think it’s a good short-term way of hedging your bets, to set up a bigger long-term return.

Keith: Yeah, definitely. I think the big thing about marketing has always been that people relate to people, not brands, right? Going back to the Wendy’s thing — Dave Thomas was the face of Wendy’s for so long, and people resonated with that. I think AI is interesting because it has that relationship kind of built in, when you have these agents that are named and have faces — cartoon faces, but still faces — and I’ve found myself doing this weird thing, like, “Hey, I’m just hanging out with Claude for the weekend” — not in a “what are you doing, Claude” kind of way, but I have things I want to work on and talk through, and there’s no one really to talk through these weird thoughts going through my head about mapping software and all this stuff, right? And I just want to talk through it. It was a little interesting, and a little bit scary at the same time — like, “What are you doing, Keith?” “Oh, I’m just chatting with Claude about what I’m building next.” So—

Mike: There’s something in software development called rubber duck debugging—

Keith: Yeah, rubber ducking. Yeah.

Mike: —so you get a rubber duck, put it on your desk, and talk to it. And of course, when you actually say the words out loud, you go, “Oh, of course, that was the thing — I should have thought of that in the first place.” But it wasn’t until part of your brain activated to get the words out of your brain, into your mouth, and into the—

Keith: Right.

Mike: —air that it happened. And that’s a powerful technique. I think we’re putting some of that into the AI, but it’s also reflecting a lot of the pieces of other stuff. There’s something with AI I’m thinking about — I’d be very curious about your take—

Keith: Yeah.

Mike: —I feel like AI tools in general raise the floor of quality—

Keith: Mm-hmm.

Mike: —but they don’t really raise the ceiling of it. By that I mean, you—

Keith: Yeah.

Mike: —scoop up the average — like, we’re talking about content marketing — they scoop up the average of all the different articles out there in the world and all the thoughts about—

Keith: Yeah.

Mike: —and you’ll get, by definition, a pretty average article—

Keith: Yeah.

Mike: —if you just ask ChatGPT about something. If you don’t have anything, maybe that’s better than nothing, but if you actually want to push things, succeed, and outcompete, I do think you need to put more into it.

Keith: Yeah.

Mike: And that’s a lot of how we’re developing this — we do generate articles, but we very explicitly call them drafts. This is something you should take as a first draft—

Keith: Right.

Mike: —you should put your own spin on it, and put in as much of your own voice as possible. It’s almost more that we’re trying to take the drudgery of a lot of the research out of this for you—

Keith: Yeah.

Mike: —going out and finding all the responses, analyzing them, and being like, “Here’s what the average is — how can you—”

Keith: I, yeah—

Mike: —do more.”

Keith: I fully agree. I think the floor has risen substantially, and there’s another part of that, which is the speed of implementation. And again, this gets into what the future’s going to look like — but it is so easy now to put out floor-level or average-level content — video, images, text, all of it — super easy to just pump out on a production line. Before, it used to be about the amount of content — I mean, it still had to be good, but the people who were pushing out a lot of content that was good enough rose to the top, to some degree. To get above that, you have to be better. But there was value in producing a lot of content, and now that content is essentially free — what does that look like? Because all the algorithms reward amount of content to a degree, but I think there’s going to be a huge change when content is essentially dirt cheap, right? All the content you’d want, a billion posts a day, is just a ChatGPT prompt away, right? So how does our attention economy change when generic content is worthless, right?

Mike: Yeah, so I think there are waves to this — some people are into it, some people aren’t, some people are adopting it in different ways. I think we’re currently in a wave where, separate from any quality metric, we see vast differences between companies that come use Knowatoa. Part of our onboarding is, “Hey, give us five of your competitors,” and they put in five of their competitor sites. We see huge differences, just in the number of pages on those websites.

Keith: Yeah.

Mike: Oftentimes by a factor of ten — you know—

Keith: Yeah.

Mike: —“Oh, you have a hundred pages, someone else has fifteen hundred pages.” Right now, I think we’re still in catch-up mode, which is — it’s pretty easy to look at that and think, “Well, there are fourteen hundred pages of some kind I should probably have.” That’s, I think, wave one: everyone getting to a baseline of, “Here’s all the stuff we need to cover, we’ve at least got some form of it that says, here’s our little spin on it, at least there’s something.” And then from there, what differentiates you is point of view — I think that differentiates you as a brand.

And what differentiates you is delivery method—

Keith: Mm-hmm.

Mike: —all of those are part of this. And search, I’d say, is a lagging metric — if you can win at those other things, you put this stuff back and win in search almost as a secondary effect. So.

Keith: Right. Do you think it’s going to be almost like a nuclear arms race — or is that just in the short term, where it’s like, “Well, that site has fifteen thousand pages, I only have a thousand, I need to get another fourteen thousand,” and then, “Oh, that site has fourteen thousand, we’re going up to thirty thousand”? Does it keep ratcheting up, or, like you’re saying, are we in that phase and then we’re going to hit a plateau where everyone’s the same, and we go, “Well, it’s not really doing anything to have fifteen thousand pages at this point”?

Mike: I think there’s — something you’ve said to us many times is “table stakes” — what’s table stakes? I think table stakes is having the 80/20 rule — the 80% of the content that covers the majority of the questions people are asking about your industry or your particular niche. You need to have that just as a baseline. I think from there, you need to have some point of view that elevates the content, and you need better distribution around it.

Keith: Hmm.

Mike: Which is — again — make more videos, more social, other pieces of that. And, you know, those are things we’re trying to help with too. Even when we say “YouTube,” or “video,” most people take that as YouTube, which is fair, but that could even be YouTube comments — it doesn’t have to be, “I’m going to go produce a thousand videos.”

Keith: Right.

Mike: You can do a comment on it. But surfacing all of that is tough, so that’s something we’re helping with — we have pages inside for, “Oh, these are the things that are cited.” That’s less like, “Make this thing,” and more like, “Start interacting with this channel, start building your own stuff along those lines.”

Keith: And then — you were showing me some of this stuff in Knowatoa a couple days ago, I think, and that’s what got me really excited about a lot of this — not just, “How am I showing up in these AI search engines,” but, “What the heck should I do about it, right? And where are those other places I need to be focusing that I might not have thought of, because I’m not on Reddit, I’m not on social, I’m not on blah blah blah,” right?

Mike: Yeah. So right now we’re trying to draw that out better. We have special citation views for Reddit and YouTube, and where we’re developing further is reviews — because reviews are very big for some of our customers. We’ve had a few people find that it makes a big difference. There’s the apocryphal sort of thing — like, “Oh yeah, there was one weird Reddit comment from six years ago that tanked my ChatGPT,” or, “There’s one weird Yelp review that was actually a duplicate” — one of our customers found it, and it was negative, but they were able to tell their Yelp rep, “Hey, we found this thing, can you get rid of it? Because it’s actually a duplicate.” And they were able to get rid of it, so that was a big win for them. So the more we can pull up those opportunities, and the more we can help frame the content that comes out, and the distribution of it — I keep repeating that—

Keith: Room.

Mike: —that that’s what matters. So — repeating.

Keith: It matters at the end because it’s what you said — it’s not what ChatGPT thinks about you, it’s not what the AI thinks about you, it’s what the internet as a whole is thinking about you. And when ChatGPT doesn’t like you, that makes it feel so much worse, somehow.

Awesome, Mike, thank you so much for talking through this and going through this. What should — I mean, what should people know? Where can they find you on the internet?

Mike: I’m on LinkedIn — you can hit me up there. I’m on Bluesky, but most of our stuff we do now is through our main channels. And if there’s an easy thing you want to try, it’s: go to Knowatoa, put in your site. We have a free action report that gives you a couple of questions, shows how you stand on them, and a couple of ideas based on those for some quick promotion stuff you can do. And then if you want to take it further, you can put in your credit card and do a free trial. But you can get that fun little quick report—

Keith: Hey.

Mike: —so.

Keith: Even the free report was super valuable. Highly recommend it for anyone who’s dipping their toes into AEO, or is worried about AEO — or, honestly, any of the… you shouldn’t be worried about it, but you should be very conscious of what’s going on here, because this is where it’s all going.

Mike: Well, I think the other thing is — whether you try us, try Knowatoa, or not — I do think it makes a lot of sense to start taking these actions now.

Keith: Yeah, yeah.

Mike: So much of what we’re seeing is that — there was even a report that the majority of the sites Google has in their top ten are from sites fifteen years or older, from domains registered more—

Keith: Right.

Mike: —years ago. And part of this is just sticking around. So the sooner you can do that, you’re building — you’re laying down the bricks for the wall. It’s going to be the foundation, as I mix my metaphors, for the next decade of—

Keith: Best time to plant a tree is on the moon, kind of thing. Yeah.

Mike: Exactly.

Keith: Well, Mike, thank you so much for joining us. We’ll have those links to Knowatoa and to the lead magnet, the opt-in test thing, in the—

Mike: It is—

Keith: —in the show notes.

Mike: It’s, it’s—

Keith: Oh, it’s just on the homepage. All right, even better. So check out Knowatoa.com, and we’ll go from there.

Mike: All—

Keith: Mike, thank you so much, man.

Mike: Bye.

Keith: Cheers.

Cross-device attribution: How to track customers across phones, tablets, and computers

Keith Perhac — Tue, 22 Sep 2026 09:00:00 GMT

Cross-device attribution measures how marketing touchpoints on different devices contribute to a conversion. It depends on cross-device tracking, which identifies which phone, tablet, and computer sessions belong to the same person or account.

Tracking connects the journey. Attribution assigns credit within it.

This article breaks down how this cross-device tracking and attribution works (and how it influences your marketing).

What is cross-device attribution?

Almost nobody lives on a single device anymore. Someone sees your ad on their phone during lunch, downloads your lead magnet on the same phone, reads a nurture email on their tablet that evening, and finally signs up for your webinar and buys on their laptop a week later. One person. One journey. Three devices.

Making sense of that involves two jobs that often get blurred together:

Cross-device tracking: This is the identity layer. It sorts out who did what and when. And most importantly, cross-device tracking establishes which sessions actually belong to the same person. When disparate sessions are found to belong to the same person, those profiles are combined into a single user profile.
Cross-device attribution is the measurement layer. This uses the data compiled by cross-device tracking to answer “which of those connected cross-device touches earned the conversion?” Any attribution windows and models you’re using will also impact which touchpoints receive credit for the conversion.

The order of operations matters here. Your attribution is only ever as good as the tracking underneath it. Without identity resolution, the customer who actually bought looks like they appeared out of nowhere. This is a problem because no attribution model can credit connected touches it can’t even see.

Why device-switching breaks your tracking

Don’t listen to the hype: Cookies are not going away. Google reversed its plan to get rid of cookies, and they’re still on by default in Chrome. (Here’s what digital marketers need to know about third-party cookies.)

Even so, you can’t rely on cookies to deliver clear and complete tracking.

For one, other browsers like Safari block third-party cookies by default.

But the deeper problem is that a cookie lives in one browser on one device. That means a cookie on your phone cannot be read on your laptop. So even in a world where cookies work perfectly, the moment someone switches devices, your tracking sees a brand new stranger with no past.

Without cross-device tracking, you may not have complete customer journeys. This means you can’t reliably trace purchases back to the first touch that brought them in. Nor can you trace first touches all the way to purchases. You just have a muddled mess of touchpoints with no idea if they drive revenue or not.

It doesn’t matter how long your attribution window is or which attribution model you select, without cross-device tracking, your attribution remains incomplete. Your analytics will be biased towards journeys that happen to be observable within the same browser, device or identity context. If you can’t see the customer journeys, no attribution model or window can save you.

Cross-device vs. cross-channel vs. cross-platform

These three terms sometimes get used interchangeably, but they’re distinct issues:

Cross-device: The same person on different hardware (e.g., phone, tablet, laptop, desktop)
Cross-channel: The same person across different marketing channels (e.g., email, paid ads, organic search, etc.)
Cross-platform: The same person across different marketing tools and ecosystems (e.g., Meta Ad Manager, ActiveCampaign, HubSpot, Stripe, etc.)

You can think of all three as different axes of the same “fragmented tracking” issue. Effective marketing attribution has to solve all three to provide transparent and complete customer journeys.

How cross-device tracking works

Identity resolution — how an attribution tool answers the “is this the same person?” question — can match separate sessions in two ways:

Deterministic matching

Deterministic matching uses a known identifier. Most of the time, this is an authenticated user ID, email address, or login. If a person logs in on both a phone and a laptop, an attribution tool can associate those sessions with the same account (provided the login and identity data are captured and passed correctly). This is generally a high-confidence, auditable approach.

It’s also why connecting your email platform and CRM to your attribution tool matters. Those systems can provide shared identifiers and conversion records that help thread anonymous sessions, known contacts, accounts, and purchases together.

However, auditable does not mean infallible. An email address is not automatically a definitive identifier. Shared household addresses, role-based addresses (like info@company.com), aliases, recycled addresses, and data-entry errors can create false matches. High-quality identity resolution therefore uses more than email addresses and documents how records are matched.

Probabilistic matching

Probabilistic matching estimates whether sessions or records belong to the same person when a reliable shared identifier is unavailable or incomplete. It uses statistical models to evaluate a combination of signals — such as browser and device characteristics, IP address, timing, geography, cookies, click paths, and behavioral patterns — and estimates the likelihood that sessions or records belong to the same person.

At SegMetrics, we call our proprietary implementation Fingerprint Tracking. Our system uses nine different dimensions — email address, browser, IP address, platform integrations, custom fields, cross-domain click tracking, cookies, screen size, and geolocation — to build unified user profiles when the signals align.

The end goal is contact journeys that follow one person, click by click, across every device and marketing platform, from first touch to purchase. For example, here is the step-by-step customer journey for a user in SegMetrics (demo account):

One caveat on customer journeys if you sell to teams: The “same person” isn’t always “same buying unit,” so check whether your tool links a user’s identity to just one person or also to a whole group or account.

In summary, here’s how the two methods compare:

	Deterministic matching	Probabilistic matching
What it uses	A login, user ID, or email	Browser, device, IP address, timing, behavior
Confidence	High and auditable	An estimated likelihood
Weak spot	Needs a captured identifier	Never certain

What about server-side tracking?

Everything above assumes the touchpoints were actually recorded by one or more of your marketing tools. But that isn’t guaranteed, especially when ad blockers and privacy settings keep interfering with what and how much can be seen.

The solution is to stop relying solely on tracking that runs in the visitor’s browser. Server-side tracking moves some of the tracking from the user’s browser to your own server. This way your tracking data is collected on your own domain, first-party, reducing losses caused by client-side blockers and browser restrictions. (Server-side tracking does not eliminate consent requirements or guarantee that every touchpoint will be captured.)

Reliable server-side tracking also gives your tracking layer more complete data to work from. This improves the accuracy of your fingerprint tracking (and with it, your cross-device, cross-channel, and cross-platform tracking).

Signs you have a cross-device tracking gap

The following are signs that your cross-device (and cross-platform) tracking has gaps which may be hurting the accuracy of your analytics.

No single source of truth for your attribution and analytics. This is a major red flag. It’s very difficult to close cross-device tracking gaps without an attribution tool that links disparate sessions into cohesive, transparent customer journeys.
Bloated “direct” or “unattributed” traffic. When your tracking loses the thread, it can dump visits into “direct”, the analytics equivalent of a shrug. Direct can also be legitimate traffic from people typing in your URL or using bookmarks. (Fingerprint tracking can help connect anonymous sessions to known visitors, but it cannot recover source information that was never captured.)
Customer journeys that seem to start at the purchase. If you have customer histories that begin with buying, their earlier sessions may have been logged as anonymous (even if it happened minutes before buying), their earlier activity was not captured, or that activity happened outside the attribution window.
Major pieces of your marketing are missing from your analytics. If major marketing channels never or rarely show up in your analytics, it could be because they aren’t performing. But it could also signal a gap in your cross-device tracking.

If one or two of these sound familiar, a real chunk of your funnel is invisible. That means you’re making strategic and budget decisions on incomplete (and potentially incorrect) data.

Frequently asked questions

What is cross-device attribution? Cross-device attribution measures how marketing touchpoints on different devices contribute to a conversion. For example, the phone where someone first saw your ad and the laptop where they bought. It depends on cross-device tracking, which identifies which phone, tablet, and computer sessions belong to the same person or account. Tracking connects the journey. Attribution assigns credit within it.

What is cross-device tracking? Cross-device tracking is the identity layer underneath cross-device attribution. It works out which phone, tablet, and computer sessions belong to the same person, then combines them into a single profile and one customer journey. It matches sessions deterministically (through a known login or email) or probabilistically (through signals like browser, IP address, and behavior).

Why does cross-device tracking break? Because a cookie lives in one browser on one device, it can’t track a user across multiple devices. A cookie set on your phone can’t be read on your laptop. So when someone switches devices, standard tracking sees a new stranger. Privacy defaults widen the gap.

What is the difference between deterministic and probabilistic matching? Deterministic matching links sessions through a known identifier, such as a login, user ID, or email address. It is high-confidence and auditable, but only works when that identifier is captured. Probabilistic matching estimates whether sessions belong to the same person from signals such as browser, device, IP address, timing, and behavior. It fills the gaps when no shared identifier exists, but its matches are likelihoods, not certainties.

Are cookies going away? Not in Chrome. Google reversed its plan to remove third-party cookies in 2025, and they remain on by default. But that doesn’t fix cross-device tracking, which never worked across devices in the first place. Safari and Firefox still restrict cookies heavily.

How does cross-device attribution work? In two layers. First, cross-device tracking links a person’s sessions across devices. It can do this deterministically (through a known login or email) or probabilistically (through fingerprinting when no login exists). Then an attribution model assigns credit across those linked touches. The attribution is only as reliable as the tracking beneath it.

Stop losing your best customers between devices

The fact is your buyers move across multiple devices before they purchase. If your tracking can’t follow them, you don’t have an accurate view of how your marketing is working. And without that, you’re just guessing about what’s driving revenue — and what’s wasting money.

Start your free 14-day trial of SegMetrics and see the full customer journeys you’re currently missing, stitched back into one view.

New here? Start with our complete guide to marketing attribution. And this introduction to what marketing attribution is.

What Is a Tracking Pixel? How Pixel Tracking Works

Denise Cullom — Tue, 22 Sep 2026 09:00:00 GMT

A tracking pixel is a small piece of code on your website. It records what visitors do: which pages they view, where they came from, and when they fill out a form. That data goes back to a platform like Meta, Google, or SegMetrics so you can see which marketing works.

Why it matters: No pixel, no data. Broken pixel, bad data.

What is pixel tracking?

Pixel tracking is the practice of using that code to follow a visitor’s actions across your site. The name comes from the early web. Marketers placed a 1x1 transparent image on a page. When the browser loaded the image, the server logged the visit.

Modern pixels are mostly JavaScript. They do more than log a page view. They read the URL, capture UTM parameters, set a first-party cookie, and fire events when something important happens, like an opt-in or a purchase.

How does a tracking pixel work?

Every pixel follows the same basic steps:

You install the code. It goes in your site’s header, usually before the closing </head> tag, or through Google Tag Manager.
A visitor loads the page. The browser runs the script.
The pixel records the visit. It notes the page, the referrer, the UTM tags, and the device.
The pixel sends an event. When the visitor opts in or buys, the pixel reports it.
The platform connects the dots. The data ties back to an ad, an email, or a campaign.

Pro Tip: Step 5 is where most setups fall apart.

What are the types of tracking pixels?

Ad platform pixels. The Meta Pixel, the TikTok Pixel, and the LinkedIn Insight Tag. They report conversions back to one ad network so it can optimize your ads. Each one only sees its own traffic.

Analytics pixels. Google Analytics 4 is the common one. It counts sessions and events across all your traffic. It does not know who the visitor is or what they paid you later. (Here’s how SegMetrics compares to GA4.)

Attribution pixels. These tie anonymous clicks to a known contact and that contact’s revenue. The SegMetrics pixel works this way. When a visitor fills out a form, the pixel links their email to every earlier visit and traffic source. Then it matches that contact to the sales in your cart and CRM.

What is a pixel helper?

A pixel helper is a browser extension that shows you which pixels fire on a page. It tells you whether each one loaded, which events it sent, and whether it hit errors.

The best known is the Meta Pixel Helper for Chrome. Google offers Tag Assistant for Google tags. Most ad platforms have something similar.

Limitations: A pixel helper answers one question: did the code fire? It does not tell you whether the data is right, or whether it connects to revenue. For that, you need to validate your ad tracking end to end.

How do I check if my tracking pixel is working?

Use three checks, from quick to thorough.

1. Run a pixel helper. Open your page with the extension on. Confirm the pixel shows up and fires a page view.

2. Check the browser console. Right-click the page, choose Inspect, and open the Console tab. Most pixels print a load message or throw a visible error. For SegMetrics, look for the version line, such as SegMetrics v2.3.0.

3. Watch the network tab. Clear the log, fill out a form on your page, and watch for the event request. For SegMetrics, you should see an identifier request when you submit an email.

Pro Tip: SegMetrics also has a built-in debugger. Go to Site Setup, enter your URL in the Debug Tracking Code window, and click Start Debugger. A console opens at the bottom of your browser and shows visitor activity as it happens.

For the full walkthrough, see How to check your SegMetrics pixel install.

Why is my tracking pixel not firing?

These cause most failures:

Wrong placement. The code sits in the footer or body instead of the head.
Forms in iframes. Embedded forms from other tools run on a different domain. The pixel on your page can’t see them. Add the pixel to the form itself if you can.
Script conflicts. Another plugin throws a JavaScript error and stops the rest of the page’s scripts.
Ad blockers and browser privacy. Safari, Firefox, and blockers limit third-party pixels. First-party tracking holds up better. SegMetrics offers a self-hosted pixel and server-side tracking for this reason. Apple’s iOS link tracking protection can also strip parameters from your URLs.
Tag Manager triggers. The tag exists but never fires because the trigger is wrong.

Why pixels alone don’t prove what’s working

Each ad pixel grades its own homework. Meta claims the sale. Google claims the same sale. Your email tool claims it too. Add them up and you have made three times the money you actually made. (Why Facebook’s attribution data is mostly guesswork.)

Pixels also stop at the click. They don’t see the webinar a lead attended three weeks later, the sales call, or the renewal in month six.

SegMetrics fixes that. One pixel captures every visit and source. When a visitor becomes a lead, SegMetrics ties them to their contact record and their real revenue from your cart and CRM. Choose the attribution model that fits your funnel, and see which campaigns drive customers, not just clicks.

Start your free trial and check your pixel in five minutes.

Tracking pixel FAQ

Is a tracking pixel the same as a cookie? No. The pixel is the code that runs. A cookie is the small file it stores in the browser to recognize a returning visitor. Most pixels use both.

Are tracking pixels legal? Yes, with rules. Laws like GDPR and CCPA require disclosure, and in some regions consent, before you track visitors. Check your consent setup with your legal counsel.

Do I need a separate pixel for every platform? For ad optimization, yes. Each ad network needs its own pixel to learn. For attribution, you want one source of truth that sees all channels together.

What does “pixel fire” mean? A pixel fires when its code runs and sends data to the platform. A page view fires on load. A conversion fires when the visitor completes the action you defined.

Does a pixel slow down my site? A single well-built pixel adds very little load time. Stacking many pixels can add up. Load them through Tag Manager or asynchronously to limit the impact.

Can AI Replace Your Marketing Attribution Software?

Denise Cullom — Mon, 21 Sep 2026 15:00:00 GMT

No. AI can replace the analyst reading your attribution data. It cannot replace the data itself, and a tracking pixel alone doesn’t produce it. AI can reason about your marketing. It cannot remember the click.

That answer is showing up more often because a new wave of tools is pitching exactly the opposite: buy a pixel once, let AI handle the rest, cancel your attribution subscription. It’s a compelling pitch if you’ve ever opened a dashboard and felt like you needed a data analyst just to read it. It’s also selling you half a product and calling it the whole thing.

What does a tracking pixel actually do?

A pixel is a script that captures visitor and conversion events on your site and forwards them to ad platforms through their conversion APIs, things like Meta’s Conversions API or Google’s Enhanced Conversions. That’s the entire job of a pixel. Every attribution platform runs one, SegMetrics included. A vendor pitching “our pixel” as the innovation is describing table stakes, not a breakthrough.

The real work happens downstream of capture, and it’s the part a pixel alone can’t do:

Stitching a visitor’s touches together across sessions, devices, and channels into a single customer journey
Deduplicating events so the same conversion doesn’t get double-counted across ad platforms
Reconciling what the pixel saw against what actually got paid, in Stripe, your CRM, or your course platform, including refunds and chargebacks that land weeks later
Handling offline and delayed conversions, like a call that closes a deal three weeks after the last ad click

Why it matters: None of that is a one-time build. Browsers keep restricting third-party cookies, Safari’s tracking prevention keeps getting stricter, and ad platforms rewrite their conversion APIs on their own schedule, not yours. Every one of the 100+ integrations an attribution platform maintains is a moving target. Keeping a pixel accurate is maintenance work every quarter, indefinitely.

What can AI actually add on top of a pixel?

AI can read clean, modeled attribution data and answer “is it working” in plain language, instead of making you interpret a dashboard and a spreadsheet yourself. Ask it which channel is driving profitable customers, not just cheap leads, and a good model answers in a sentence instead of an afternoon. That’s a real and overdue advance for marketers who never wanted to become data analysts.

AI is also becoming something you have to measure, not just something you use. AI search tracking exists because buyers now arrive from ChatGPT and Perplexity, and those visits need attributing like any other channel. A tool that can’t see them is already behind.

But a model is only as good as what it reads. Fed raw, unreconciled pixel events, it will answer confidently and wrong, because it has no way to know that a click was a duplicate, that a sale later refunded, or that the “conversion” it’s citing was a $7 tripwire and not the $2,000 course sale behind it. Garbage in, fluent garbage out.

Why is “one-time purchase” a red flag for attribution software?

Because attribution has real recurring costs that don’t stop the day you buy: event processing at scale, data storage, and constant integration maintenance as Meta, Google, TikTok, Stripe, and every CRM change their APIs. Subscription pricing reflects that ongoing cost. It isn’t just a billing preference.

A one-time fee for a “pixel plus AI” tool usually means one of two things happened to that cost structure:

You’re hosting the script yourself, and the vendor’s costs drop to near zero the moment the sale closes. Little incentive remains to keep integrations current once they’ve been paid.
The tool never built the expensive parts, no historical backfill, no multi-touch modeling, no revenue reconciliation, and the AI layer narrates whatever raw numbers your ad platform already shows you for free.

Pro Tip: Lifetime-deal pricing on infrastructure-heavy products is a familiar pattern for pre-launch tools chasing early revenue and testimonials before their real costs catch up. Some quietly move to subscription or usage fees once they leave alpha. Worth watching for, not worth being surprised by.

Is there ever a legitimate case for a bare pixel plus AI?

Best for: a single channel, a single offer, and no need to reconcile against a CRM or handle delayed conversions. In that narrow case, a lightweight pixel with an AI summary on top can be genuinely useful, and genuinely cheaper.

The pitch breaks down the moment you’re running multiple channels, multiple offers, or anything with a sales cycle longer than the click. That describes most course creators, coaches, and agencies as soon as they’re spending real money on ads. It’s also why buyers comparing Hyros alternatives or Triple Whale alternatives are usually shopping for more modeling, not less.

What does SegMetrics do differently?

SegMetrics Advisor is an AI layer that reasons over attribution data already captured, deduplicated, and reconciled against actual revenue, not over a model’s best guess at your funnel. The pixel and the modeling happen first, built to survive the API changes and tracking restrictions that break lighter tools. Multi-segment analysis means the answer accounts for the fact that your cold traffic and your list behave nothing alike.

The reasoning sits on top of that foundation, not instead of it. AI can reason about your marketing. It cannot remember the click, so something has to remember it first. That’s the case for SegMetrics in one sentence.

Four questions to ask before switching to an “AI plus pixel” tool

Does it reconcile against actual revenue? Check whether it matches conversions to what landed in Stripe, refunds included, or only to what the ad platform self-reports.
Does it keep historical data? Ask what happens to a year of attribution history the day you switch. If the answer is “it resets,” you’re flying blind for months while the new tool builds a baseline.
Who owns the fix when an ad platform changes its API? Every conversion API gets rewritten eventually. Know whether that’s the vendor’s job or yours before you own the pixel outright.
Is the AI reasoning over modeled data or raw events? Ask specifically what it reads. A vague answer usually means the modeling doesn’t exist.

Our guide to marketing attribution tools walks through the same criteria across the major platforms if you want the full comparison.

Recommendation: If a tool can’t answer all four clearly, it isn’t replacing your attribution platform. It’s replacing your dashboard, and charging you once for the privilege of maintaining the rest yourself.

Best MCP Servers for Marketing (2026)

Denise Cullom — Fri, 18 Sep 2026 09:00:00 GMT

The best MCP servers for marketing in 2026 are Klaviyo, HubSpot, Ahrefs, Google Ads, Stripe, Zapier, and SegMetrics. Klaviyo is free with any account and can create campaigns. HubSpot covers CRM and content. Ahrefs covers search and AI visibility. SegMetrics connects revenue back to the source. Most official servers are read-only.

What is an MCP server?

An MCP server is a connector that lets an AI assistant read and act on data inside a tool you already pay for.

MCP stands for Model Context Protocol. Anthropic released it in November 2024. OpenAI adopted it in April 2025. It is now the standard way AI models talk to software.

Before MCP, you exported a CSV and pasted it into a chat window. The model saw a snapshot. It could not check anything. It could not go deeper.

With MCP, the model queries the live account. You ask a question. It pulls the data. It asks the next question itself.

The official MCP registry listed 9,652 distinct servers as of May 2026. About 606 of them sit in the marketing automation category. Monthly SDK downloads grew from 100,000 at launch to 97 million by March 2026.

Most marketers have never heard of it. That is the opportunity.

What can a marketing MCP server actually do?

It depends on the server. There are three kinds.

Read-only servers pull data and report. Google Analytics, Google Ads, Amplitude, and SegMetrics work this way. They answer questions. They change nothing.

Write-capable servers can act. Klaviyo can launch a campaign. HubSpot can update a deal. Stripe can issue a refund. X Ads can build a campaign.

Bridge servers connect to everything else. Zapier reaches 9,000 apps. Firecrawl reads any web page.

Read-only is where most marketers should start. The value is in the questions, not the actions.

The 13 best MCP servers for marketing

1. Klaviyo MCP (best free official server)

Klaviyo published its MCP server in September 2025. It is free with any Klaviyo account. It is hosted at mcp.klaviyo.com/mcp and uses OAuth.

It reads campaign and flow performance. It suggests subject lines based on your own historical data. It builds segments. It drafts emails, uploads content, and launches campaigns.

That combination is rare. Official, free, write-capable, and usable by a marketer without a developer. You need Owner, Admin, or Manager access on the account.

Ask it: “Show me flow performance for the last 60 days and tell me which one is decaying.”

2. HubSpot MCP (best for CRM and content together)

HubSpot runs a remote server at mcp.hubspot.com with OAuth 2.0 and PKCE. It exposes more than 30 tools.

It reads and writes contacts, companies, deals, tickets, products, quotes, and engagements. It reads campaigns, landing pages, and blog posts. It runs SQL-style queries against your CRM data.

Two limits matter. AEO tools require Marketing Hub Professional or Enterprise. If your account has Sensitive Data enabled, activity and conversation data is blocked through MCP entirely.

3. Ahrefs MCP (best for search and AI visibility)

Ahrefs hosts its server at api.ahrefs.com/mcp/mcp with more than 100 tools. It covers Site Explorer, Keywords Explorer, Rank Tracker, and Site Audit.

The standout in 2026 is Brand Radar. It tracks how often AI models mention and cite your brand. If you care about getting picked up by ChatGPT and AI Overviews, that data is hard to get anywhere else.

It is not cheap. Plans start at Lite, $129 per month. There is no free tier and no separate MCP fee.

Watch the row cap. On Lite, every report truncates at 100 rows. The AI will summarize those 100 rows and never mention the other 900. That is the most common way to get a confidently wrong answer out of a marketing MCP server.

4. Semrush MCP (the Ahrefs alternative)

Semrush hosts at mcp.semrush.com/v2/mcp with OAuth. It covers domain overview, keyword research, and backlinks, plus a report catalog you can query directly.

The free Semrush plan does not include MCP. The cheapest qualifying tier is SEO Classic Pro at $139 per month. Semrush uses different plan names in its docs than on its pricing page, so confirm eligibility with support before you buy.

Client support is the broadest on this list. Claude, ChatGPT, Cursor, VS Code, and Perplexity.

5. Google Ads MCP (official, and read-only)

Google released an open-source Google Ads MCP server in October 2025. It lists accessible accounts, runs GAQL queries, and returns schema so the model writes valid queries.

It is read-only. It cannot create campaigns, change bids, or edit assets. Any tool promising to let AI run your Google Ads is using something else.

It runs locally through pipx or self-hosted on Cloud Run. There is no Google-hosted endpoint. That setup step stops most marketers.

6. Google Analytics MCP (official, and harder than it sounds)

Google publishes this one too. It runs reports, funnel reports, and realtime reports in plain language.

The repo is labeled Experimental. It is read-only. It is local install only, and it requires a Google Cloud project with the Admin API and Data API enabled plus Application Default Credentials.

That is a real barrier. There is a large gap between “Google has an official GA MCP server” and “a marketer can use it today.”

7. Stripe MCP (best for revenue questions)

Stripe hosts at mcp.stripe.com. It reads customers, subscriptions, invoices, and disputes. It writes payment links, coupons, promotion codes, products, and checkout sessions.

This one moves money, so Stripe built guardrails. Refunds and outbound payments require human confirmation through an approval URL. Unapproved requests expire in 24 hours. Sessions are revocable in the dashboard.

Stripe’s own documentation warns customers to be careful running its MCP server alongside others because of prompt injection. Take that seriously. More on it below.

8. Zapier MCP (best for everything without its own server)

Zapier generates a private endpoint for you at mcp.zapier.com. It reaches 9,000 apps and 40,000 actions.

That makes it the practical answer for every tool on your stack that has no MCP server of its own. It also has the best permission model on this list. You toggle individual actions on and off, with rate limits and audit logs.

It is available on every Zapier plan including free. Each tool call costs two Zapier tasks, so an agent that chains ten lookups burns twenty tasks.

9. SegMetrics MCP (best for attribution)

SegMetrics released its MCP server in May 2026. Setup takes about a minute through the third-party connection flow in your account.

It exposes 13 tools. The core one is run-query. It traces revenue back to first-touch source and campaign across the full funnel. It segments any metric by traffic source, list, tag, or product. It runs multi-step funnel drop-off analysis.

Two things make it different from a thin API wrapper. It is multi-account by design, so an agency can query across client accounts in one conversation. And it ships with an opinionated analyst framework that refuses to hand you an aggregate number without breaking it down by segment.

It is read-only. Plans start at $57 per month with a 14-day free trial.

10. Firecrawl MCP (best for competitor research)

Firecrawl hosts at mcp.firecrawl.dev/v2/mcp and also runs locally through npx. It ships 25 tools.

It scrapes, searches, maps, crawls, and parses. The monitoring tools track competitor page changes over time. There is a keyless free tier for scrape, search, and parse.

Use it for landing page teardowns, SERP research for content briefs, and bulk content audits.

FeedHive hosts its MCP server at mcp.feedhive.com. It connects with OAuth or a FeedHive API key, and works in Claude, ChatGPT, Cursor, and other MCP clients.

It covers posts, media, labels, social accounts, posting-plan slots, and analytics across Instagram, LinkedIn, X, TikTok, Facebook, Threads, YouTube, and more. It can draft posts in your brand voice, slot them into your posting plan, and pull post-level analytics.

It can write, with guardrails. Scheduling, edits, and deletes each need an explicit confirmation flag, so the model cannot publish anything by accident. Workspaces with approvals turned on keep agent posts pending until a person approves them. Plans start at $15 per month.

Ask it: “Draft next week’s LinkedIn posts from our last three blog articles and put them in my open plan slots as drafts.”

12. X Ads MCP (best-designed write-capable ad server)

X launched its Ads MCP server in August 2026 at ads-api.x.com/mcp. It has 74 tools across 8 categories, and unlike Google’s it can write. Campaign creation, line item management, audience building, creative uploads.

The safety design is the part worth copying. Every campaign and line item is created paused. Nothing spends money until you activate it by hand.

Scopes are granular. Take ads.read and offline.access for a read-only agent. Add ads.write only when you want the agent to build.

It is free. Meta also runs an official ads MCP server at mcp.facebook.com/ads. LinkedIn and Reddit have no official servers, only community ones.

13. Canva MCP (best for creative production)

Canva ships an AI Connector that works with Claude, ChatGPT, Gemini, and Microsoft Copilot. It searches your designs, generates content, exports files, and autofills Brand Templates.

The free tier works for basic actions. Brand Template autofill requires Canva Enterprise. Some actions consume Canva AI credits.

Do not confuse the AI Connector with the separate Canva MCP Server for developers at mcp.canva.com/mcp, which is waitlisted. Most articles blur the two.

Comparison table

Server	Official	Hosted	Auth	Read/Write	Cost
Klaviyo	Yes	Yes	OAuth	Read + write	Free with account
HubSpot	Yes	Yes	OAuth + PKCE	Read + write	Some features Pro/Ent
Ahrefs	Yes	Yes	OAuth to MCP key	Read	From $129/mo
Semrush	Yes	Yes	OAuth	Read	From $139/mo
Google Ads	Yes	No, local	OAuth	Read only	Free
Google Analytics	Yes	No, local	ADC / OAuth	Read only	Free
Stripe	Yes	Yes	OAuth	Read + write	Free
Zapier	Yes	Yes	OAuth	Read + write	All plans, 2 tasks/call
SegMetrics	Yes	Yes	Account auth	Read	From $57/mo
Firecrawl	Yes	Yes	API key	Read	Free tier + credits
FeedHive	Yes	Yes	OAuth or API key	Read + write	From $15/mo
X Ads	Yes	Yes	OAuth + PKCE	Read + write	Free
Canva	Yes	Yes	Account	Read + write	Free tier, Ent for templates

Which MCP servers should marketers avoid?

Three warnings.

The archived WordPress server. Automattic/wordpress-mcp was archived on January 19, 2026. Many 2026 roundups still recommend it. The successor is WordPress/mcp-adapter, a plugin that needs WordPress 6.9 and a developer to install it.

Mailchimp Marketing. Mailchimp’s Transactional product has an official MCP server at mandrillapp.com/mcp. The Marketing platform does not. Campaigns, audiences, and automations are community servers only.

Shopify for merchant analytics. Shopify’s official MCP servers are built for shopping agents buying from your store. They are not merchant marketing tools. There is no confirmed official Admin MCP server for analyzing your own store performance.

Are MCP servers safe for marketing data?

Not automatically. Marketers carry more risk than most users, and the reason is specific.

Marketing agents read untrusted content by design. Scraped competitor pages. Inbound form fills. Ad comments. Review text. Support emails. All of it is text an attacker can write.

That text goes into an agent that may also hold write access to your CRM and your ad accounts. That is called indirect prompt injection, and researchers demonstrated it against MCP systems in April 2025.

The clearest case for marketers happened on September 25, 2025. A malicious npm package named postmark-mcp impersonated the real Postmark email server. It worked exactly as advertised. It also silently blind-copied every outgoing email to an attacker. Nothing looked broken. Snyk tracked it as the first confirmed malicious MCP supply chain attack. Postmark published its own advisory confirming the package was not theirs.

The wider numbers are not reassuring. Only 8.5% of MCP servers use OAuth at all, according to an Astrix audit of 5,200 servers in October 2025. The rest use long-lived API keys with no scoping and no expiry. Only 12.9% of indexed servers score as high trust.

Five rules that cover most of it:

Prefer OAuth servers with revocable sessions over paste-your-API-key servers.
Take read-only scopes until you have a reason not to.
Require human confirmation on anything that writes or spends.
Do not connect a scraper and a sending tool to the same agent without review.
Verify the publisher namespace before installing anything local.

Which AI apps support MCP servers?

Claude has the deepest support. The desktop and web apps, Cowork, and Claude Code all handle local and remote servers with OAuth. Custom connectors require a paid plan.

ChatGPT added MCP under Developer Mode in late 2025. Paid plans only. Remote servers only, no local ones. Write tools are limited to Business and Enterprise.

Cursor, VS Code with Copilot, and Windsurf all support MCP well, but they lean developer. Windsurf caps you at 100 tools.

Consumer Gemini has no user-configurable MCP. Support lives in Vertex AI.

The practical answer in 2026: a marketer using MCP is on Claude or on paid ChatGPT. Everyone else is waiting.

How do you start with MCP servers for marketing?

Start with one server, not six.

Connecting several at once floods the context window before you ask anything. Ahrefs alone exposes over 100 tools. X Ads has 74. ActiveCampaign has around 80. Three of those at once and the model spends its attention reading a tool menu.

Pick the server that answers your most expensive recurring question. If that question is “which channel actually made money,” start with attribution. If it is “why did that flow stop converting,” start with Klaviyo. If it is “are we losing ground in AI search,” start with Ahrefs.

Then ask it something you would have paid an analyst to answer. Not “how many visitors did we get.” Ask “which traffic source produced customers who are still paying after six months.”

If you want a running start, we published 174 AI marketing prompts for attribution and analytics built for exactly this. Paste one in and see what comes back.

That second question is the whole point. It takes four dashboard exports and an afternoon. It takes an MCP server about forty seconds.

Frequently asked questions

What is the best MCP server for marketing? Klaviyo is the best free official option because it is hosted, uses OAuth, works for any account holder, and can create campaigns rather than only read them. For attribution, SegMetrics. For search and AI visibility, Ahrefs.

Are MCP servers free? Some are. Klaviyo, Stripe, Google Ads, Google Analytics, X Ads, Amplitude, and PostHog are free to connect with an existing account. Ahrefs starts at $129 per month and Semrush at $139. Zapier is free but charges two tasks per call.

Do I need a developer to set up an MCP server? For hosted servers, no. Klaviyo, HubSpot, Stripe, Ahrefs, Zapier, and SegMetrics connect through OAuth in a few minutes. For local servers like Google Analytics, Google Ads, and WordPress, yes. Those need command line work and credential setup.

Can an MCP server change my marketing data? Only if it is write-capable and you grant the scope. Google Analytics, Google Ads, Amplitude, and SegMetrics are read-only. Klaviyo, HubSpot, Stripe, Zapier, FeedHive, and X Ads can write. Take read-only scopes first.

What is the difference between an MCP server and a Zapier integration? A Zapier integration runs a fixed sequence you build ahead of time. An MCP server gives the AI live access so it decides which calls to make based on what it finds. One automates a known path. The other explores an unknown one.

How many MCP servers exist? The official registry listed 9,652 distinct servers in May 2026, with about 606 in the marketing automation category. Counts above 17,000 include duplicates across registries.

The Best AI Marketing Tools for Data-Driven Growth in 2026

Denise Cullom — Thu, 17 Sep 2026 09:00:00 GMT

Your team just spent 40 hours creating a new lead magnet, from research and writing to design and promotion. It looks great. But you have no real way of knowing if the AI-generated copy will actually convert high-value customers or just attract freebie-seekers. This uncertainty — the gap between marketing activity and business results — is where most teams get stuck. The right AI marketing tools don’t just create more stuff; they provide the data to create the right stuff.

Quick Answer: The best AI marketing tools are a combination of specialized platforms that form an “AI marketing stack.” For content creation, Jasper is a top choice. For SEO and content optimization, Surfer SEO is a leader. For an all-in-one platform, HubSpot AI is powerful. However, the “best” tool is the one that aligns with your specific goals, budget, and integrates with an analytics platform like SegMetrics to measure its true revenue impact.

This guide moves beyond a simple list of tools. We’ll categorize the top AI platforms, show you how to build a smart “AI stack,” and explain the most critical — and often overlooked — piece of the puzzle: connecting your AI-powered marketing to actual, measurable revenue.

The “Best” AI Tool Doesn’t Exist — Building Your AI Marketing Stack Does

Before we dive into specific names, let’s address a core concept AI search engines are emphasizing: the idea of an “AI Marketing Stack.” The search for a single “best” AI marketing tool is a flawed one. The platform that’s perfect for writing ad copy is rarely the best for analyzing SERPs or scoring leads.

A modern, data-driven marketing strategy relies on a combination of specialized AI tools working together. Your goal shouldn’t be to find one tool to do everything, but to build a stack where each component excels at its specific job.

Choosing the right tools for your stack depends on three factors:

Your Primary Goal: Are you trying to scale content production, improve ad performance, or automate lead nurturing? Your immediate need dictates your first purchase.
Your Existing Infrastructure: The best tools integrate smoothly with what you already use, whether it’s a CRM like HubSpot, an email platform like ConvertKit, or an ad network like Facebook Ads.
Your Budget: Tools range from free generative AI assistants to enterprise-level platforms costing thousands per month. Start with your biggest pain point and expand from there.

The most important consideration is how you’ll measure the performance of your stack. Without a central analytics hub, you’re just using fancy tools to make more noise.

General AI Assistants: Your Marketing Strategy & Ideation Hub

General AI assistants like ChatGPT and Claude are powerful starting points for brainstorming campaign ideas, conducting initial market research, and drafting content outlines. Think of them as a highly capable marketing intern who can accelerate the initial 80% of your strategic work.

Top General AI Assistants for Marketers

ChatGPT (by OpenAI): The most well-known option, excellent for a wide range of tasks from drafting emails to generating keyword ideas and summarizing customer feedback. Its versatility makes it a foundational tool.
Claude (by Anthropic): Known for its large context window, Claude is exceptional at analyzing long documents. You can upload an entire ebook or dozens of customer interviews and ask it to extract key themes, pain points, and testimonials.
Perplexity AI: This tool functions as an “answer engine,” citing its sources directly. It’s ideal for market research, competitor analysis, and finding recent statistics to support your content, as it provides links to its sources for verification.

Use Cases for General AI in Marketing

These tools aren’t meant to write a final, polished blog post. Their strength is in the pre-production and strategy phases.

Campaign Ideation: Feed it your target audience’s pain points and ask for 10 campaign angles for a new product launch.
Persona Generation: Provide it with survey data or customer interview transcripts and ask it to create a detailed customer persona.
Content Briefs: Give it a target keyword and ask for a comprehensive content outline, including H2s, H3s, and potential questions to answer.

Example in Action: A course creator used Claude to analyze the chat logs from their last 5 webinars. They asked it to identify the top 10 most frequently asked questions. Those questions became the core modules for their new mini-course, ensuring the content directly addressed proven customer needs.

AI Content Creation & Copywriting Tools

AI content creation and copywriting tools help marketing teams scale content production, from blog posts and ad copy to social media updates, while maintaining brand voice. These platforms are more specialized than general assistants, offering templates, workflows, and brand voice features tailored specifically for marketing content.

Top AI Copywriting Tools

Jasper: One of the most established players, Jasper offers a huge library of templates for everything from AIDA-framework ad copy to long-form blog posts. Its “Brand Voice” feature can learn your style from existing content.
Copy.ai: A strong competitor to Jasper, Copy.ai excels at sales and marketing copy. It has robust workflow features that allow you to generate entire sequences of content (e.g., a series of emails or social posts) from a single brief.
Writesonic: A versatile tool that includes features for writing, AI art generation, and a chatbot builder. It’s a good option for teams that need a multipurpose content tool.

Jasper vs. Copy.ai: Which Is Right for You?

While both are excellent, they serve slightly different needs. Choosing the right one depends on your primary content focus.

Feature	Jasper	Copy.ai	Best For
Core Strength	Long-form content (blogs, articles) and brand voice consistency.	Sales and marketing copy (ads, emails, social) and workflows.	Teams focused on content marketing vs. direct response.
Templates	Extensive library covering a wide range of content types.	More focused on marketing and sales-specific frameworks.	Generalists vs. specialists.
Workflows	Good for individual content pieces.	Excellent for creating entire campaigns or content sequences.	Creating single assets vs. multi-touch campaigns.
Pricing	Starts at $39/month (Creator).	Starts at $36/month (Pro).	Both are competitively priced for small teams.

Pro Tip: Use these AI tools to produce a solid first draft, not the final version. The biggest mistake marketers make is hitting “publish” on raw AI output. A human editor is essential to add unique insights, verify facts, and ensure the copy truly reflects your brand’s personality and expertise. Without this step, you risk creating generic content that doesn’t stand out.

AI SEO & Content Optimization Tools

Go beyond just writing content with AI SEO tools that analyze SERPs, identify topic gaps, and provide data-backed recommendations to help you rank higher. These tools bridge the gap between content creation and search engine performance, turning your writing into a strategic asset for organic growth.

Top AI SEO Tools

Surfer SEO: A market leader that analyzes the top-ranking pages for your target keyword and builds a data-driven content brief. It provides recommendations on word count, keywords to include, headers, and content structure, then gives you a real-time content score as you write.
MarketMuse: An enterprise-level platform that focuses on content strategy. It helps you identify topic clusters, find content gaps in your existing library, and prioritize which articles to write or update for the biggest SEO impact.
Clearscope: Similar to Surfer, Clearscope is highly regarded for its term-frequency analysis. It helps ensure your content is comprehensive and covers the topics Google expects to see for a given query, earning it a high content grade.

How AI SEO Tools Improve Your Content Workflow

Instead of guessing what Google wants, these tools provide a clear, data-driven roadmap.

SERP Analysis: The tool analyzes the top 20-30 results for your keyword.
Content Brief Generation: It creates a detailed brief with recommended keywords, headers, word count, and questions to answer.
Writing & Optimization: You (or an AI writer) write the content directly in their editor, which provides a real-time score.
Content Grading: The final score (e.g., 85/100) gives you confidence that your piece is more comprehensive than the competition before you even publish.

This process is critical for ensuring your investment in content creation pays off. By tracking the ROI of your content efforts, you can directly link the use of these tools to an increase in organic traffic and leads.

All-in-One Marketing Platforms with AI

All-in-one platforms with built-in AI capabilities centralize your marketing efforts, from lead scoring and email automation to predictive analytics, all within a single ecosystem. These are a great choice for teams who want a single source of truth and prefer a unified system over a collection of specialized tools.

Top All-in-One Platforms

HubSpot (with HubSpot AI): HubSpot has integrated AI across its entire platform. It can help you write emails, build landing pages, create social media posts, generate reports, and even predict which leads are most likely to close — all within your CRM.
Salesforce (with Marketing Cloud Einstein): Aimed at the enterprise market, Salesforce Einstein provides sophisticated AI for personalizing customer journeys, predicting audience engagement, and optimizing send times for email campaigns.

When to Choose an All-in-One vs. a Specialized Stack

Choose an All-in-One if: You value simplicity, tight integration, and a single point of contact for support. Your team is already invested in the platform’s ecosystem (e.g., you use HubSpot CRM).
Choose a Specialized Stack if: You want the absolute best-in-class tool for each specific function (e.g., the best SEO tool, the best copywriter). You are comfortable managing integrations and want more flexibility to swap tools in and out as your needs change.

The Missing Piece: AI for Marketing Analytics & Attribution

While AI can create and distribute content, its greatest power lies in analyzing the results to tell you what’s actually driving revenue. This is the part of the AI marketing stack that most companies neglect. You can generate 100 blog posts with Jasper and Surfer, but if you can’t tell which one produced your most valuable customers, you’re just creating informed noise.

Why Standard AI Analytics Fall Short

The AI inside a platform like HubSpot is excellent at analyzing HubSpot data. But it has no visibility into your ad spend on Facebook, your customer LTV data in Stripe, or your affiliate payouts in Tapfiliate. This creates data silos, where each tool knows its own performance but none of them can see the full customer journey.

This is where true marketing attribution becomes essential. You need a system that can connect the dots from the first ad click to the final purchase and beyond.

How SegMetrics Connects Your AI Marketing Stack

SegMetrics acts as the central data brain for your entire marketing stack. It integrates with your AI content tools, your ad platforms, your CRM, and your payment processor to give you a single, unified view of performance.

It answers the “So what?” question: Your AI-powered email campaign got a 40% open rate. So what? SegMetrics can tell you if those openers turned into $500 customers or $5 customers.
It validates your AI tools: You used Surfer SEO to optimize a blog post that now ranks #1. SegMetrics can show you the lifetime value of all the customers who signed up through that post.
It optimizes your entire funnel: By connecting data from every touchpoint, you can see exactly where your best customers are coming from and optimize your entire marketing funnel for revenue, not just vanity metrics.

Without a dedicated analytics layer, your AI marketing stack is just a collection of expensive content machines. With it, your stack becomes a data-driven growth engine.

Frequently Asked Questions about AI Marketing Tools

What is an AI marketing tool?

An AI marketing tool is a software application that uses artificial intelligence, machine learning, and natural language processing to automate or augment marketing tasks. These tasks include content creation, data analysis, customer segmentation, SEO optimization, and ad campaign management, helping marketers make faster, more data-driven decisions.

How much do AI marketing tools cost?

The cost of AI marketing tools varies widely. Some tools, like the free version of ChatGPT, cost nothing to start. Specialized tools like Jasper or Surfer SEO typically range from $40 to $200 per month for small businesses. All-in-one enterprise platforms like Salesforce Marketing Cloud can cost thousands of dollars per month depending on the scale of your operations.

Can AI replace human marketers?

No, AI is a tool that augments human marketers, it doesn’t replace them. AI excels at executing repetitive, data-heavy tasks like drafting copy or analyzing large datasets. However, it cannot replicate human creativity, strategic thinking, emotional intelligence, or the ability to build genuine customer relationships. The future of marketing is a human-AI collaboration.

What is the best free AI tool for marketing?

For general marketing tasks, ChatGPT’s free version is arguably the best starting point due to its versatility. For more specific needs, HubSpot offers a suite of free tools with built-in AI features for CRM and email marketing. Many paid tools also offer limited free trials or freemium plans to let you test their capabilities.

How do I choose the right AI marketing tools for my business?

Start by identifying your single biggest marketing bottleneck. Is it content creation speed? SEO performance? Lead quality? Choose a tool that directly addresses that primary pain point. Assess its ability to integrate with your existing software, consider your budget, and prioritize tools that provide clear analytics to measure their impact.

What are the risks of using AI in marketing?

The main risks include over-reliance on AI leading to generic, unoriginal content that doesn’t reflect your brand’s unique voice. There are also concerns around data privacy and the accuracy of AI-generated information, which always requires human verification. Finally, focusing too much on AI-driven vanity metrics (like content volume) without tying it to revenue can lead to wasted effort.

Conclusion

The best approach to AI marketing tools in 2026 is not about finding one perfect platform, but about building a smart, integrated stack that solves your specific challenges. Start with powerful tools for content creation and SEO optimization, but never forget the most critical piece: analytics that connect every action to actual revenue. Generating more content is easy; knowing what content drives growth is what matters.

This data-first mindset is the difference between simply using AI and using AI to build a more profitable business. To see how a dedicated analytics platform can unify your stack and reveal what’s truly working, check out the features of SegMetrics.

What is marketing attribution? (And why does it matter?)

Keith Perhac — Thu, 17 Sep 2026 09:00:00 GMT

Marketing attribution is how you figure out which of your marketing touchpoints — ads, emails, blog posts, sales calls, web pages, webinars, etc. — deserve credit for a conversion. Instead of guessing what’s working, you trace each customer’s path from first click to purchase.

Once you have this attribution data (and it’s accurate), you can measure how well any marketing activity is driving actual revenue. And if you don’t have it, well, you’re stuck guessing about what’s working.

This article is a plain English guide to the basics of marketing attribution. What it is, what it looks like in practice, and why so many smart marketers draw the wrong conclusions from it (AI included).

What does attribution mean in marketing?

At its simplest, attribution in marketing is about assigning credit. Someone buys, and you want to know which of your marketing efforts helped make that happen — so you can do more of what works and stop paying for what doesn’t.

Marketing attribution used to be easy. People had one device, came from one place, and did one thing. Perhaps they clicked an ad, landed on your page, and bought. Credit assigned, case closed.

But now people have multiple devices, most marketing is multi-channel marketing, and most buying journeys are long and complicated. For example, a person may see your ad on their phone at home, walk outside (changing their IP address), forget about you for a month, then come back on their laptop through a Google search ad. Add in a handful of emails (only some of which they saw), a webinar they half-attended, more ads, and an SMS message. What deserves the credit for the sale?

That question — which touchpoints get the credit — is the heart of marketing attribution. But the real value isn’t theoretical credit. It’s knowing what marketing activities to invest more time, attention and budget into and which to cut back on. When you have limited resources, knowing what to prioritize can mean the difference between growing or stalling (or failing).

Ultimately, marketing attribution helps take your raw and scattered marketing data, organize it, and pull out relevant trends and actionable insights.

A quick example

Say you run a $2,000 online course. Meta’s Ad Manager tells you that your Facebook ad is responsible for the sale. If that’s true, increasing your Facebook ad budget could drive more sales.

But the customer’s actual journey tells a different story:

She first found you through a podcast interview eight weeks ago.
A month later she clicked a Facebook ad and grabbed your free lead magnet.
She read four emails from your nurture sequence.
She registered for your webinar but didn’t attend.
The next morning, she clicked the link in your follow-up email and bought.

So, what “drove” that sale? If you only credit the first touch, it’s the podcast. If you only credit the last touch, it’s the follow-up email. If you credit the moment she became a lead, it’s the Facebook ad.

In fact, all three answers are correct. Or better put, they are each correct answers to different questions. And they tell you three completely different things about where to spend your next dollar.

This is the power of marketing attribution. Not only can it capture real customer journeys, it can also help you analyze your marketing from different POVs.

Why marketing attribution matters

Marketing attribution matters because leads and customers look identical at the top of the funnel.

If you’re measuring success by traffic or clicks — or even leads — you have no idea if those people actually become customers. Most businesses measure success by total revenue. “Hey, if our annual revenue is going up, we must be doing something right.” But there are a couple big problems with this approach:

It’s very inefficient. This traps you into a volume game where you have to keep working notably harder and harder to drive smaller and smaller gains. The reason for this is that the volume game forces you to scale all the channels and campaigns that don’t drive revenue along with the ones that do.
It’s not insightful. Most businesses use the volume approach…until it stops working. Revenue plateaus. Or maybe even starts falling. But without accurate marketing attribution, it’s impossible to pinpoint what, exactly, is the problem. If you don’t know the problem, how can you fix it?

With marketing attribution, you can see how much each channel, each campaign, each ad, each email, and each sales call drives lifetime revenue. For any touchpoint in your funnel, you can tell if it’s working or not.

This allows you to surgically fix or prune any touchpoints that aren’t working and double-down on those that are.

Even more, during times of rapid growth, you can see exactly what is causing that growth. And during slow downs, you’ll know where you could cut your spending without cutting your conversions.

Why most marketers get attribution wrong

If attribution is so useful, why do so many marketers skimp on it?

Several reasons.

First, marketing attribution and analytics has a learning curve. Even with user-friendly software and ready-built dashboards, there is some time and effort required to making sure your data is clean and complete and that you know how to use the tool.

Second, the answers you pull from your marketing attribution will only be as good as the questions you ask. Vague questions like “who are my best customers?” don’t deliver actionable info. What does “best customer” even mean? Is that the biggest spenders? Or the customers with the highest ROI? Or the ones who require the least ongoing support? Effective attribution and analytics requires some clarity from the user and a willingness to dig into the data.

Third, many marketers find it easier to default to the data they get from their marketing tools. Or they assume AI can handle all of the attribution and analytics for them. Unfortunately, this isn’t the case.

Can AI do marketing attribution?

It’s the $1,000,000 attribution question, so let’s answer it.

Can AI do marketing attribution? Partly — and not the part you’d hope.

When people say “marketing attribution,” they’re really describing two separate jobs:

Capturing and organizing: Recording every visit, click, opt-in, and purchase, tying each one to an identifiable person across every tool and device, over months or years, and labeling it all so it’s clean enough to trust.
Reasoning: Reading that organized data and telling you what it means and what to do next.

AI is genuinely good at the second job. It’s often faster and sharper than a human analyst. But AI is structurally incapable of the first job. Not because the models aren’t smart enough but because the raw data was never tied to real people in the first place. That’s what attribution software does. For example, SegMetrics uses nine different signals (email, browser, IP, integrations, custom fields, cross-domain click tracking, cookies, screen size, and geo-location) to identify one person across different sessions, devices, and platforms. AI can’t reconstruct that from a pile of disconnected exports or from multiple MCP Servers.

The real danger is that if you give AI messy, duplicate-ridden marketing data, it won’t tell you it’s confused. It’ll just take a confident swing. When we tested 174 marketing-analytics prompts on an AI connected only to Stripe, a CRM, an email tool, and an ad platform — no attribution layer underneath — it could correctly answer just 30% of the questions. Half were flat-out impossible, and the other 20% came back with credible-looking answers that pointed in the wrong direction.

So no, AI can’t do attribution.

But if you feed it clean, verified attribution data, then your AI becomes a genuinely powerful analyst. That’s the whole idea behind the SegMetrics MCP Server. Accurate data first, then AI reasoning on top.

Attribution Models and Windows, in 60 Seconds

Two related concepts come up the moment you go deeper into attribution, so here’s the tl;dr:

Attribution models are the rules for how you assign credit to different touchpoints in a customer journey. They include first-touch, last-touch, linear, and a few other models. Each answers a different question.
Attribution windows are how far back you let a touchpoint earn credit. A 7-day window will ignore the ad someone clicked a month before they bought. Short attribution windows are a real problem if your sales cycle runs long. Lifetime attribution is the gold standard.

Both attribution models and windows are choices that can change your numbers.

Frequently Asked Questions

What is marketing attribution? Marketing attribution is the practice of connecting each sale back to the marketing touchpoints that lead up to it. This can stretch across the whole customer journey. When you know the specific channels, campaigns, emails, ads, pages, and actions that moved someone to buy, you can see what’s really driving revenue in your business.

What does attribution mean in digital marketing specifically? Attribution in digital marketing is the same idea applied across your digital channels. It stitches a person’s activity across ads, emails, your website, and social into one journey, then assigns credit to the steps that contributed to the conversion. A key part of digital marketing attribution is tracking customers across different devices, platforms and over time.

What’s an example of marketing attribution? Say a buyer finds you via a video, clicks a Facebook ad to grab a lead magnet, reads a few nurture emails, attends a webinar, talks to a sales rep, then buys. Attribution decides which of those touchpoints gets credit for the sale. The attribution model you use will change how you view the data and can change the answer you receive.

Why do so many marketers get attribution wrong? The number one reason is that they don’t invest in an accurate and transparent attribution layer in their business. They rely on their marketing tools (like their ad platforms and email) to tell them what’s working. But these tools focus on top of funnel metrics, which means most marketers end up optimizing for clicks, not revenue.

Ready to see your own attribution?

The fastest way to understand attribution is to watch it map your own funnel. You can set up marketing attribution for your business in as little as 10 minutes.

Start your free 14-day trial to get started today — no credit card required. Or book a call with one of our marketing analytics experts if you’d like a private tour.

New here? The Complete Guide to Marketing Attribution ties all of this together.

AI Overview Tracking: How to Measure What Google's AI Is Doing to Your Traffic

Denise Cullom — Tue, 15 Sep 2026 09:00:00 GMT

You checked Search Console. Impressions are up. Clicks are flat, maybe down. Nothing in Google Analytics explains why.

Here’s what’s probably happening: Google answered the question on the results page before anyone reached your site. An AI Overview sat above your listing, summarized your content, and sent the click somewhere else, or nowhere at all.

AI Overviews now show up on roughly one in five Google searches in the US, and higher than that in some countries and categories. Health queries trigger them more than 60% of the time. That’s not a rounding error. That’s a structural change in how search works, and most marketing dashboards weren’t built to see it.

This guide is about the part almost nobody talks about: not how to rank in AI Overviews, but how to track whether you’re showing up in them, whether it’s helping, and whether it’s worth the effort. If you haven’t read our guide on how to actually rank in Google’s AI Overview, start there first. This one picks up after you’ve done the work and need to know if it paid off.

What “AI Overview tracking” actually means

AI Overview tracking is the practice of monitoring whether your pages get cited inside Google’s AI-generated answer boxes, how often those citations appear, and what happens to your traffic and revenue as a result.

That’s three separate questions, and most tools only answer the first one:

Presence. Does an AI Overview show up for this keyword at all?
Citation. When it shows up, does it cite you?
Impact. When it cites you, does anyone click through, and does that click turn into a trial or a sale?

A rank tracker can tell you presence and citation. Almost nothing tells you impact on its own. You have to build that connection yourself, and that’s the part worth doing right.

Standard analytics tools were built around a simple model: someone searches, someone clicks a blue link, someone lands on your site. AI Overviews break the model in two places.

First, a huge share of searches now end without any click at all. Estimates put “zero-click” search sessions at close to 58% of all Google queries. The person got their answer and left. No visit, no session, no data point in Google Analytics.

Second, the clicks that do happen often don’t look like anything special in your reporting. If someone clicks a link inside an AI Overview, most setups log that as ordinary organic traffic. It gets mixed in with everyone else. You can’t tell an AI Overview referral from a plain old search result unless you go build a filter for it.

That second problem is the one you can actually fix, and it’s where tracking work should start.

Free ways to start tracking today

You don’t need a new subscription to get moving. Start with what you already have.

Google Search Console. Search Console’s “Search appearance” filters now break out AI-related search features separately from standard organic results, so you can see impressions and clicks tied to AI surfaces instead of guessing. If you don’t see it yet, check back monthly. Google has been rolling this reporting out in stages, and coverage keeps expanding. Google’s own announcement of expanded AI performance reporting in Search Console is worth reading if you want the details straight from the source: Google Search Central’s post on the new reports.

Google Analytics 4. Build a custom channel group for AI referral traffic. Most guides suggest a source match on patterns like google.*ai, chatgpt.com, and perplexity.ai, then placing that channel above standard Organic Search in your channel priority list so it doesn’t get swallowed. Once it exists, you can watch conversion rate for that channel the same way you’d watch any other source.

Manual spot checks. Pick 20 to 30 of your most important keywords. Search each one and record three things: whether an AI Overview appeared, whether you were cited, and who else was cited. Do this monthly. It’s tedious, but it’s free, and it catches things automated tools sometimes miss, especially for local or niche queries.

A simple citation rate formula. Take the keywords where an AI Overview appeared and divide the number where you were cited by the total. That’s your citation rate. Track it over time the same way you’d track average position. A citation rate that’s climbing means your content is winning the trust of the summarizer. One that’s flat or falling tells you something in your content structure needs work, which is exactly what our guide to ranking in AI Overviews walks through.

What to actually measure, on what schedule

Tracking everything at once is how tracking projects die. Break it into tiers.

Check weekly: AI Overview trigger rate for your priority keywords, your citation rate, and how often competitors get cited instead of you. These move fast and tell you if something changed.

Check monthly: Total estimated traffic impact from AI Overview appearances, which specific pages get cited most, and which new keywords started triggering AI Overviews that didn’t before. Content gets caught up in AI Overviews as topics evolve, not just when you publish something new.

Check quarterly: Whether any of this actually turned into pipeline. This is the one that matters most and the one teams skip most often.

The part that actually matters: does it make money

Citation counts and trigger rates are useful, but they’re vanity metrics if you stop there. A citation with zero downstream revenue is a nice screenshot for a slide deck and nothing else.

This is where most AI Overview tracking falls apart, because it lives in an SEO tool that has no idea what happened after the click. Someone clicks through from an AI Overview citation, lands on a blog post, reads for two minutes, leaves, comes back nine days later from an email, starts a trial, and converts to paid six weeks after that. An SEO rank tracker sees none of that. It saw one click and stopped watching.

Why it matters: That’s an attribution problem, not a search-visibility problem, and it needs a different kind of tool to solve. If your AI Overview traffic isn’t tagged and followed all the way to revenue, you’re guessing at ROI instead of measuring it. We wrote about this exact gap in how ad platforms guess at revenue instead of tracking it, and the same problem applies to organic and AI-driven traffic, not just paid ads.

Practically, that means connecting three layers instead of one:

Visibility layer: are you cited, and how often (rank tracker or manual audit).
Session layer: did the visit get tagged as AI-referral traffic instead of generic organic (your GA4 channel group).
Revenue layer: did that visitor eventually become a customer, and what was that customer worth (your attribution platform).

Most teams have the first layer. Some have the second. Almost nobody connects all three, which is exactly why “is our AEO work paying off” is such a hard question to answer honestly at most companies. If you’re evaluating tools for that third layer, our breakdown of attribution tools for 2026 is a reasonable place to start.

Dedicated AI Overview tracking tools, briefly

If manual audits and DIY dashboards aren’t enough, a handful of tools now track AI Overview citations directly, checking your target keywords on a schedule and reporting who gets cited. Pricing runs anywhere from around $30 a month for lighter tools up to well over $100 a month for platforms that bundle it into broader rank tracking. None of them close the loop on revenue by themselves. They tell you the SEO side of the story. You still have to connect that to what happens after the click.

Fine print note: AI crawlers hitting your site to source their answers show up in your server logs as bot traffic, not human visits. If you’re not separating bots from real visitors somewhere in your stack, your baseline traffic numbers are quietly wrong before you’ve even started measuring anything AI-related. We cover this in our piece on user agent tracking, and it’s worth a look if your traffic numbers have felt off lately.

A tracking routine you can actually keep up

Here’s a version that takes under an hour a week once it’s set up:

Monday morning, check your GA4 AI-referral channel for the past week. Note the session count and conversion rate. Once a month, run your manual keyword audit and update your citation rate. Once a quarter, pull every contact who first touched your site through an AI-referral session and see how many became paying customers, and what they were worth.

That last step is the one that turns “we think AI search matters” into “AI search brought us $14,000 in new revenue last quarter.” One of those gets budget approved. The other gets a shrug in a meeting.

FAQs

What is AI Overview tracking? AI Overview tracking means monitoring whether your web pages get cited inside Google’s AI-generated search summaries, how often that happens, and what effect it has on your traffic and revenue. It combines SEO visibility monitoring with the marketing analytics work of following a visitor from that first click to an eventual sale.

How do I know if my site has been cited in an AI Overview? Search your target keywords manually and look for citations under the AI-generated summary, or use a rank-tracking tool that flags AI Overview presence and citation status. Google’s Search Console also breaks out some AI-related search appearance data, which can surface citations at scale without manual checking every keyword.

Does Google Analytics show AI Overview traffic separately? Not by default. GA4 typically groups AI Overview referral clicks under standard organic or referral traffic. You can build a custom channel group in GA4 that isolates traffic from AI-related sources so it stops blending into your regular organic numbers.

Why did my organic traffic drop even though my rankings stayed the same? This is one of the clearest signs of AI Overview impact. When an AI Overview answers the query directly, fewer searchers click through to any website, including yours, even if your position in the traditional results hasn’t moved. Check whether AI Overviews now appear on your top keywords before assuming a technical or ranking problem.

Is it worth paying for a dedicated AI Overview tracking tool? It depends on how much organic traffic and revenue rides on the keywords in question. For a handful of important keywords, a monthly manual audit works fine and costs nothing. For larger keyword sets or competitive categories, a dedicated tool saves real time, but it still won’t tell you the revenue outcome. You’ll need to connect that data to an attribution platform to see the full picture.

How does AI Overview tracking connect to marketing attribution? Citation and traffic data only tell you that someone showed up. Attribution tracking tells you what that visitor was worth. Connecting the two means tagging AI-referral sessions distinctly, then following those visitors through to a sale, so you can report actual revenue instead of just impressions and clicks.

Attribution Windows Explained: Why Every Ad Platform Counts Your Sales Differently

Keith Perhac — Mon, 14 Sep 2026 09:00:00 GMT

An attribution window is the length of time a marketing touchpoint can still get credit for a conversion. Ad platforms measure it forward from the ad click. For example, Meta’s default attribution window is 7 days, so if someone clicks your ad today and buys 40 days later, that ad gets no credit for the sale. The attribution window you choose can impact which channels appear to drive your revenue.

That’s worth repeating: The attribution window you choose impacts the conclusions you can draw from your data. That’s why it’s so important to understand attribution windows (and select the right one for the analysis you want to do).

This article gives you the essentials.

What Is an Attribution Window?

Think of an attribution window like a timer. It starts the second someone interacts with your ad. It asks one question: “Did a conversion happen before time ran out?” If the purchase happens before the time runs out, the ad platform takes credit. If it happens afterward, the ad platform doesn’t take credit.

In the above example, you’ll note that the customer journey was a success. After clicking an ad (day 0), the user returned to the website (day 5), opted in via a lead magnet (day 25), read an email (day 35) and purchased via a webinar (day 40). Success!

However neither the 7-day or 30-day attribution window counted that conversion because it happened on day 40.

Only with lifetime attribution was this conversion correctly tracked back to the original ad. (More on how this works later.)

Click-through vs View-through attribution windows

There are two common types of attribution windows:

Click-through windows: How long after someone clicks an ad can a conversion be credited to that ad? This is usually the stronger and more actionable signal because the person took an explicit action.

View-through windows: How long after someone is served or views an ad — without clicking the ad — can a conversion still be credited to that ad? This window is usually short because an impression with no click isn’t great evidence that the buyer was influenced by the ad.

There are two key things to note here:

With both click-through and view-through attribution windows, the platforms start the clock and count forward. A long gap between the click and the purchase — normal for anyone with a real sales cycle — can push the sale right past the window, and the ad that started everything looks like it did nothing.
Ad platforms want to take credit for as many conversions as possible, regardless of where their ad click falls within the customer journey. For example, if a customer clicks a Meta ad three days before buying, Meta will take credit. Even if they had previously done months of research, downloading reports, reading emails, attending demos. And even if they followed that ad click with several more days of reviewing emails, webpages, webinars, and videos.

Attribution Windows vs. Lookback Windows

You’ll also hear the terms lookback window and conversion window. Some people and platforms use these terms synonymously. They’re closely related, but they’re measured from opposite ends:

Term	How it measures conversions	Often used by
Attribution window	Forward from the ad click	Meta / most ad platforms
Conversion window	Forward from the ad click	Google Ads
Lookback window	Backward from the conversion	GA4 / CRM tools

An attribution window asks, “Did they buy within ___ days after the click?” A lookback window asks, “which touches happened in the ___ days before this sale?”

For some businesses with very short and simple funnels, the “attribution window” and the “lookback window” might be the same. This is common in e-commerce where most sales go straight from an ad click to a purchase.

For businesses with longer and more complicated funnels, the attribution window and the lookback window might be different. This is common in B2B SaaS, coaching, info product and other nurture-dependent sales funnels.

In the above example, notice what the 7-day and 30-day lookback windows include when assigning credit for a conversion (and what they omit). Again, only the lifetime window includes all of the steps.

So, attribution window vs. lookback window? While these terms are not standardized, what matters most is the direction and the scope, not the label.

Ad platforms look forward from their ad. Google Analytics and CRM tools look backward from your sale. Attribution Engines (like SegMetrics) allow you to look in either direction. And each may use different window lengths.

How long is an attribution window?

Well, that depends. There’s no standard.

Each platform sets its own default attribution window. (This is another reason why Facebook, Google, and your CRM never tell you the same story… and why having a single source of truth is so important.)

As of 2026, here are the current default click-through and view-through windows for the most common ad platforms:

Platform	Default click-through window	Default view-through window	Max click window
Meta (Facebook & Instagram)	7 days	1 day	7 days
Google Ads	30 days	1 day	90 days
TikTok	7 days	1 day	7 days
LinkedIn	30 days	7 days	90 days
X (Twitter)	30 days	1 day	adjustable

What do you notice about these attribution windows?

They’re short! Most cap out well under 90 days. Meta and TikTok top out at 7 days.
They’re all different. This can make comparing ad performance between platforms difficult because you’re not counting apples to apples.

Fine print note:

Meta overhauled parts of its attribution reporting in early 2026. It removed several legacy attribution-window breakdowns—including the 28-day click breakdown from relevant Ads Insights reporting—and introduced a separate “engage-through” category for qualifying non-link ad interactions. Because available settings and definitions can vary by campaign and account, always confirm the current configuration in your own Ads Manager. See the official documentation for Meta attribution settings, Meta’s Ads Insights API changes, and Google Ads conversion windows for more details.

Who gets credit for a sale?

One clarification: the attribution window is not the same thing as the attribution model.

The window decides which touches are eligible for credit. The attribution model decides how those eligible touches split the credit. For example, a first-touch attribution model gives 100% of the credit to the first click while a linear attribution model evenly distributes credit across all touchpoints. But any touchpoints that occurred outside the attribution window won’t be considered by the attribution model.

Also, an attribution window is not the same as tracking.

Again, this is another reason why lifetime attribution is so critical.

What attribution window should you use?

As a rule, the longer your attribution window, the safer. You can always narrow a long window, but you can’t recover data that a short window never captured.

At a minimum, you should set an attribution window that is the real length of your sales cycle. This might be longer or shorter depending on your business.

The most accurate attribution window, to show you the full picture of your funnel, is lifetime attribution. This requires a third-party attribution tool that pulls data from all of your marketing tools (ad platforms, CRM, payment processor, email, etc.) and tracks all visitors on your site.

Frequently Asked Questions

What is an attribution window? An attribution window is the length of time a marketing touchpoint can still get credit for a conversion. Ad platforms measure it forward from the ad click. If the purchase happens inside the attribution window, the ad gets credit. If it happens after the attribution window closes, the ad doesn’t get credit. The window you choose can impact which channels appear to drive your sales. For a high-level overview of marketing automation, check out our Complete Guide to Marketing Automation.

What’s the difference between an attribution window and a lookback window? They define the eligible period for assigning credit for a conversion from opposite ends. An attribution window is measured forward from the ad click (“Did they buy within X days after clicking?”). This is how ad platforms frame it. A lookback window is measured backward from the conversion (“Which touches happened in the X days before the sale?”). This is the way many CRM tools frame it. Many people use the terms interchangeably, which can cause confusion.

Is a conversion window the same thing? Yes. “Conversion window” is Google Ads’ name for its attribution window. This is the period after an ad interaction during which a conversion can be credited to it. Different platforms, different labels, same underlying idea.

What is Meta’s default attribution window? As of 2026, Meta (Facebook and Instagram) defaults to a 7-day click-through window and a 1-day view-through window, and no longer offers a 28-day click window for optimization. For long sales cycles, that 7-day cap undercounts the ads that acquired the customer weeks or months earlier.

What attribution window should I use? Match it to your sales cycle and your question. Use a short window to see what triggered an immediate action, and a long (or lifetime) window to see where customers came from and what drove the eventual purchase. When in doubt, go longer — you can’t recover data a short window never counted. Be sure you have cross-device tracking so you can see the full customer journey, regardless of length.

See your full funnel. Not a 7-day slice.

Your customers don’t decide in a week, so your data shouldn’t expire in one. A lifetime attribution window shows every touch — from first click to purchase — no matter how long the journey takes and no matter which platform stopped watching.

Start your free 14-day trial of SegMetrics to unlock lifetime attribution for your business.

Introducing User Agent Tracking

Ryan Johnson — Tue, 11 Aug 2026 16:58:19 GMT

Half of all web traffic is bots. Now what?

When you look at your traffic numbers, how many of those visitors are actually real people?

More than half of all traffic on the internet today is bots. And that number is climbing. These bots probably aren’t hacking your site or stealing your data. They are, however, quietly messing with your marketing numbers.

Bots don’t buy. But they still pad your visitor counts, your page views, and sometimes even your lead counts. (Plenty of bots are perfectly happy to fill out a form.)

This is a real problem. Almost every business makes budget and channel decisions based on top of funnel metrics. If bots are quietly skewing those numbers, every decision you stack on top of them inherits the same problem. Pick the wrong channel to scale because the traffic looked good…and you might be funding a bot magnet for months.

When we checked this at SegMetrics, our bot-to-human ratio was about 1 in 5. That means 20% of our TOFU traffic was bots!

Introducing User Agent Tracking

Starting today, SegMetrics separates bots from real humans across your entire funnel.

With User Agent Tracking, you can see your own bot-to-human ratio. You can also break down your traffic by device type, operating system, and browser — and then filter it, segment it, or build reports around any of it.

Bottom line: “How many visitors did I get?” is the wrong question. “How many people?” is the one that actually moves money. Our User Agent Tracking lets you identify the humans in your funnel — and how they’re navigating your site.

See User Agent Tracking in action…

See who’s really there

Here are two checks to run against your own traffic this week. Each just takes a minute, but the answer could totally change your marketing strategy and budgeting.

1. Are your channels even real?

Before you crown a channel as your best performer, look at its bot-to-human split.

A channel that looks great on visitor count can tell a very different story once you strip the bots out. As in, same spend but half the actual people. You can’t optimize a metric that’s half fiction. So start by getting the truth.

2. Desktop vs. mobile on your conversion pages

Look at device type specifically on the pages where it counts: signup, webinar registration, checkout.

A lot of us still design for desktop first, even knowing better — which means the mobile experience on your highest-stakes pages might be worse than you think. If a chunk of your traffic is hitting that signup page on mobile and bouncing, you’re paying to send people somewhere that wasn’t built for them. This device breakdown is critical for advanced conversion optimization.

Try User Agent Tracking for yourself

Find out what your bot-to-human ratio is today.

Start your 14-day free trial today.

Data beats opinion. 🚀

Adrian Savage: Inside the future of email deliverability (Episode 14)

Ryan Johnson — Tue, 28 Jul 2026 16:54:49 GMT

Is anyone even seeing your emails?

Just because your email was delivered doesn’t mean anyone actually saw it. In fact, more than half of your new leads may never see a single email you send.

More than half. Let that sink in.

It’s not your subject line, offer or copy. It’s something happening silently before your email ever reaches an inbox.

In this episode of Data Beats Opinion, SegMetrics founder Keith Perhac talks with Adrian Savage, co-founder of EmailSmart and one of the sharpest minds in email deliverability. They dig into why the open rate you’re tracking is probably lying to you, the single most-overlooked engagement metric, and why blasting your entire list could be quietly wrecking the one marketing asset you actually own.

If email is core to how you sell — and it should be — this episode will change how you read your own numbers.

Watch the full episode →

Meet Adrian Savage

Adrian Savage is co-founder of EmailSmart, maker of the Deliverability Dashboard, where he helps senders diagnose email deliverability and visibility problems that mailbox providers won’t reveal. He’s analyzed thousands of email accounts and pioneered rolling 30-day engagement tracking as a way to measure list health that open rate alone can’t capture.

Connect with Adrian at EmailSmart.com or on LinkedIn.

Show Notes

[00:00] Introduction [00:53] Catching up with Adrian [01:34] How Adrian got into email deliverability [02:52] How email deliverability rules have changed [04:23] Why deliverability is such a confusing word [05:54] Why you can’t measure if an email is actually seen [08:31] Getting people to rescue emails from spam [12:44] Tracking engagement instead of guessing [14:43] The 30-day metric nobody tracks [16:50] A real launch that failed from bad visibility [19:41] Attention spans and the point of no return [22:21] Reply rates, sending addresses, and domain reputation [28:20] Cold outreach and staying under Google’s radar [30:44] The future of AI and personalized email [35:30] “List size is vanity. Engagement is sanity.” [40:06] Why AI can’t handle deliverability [44:18] Where to find Adrian

About Data Beats Opinion

Try SegMetrics Free

Start your 14-day free trial today.

Data beats opinion. Growth beats guesswork. 🚀

Transcript: Adrian Savage – Ep 14

Keith Perhac: What if more than half your new leads never see a single email you sent? And not because of bad copywriting, and not because of poor targeting, but because of something happening silently before your email ever reaches an inbox. And this horror story is true for lots of businesses, and they have no idea.

In this episode of Data Beats Opinion, we’re talking with Adrian Savage, co-founder of EmailSmart, and one of the sharpest minds in email deliverability, or really, as he prefers to call it, inbox visibility. We’re going deep on why the email metrics you’re watching are probably lying to you. Also, the number one thing you should be measuring but probably aren’t, and why emailing your entire list could actually be destroying that single asset that you’re trying to build.

If email is critical to your marketing, and it should be, this episode will change how you think about it. Let’s start the show. Adrian, super happy to have you. Hello. Like, this is exciting. We’ve, we’ve known each other forever, like way back in the day. Years and years. I don’t even want to think about how long it’s been. More than,

Adrian Savage: more than, more than 10 years. Mm.

Keith Perhac: Oh, yeah. Yeah. Definitely.

Adrian Savage: Yeah. A long time.

Keith Perhac: Yeah. I don’t… It’s, you’re one of those friends that I don’t even remember where we first met, it’s been so long.

And those are the best friends. So I’m, I’m super happy to have you on this, and- Man, email is, uh, it’s a-changing. Yeah. Ooh. So, uh, I think this is very apropos, and I’m really excited to, to talk to you about this.

Adrian Savage: Awesome. Let’s go for it.

Keith Perhac: Yeah. So for people who don’t know you, uh, give us a little bit of your backstory.

How… Email is, and especially email deliverability is such a thing, how’d you, how’d you get into this? Like-

Adrian Savage: Yeah, you don’t kind of wake up one morning and think, “Hey, I wanna be an email deliverability specialist.”

Keith Perhac: Unless you’re a masochist, like

Adrian Savage: And I’m, I’m not, definitely not. Um, so I mean, I got into tech when I was still in short pants.

I was about seven, I think, when my dad brought an Apple II computer home, if anyone remembers those. Um- Mm-hmm … and I read the manual and taught him how to use it. So that’s… I’ve been a geek for the last, what, uh, 45, 46 years, something like that. I’m showing my age a bit. Did the usual corporate stuff, escaped from that maybe 15 years ago.

Uh, my ex-wife very kindly moved to the other end of the country with my kids, and the only way I could keep seeing them was by quitting my job and going into the big, bad world of being an entrepreneur. So that was how I got into the world of email marketing, because I joined mastermind groups, and I was the only techy guy in the group, so everyone was like, “Hey, Adrian, how do I do my email marketing?”

And then a couple years later, it was, it was like, “Hey, Adrian, some people aren’t seeing my emails.” So that’s how it all started, is I just kind of stumbled across people that were having what turned out to be deliverability problems. Mm. And I just started learning about how to get the emails to land in the inbox.

Um, back then, 10, 12, 13 years ago, it was a very different, uh, landscape to what it is today. Back then, as long as you didn’t have certain spammy words in the subject, and as long as the server you were sending from was well trusted, then your emails would get through pretty much. Um- Mm-hmm … fast-forward to now, and there’s a million different rules that you gotta follow if you want your emails to be seen.

So it’s changed a lot in that time. I started off writing software to help people get their emails seen, and that’s, I think that’s how you and I first met when, when I launched WeDeliver back in 2015 or something like that. Um- That’s right, yeah … and then from there, as Google started to change their rules and, you know, back in the, the late kind of 2018, 2019 even, Google were using AI to decide whether to deliver emails or not.

Things changed so that rather than where you were sending it from, they started looking at what you as a sender were doing. Um- Mm-hmm … and it’s all evolved further from there, and it gets more complex. So one of my jobs is to stay ahead of the game there. Um, and I’m very lucky that Evan, who’s my co-founder of EmailSmart, he’s even more of a nerd than me when it comes to how emails land in the inbox.

Um, so that then gives me the time to focus on getting the best possible software to help people get their emails seen as well.

Keith Perhac: I think one of the hardest things about email deliverability for an end user, right, someone who’s not in this, is that it’s completely opaque, right? Yeah. We have no idea if our emails are getting through or not, and there’s, there are tools that help- But even they are like, they’re either super expensive or they’re…

We don’t know how to read them, right? Yeah. And so when we’re talking about email deliverability, like, what does that kind of mean? Because I think there’s, and I think you’d agree, there’s different layers of deliverability now that we have… Like you were saying, 15 years ago, there was no promotions box.

There was no notifications box. There was just this fire hose of, of content in your inbox, right?

Adrian Savage: Yeah, exactly. And this is why if I had access to the folks that, that maintain dictionaries and things like that, I would have them go and find the entry that says deliverability and scratch it out, never to be seen again.

Because nobody understands what the word actually means. Some people think it means delivery. Other people means has it landed in the inbox. Other people mean… think it’s, “Oh, it’s something that my email provider’s gonna take care of anyway, so I don’t care.” And this is why I hate that term, because it confuses people, it sounds incredibly techy, um, it’s very easy to just say it’s someone else’s problem, and so on.

So this is why I’m trying to focus a lot more on what really matters here, which is what, what I call inbox visibility. Mm. Is your mail landing in the inbox, and is it being seen? Because if your email isn’t being seen, it doesn’t matter how good your offer is, it doesn’t matter how amazing your audience is, it doesn’t matter how good your copy is.

If someone doesn’t see your email, you can’t make a sale. It’s as simple as that. So we don’t care if it’s been delivered or not, we care has it been seen. That’s what matters here.

Keith Perhac: What is the measurement of being seen? ‘Cause it can’t be open rate, because an open rate, now that they auto open emails and stuff, is a difficult number- Yeah

at that point. Yeah. So, like, what does being seen mean?

Adrian Savage: This is the challenge. You cannot measure it because the only people that know if your email has been seen or not are the mailbox providers themselves. The only person that knows… If I send something to your Gmail address, the only person apart from you that knows if you’ve read it or not is Google, and they sure as hell ain’t telling anyone.

So you can put as many tracking pixels and whatever else as you want in there, but as you just said, you know, Apple and even Google these days, they sometimes preload those things. So it looks like someone’s opened something even if they haven’t. So we can’t track opens like that. We can’t track clicks because let’s face it, if you send an email out and you get a 3% click rate, you have a party.

But that doesn’t mean that the 97% didn’t open the email. We just don’t know. Right. So I’ve spent a lot of time in the last five or six years trying to work out, well, what metrics can we come up with to infer whether people are seeing your email sufficiently or not, because we can’t measure it directly, so we have to come up with different metrics that help us get an understanding of what our audience is doing.

And based on what we can see, we have to kind of fill in the gaps because it’s a bit like SEO. Nobody knows what Google’s rules are, and email is just the same. Um, and anyone who says they’ve got this software that will tell you whether it lands in the inbox or the promotions tab, that’s great, but they’re just…

They’re mainly, with the greatest of respect, they’re selling snake oil because the only way you can tell if an email lands in the inbox or not is to look inside an email, inside a mailbox. Mm-hmm. And the problem is these people doing that, they’re either buying the data from real users that may not be representative of your audience, or they might be operating their own inbox, and all that can do is either open things or not open things.

And- Right … Google changes the behavior of whether the email lands in the inbox or not based on the behavior of each individual subscriber.

Keith Perhac: Right.

Adrian Savage: So if someone’s saying, “I can tell you if this lands in the inbox or not,” they’re only telling you that for that one specific mailbox they’re monitoring or the collection of mailboxes they’re monitoring, and they’re not gonna be real people normally.

And- Yeah … they’re either gonna open everything or ignore everything, and, you know, you can’t read anything into what someone else is telling you they are seeing.

Keith Perhac: It’s so personalized at this point, up to a point where we… And we don’t control that personalization- Right … at all, right? And but there are things I believe that you can do from the user perspective of the person getting the email that then would make those emails always come in, like responding to an email and stuff like that, like that type of engagement.

Is that still valid? Like- Oh,

Adrian Savage: yes, absolutely. Because, you know, I’ll, I’ll focus on Google because they are the biggest mailbox provider by at least a factor of two. Typically- Mm … Google will control more than 50% of all the addresses on your email list. If you’re a standard business to consumer or even business to small business, Google are gonna be in there.

So I’ll focus on them, but actually Microsoft and Yahoo that typically make up… You know, between the three of them, they make up 75, 80, 85% of an email list. They all have- Mm … similar rules. So when I talk about Google, I’m, I’m including everyone else in there as well. But what Google are looking at here is, well, what signs can I see that show that Keith is responding to Adrian’s emails?

And opening it is obviously one of them. Keeping the email open and scrolling through it is another one. Clicking a link is another one. Replying to an email is a really big signal. And then there’s other things, if you don’t do it, then that’s a negative signal. So if you ignore my email and leave it in the inbox unread, that’s a bad signal.

The worst possible signal is if my email lands in the spam folder for some reason and you leave it there. That’s the, that’s, that’s the worst.

Keith Perhac: Interesting.

Adrian Savage: Yeah. Because obviously Google will treat people with suspicion sometimes, and, you know, the biggest challenge is when someone opts into your email list the first time there’s a greater than 50% chance that that first email is gonna go into spam no matter how good your reputation is.

And if your recipient doesn’t rescue that from spam and mark it as safe, then they are unlikely to ever see another email from you again.

Keith Perhac: I mean, considering how much spam… Like, I have probably 10,000 spam emails- Yep, yep … in my spam folder, and it’s like I don’t go in and clean that up, right? Like, Google’s cleaning that up every 30 days when it deletes them.

Yep. But, like, if something gets in my spam, there’s a zero chance that I’m seeing it.

Adrian Savage: Exactly. So if someone signs up to my email list, the first thing I have to do, and this is my most important job, and this is why I never have an upsell on my thank you page. The only thing I care about when someone opts into my email list is please go and find my email and get it out of spam.

Keith Perhac: Way back in the day when we were doing work with I Will Teach, um, we had a script that would check the MX records of the person’s email address and see are they on Hotmail or, like you said, Gmail. ‘Cause everyone’s… Even if it’s their own domain, they… There’s a high chance they’re still using Gmail as the primary provider, right?

And we would give them a link to search for that, ’cause we knew what the subject line was. Mm-hmm. And we’d be like-

Adrian Savage: Mm-hmm … “

Keith Perhac: Looks like you’re on Google. Click here,” and it will open the email and search for it and go find it. Is that still a tactic you see that works? Is that, like… There, there’s so much that has changed in the years, right?

Adrian Savage: Yeah. I’ve, I’ve, I’ve… That’s, that’s something that I’ve never heard before. But as long as they’re using the web interface into Gmail, then that is, that’s, that’s absolute gold dust. So that’s exactly it, particularly if it’s making it to check the spam folder as well, because that’s the biggest issue is it’s not just finding it, but finding it inside the spam folder and marking it as safe.

Because Google is probably the most flexible with this, um, ’cause with Google you can create a rule that says, “If an email comes in from Keith at SegMetrics,” say, “never put it in spam, always put it in the inbox.” Mm-hmm. So if you wanna be that kind of hard-hitting with your audience, you can actually say, “Don’t just rescue it out the spam box and mark it as safe, but create a rule to say never send it to spam.”

Keith Perhac: Right

Adrian Savage: And that’s the, that’s the ultimate way, but unfortunately I would say 99 people out of 100 are never-

Keith Perhac: Yeah, not gonna do it. First of all, they probably don’t know how to do it. Yeah And second of all, they probably wouldn’t set that up, right? Exactly. But

Adrian Savage: w- but you know, we, we can, we can do the basic version of that, which, you know, Evan, my co-founder, he calls it modifying human behavior.

It’s training people to go into the spam folder, grab it, rescue it, mark it as safe, put it back into the inbox, and then if you tell them to reply to it as well, that gives that extra legitimacy signal to Google saying- Mm-hmm … Hey, this person hasn’t only rescued it from spam, they’ve actually replied, so they must be interested in this.

And the more things you can get people to do to engage with you at the time of opt-in, the more likely you are to get your emails delivered forevermore for that person.

Keith Perhac: So this is interesting because this is something that, you know, we’re, we’re all about data. We’re a- about understanding how much of this gets that engagement.

How are you tracking if people responded or not? So let’s say that you have that, that people signed up for the list. You’re like, “Go into your inbox, respond to this thing. I’m gonna give you a PDF or whatever,” like just something to tease them into, into engaging and responding to that. How are you then measuring that on the back end of this person responded, this person didn’t?

Adrian Savage: All we can do is just see if the open indicator is there or not and see if they clicked. That’s, that’s all we can do. And the, the time that my heart sinks is when someone asks me to troubleshoot an individual email visibility issue where they say, “This one person hasn’t got my emails.” And that’s the toughest thing to do in the world because we don’t really know what’s happening.

The only thing we can do with this is look at things at scale and look at the trends because, you know, okay, I mean, I’ve analyzed thousands of different email senders, so I’ve got an idea of what good looks like and what not so good looks like. To some extent, as long as the metrics are within a, a tolerance range, somewhere in the middle, so it’s like if, if the, if a typical open rate is somewhere between 45% and 60%, then it means people are doing okay.

If the click rate is, you know, 1, 2%, ideally higher, then that’s okay. And then you have to look at the trends because obviously we know that Apple are kind of, you know, they’re, they’re, th- they are distorting the figures because they’re making it look like a lot more emails were opened than really were.

But as… And, and this is why I expect to see an open rate of 50 to 60% these days because that includes all of the fake opens and things like that. So as long as it’s that range, it’s okay, but the thing that matters is the trend. Right. Because as long as you are fairly consistent with your opens and your clicks, you know you’re doing okay.

If they start to go down, you know you’ve got a problem, and that’s when you need to investigate. And that’s looking at kind of the simple stuff. But then one of the metrics that I look at that I don’t think anybody else does, I’ve, I’ve, I’ve yet to find software that does it, is looking at how the overall audience engages on a rolling 30 days.

Keith Perhac: Hm. Mm-hmm.

Adrian Savage: And what, what I’m looking at there is, well, how many people have opened at least one email in the last 30 days? How many people have clicked at least one email in the last 30 days? We, we can control that level because obviously if they’re not clicking or not opening, we can stop sending to them- Mm

and that will artificially inflate the percentage of people opening or clicking because, you know, I’ve always joked to clients that I can double their open rate overnight, and the way I do it-

Keith Perhac: Just by sending less emails.

Adrian Savage: Yeah. I just, I just delete half their contacts off their list that haven’t opened for a while, and suddenly-

the number of openers hasn’t changed, but the open rate’s doubled. Yeah. And here’s the thing, Google love you if you’ve got a high open rate because what Google wanna do is they wanna make money from the ads that they display when someone opens an email. If you’ve got a low open rate, you’re not making Google much money.

So it’s very much in your interest to only send emails to the folks that have recently opened something because they’re the ones that are most likely to open the next one.

Keith Perhac: Right.

Adrian Savage: And that’s what matters here because the other side of that as well is, and this is, you know, this is probably the, the least talked about metric ever.

The reason I’m really excited to be sharing this on this particular podcast is because I would expect that the majority of people listening to this will be fairly clued up about numbers and ROI and- Yeah … and how well things are performing. Because a lot of my clients spend a lot of money on ads, maybe-

Keith Perhac: Yeah

Adrian Savage: 10, 20, $30,000 a month is not uncommon. Now, if you are doing this right, you’re gonna be doing lead gen on the front end, capturing the email address, and then following up because we know that not everybody buys straight away. So if the follow-up emails aren’t getting through, then you are losing the ability to sell to those people.

And for a typical new client or new prospect that I look at, the metric that I look at is what percentage of new leads have opened something in the first 30 days? Now, it is possible to achieve 90% of people opening something in the first 30 days. The challenge is that most businesses, it’s less than 50%.

And one guy I spoke to last week, who’s coming on board as a client now, it was under 10% of his new leads- Oof … opened anything in the first 30 days. He was spending 30 grand a month on ads and he was wondering why his launch had gone from 100K last year down to 25K this year, and that was why. Nobody was seeing his emails.

Keith Perhac: That’s painful.

Adrian Savage: And he wasn’t measuring that metric, but most people don’t. So if there’s nothing else that you take away from this podcast, the one thing I’d encourage you to do is look at all the people that have joined your list in the last 30 days and work out the percentage of them that have opened something.

If it’s not 80 or 90%, then you’ve got some work to do, and similarly, you, you need to look at the percentage of new leads that are clicking. If, if you’re not getting 50% of your new leads clicking, then again, you’re leaving money on the table, you’re wasting ad spend. And, you know, Evan, my co-founder, he’s got a, a very well-known client in the coaching personal development space.

She is achieving 75% of new leads clicking, which is insane. That puts her in the top 1% of all of our people we’ve ever measured. That’s crazy. So it’s possible to get 75%. I would say that 50% is a reasonable target, and if you’re nowhere near that, then please get some help.

Keith Perhac: Now I kind of want to see what our, uh, what our numbers are.

So this is… it’s interesting because, you know, we have that partnership with Kit. Uh, Kit Insights is running on SegMetrics- Mm-hmm … uh, on our data platform- Yep … and everything and using our analytics. One of the first things that Nathan and the team came to me with was the most important thing for our customers is to understand the engagement of that list, and especially not only just in the 30 days, but continuing.

And we were like, “Yeah, that’s an easy thing to do,” but we had never thought of it in those terms before of, oh, this is something that just needs to be out of the box. This isn’t a report that you go build. This is like a core fundamental piece- Yeah … of how your marketing is doing is that engagement, especially in the 30 days.

And he was like, “Here’s what it needs to look like,” and he has this really cool graph that he sent me, and I’m like, “This is brilliant.” And so we built that out, and now it’s kind of in Kit Insights right now and the main SegMetrics of like, of the people on my list, how long from the day they are created to whenever they stop are they engaging with those emails, right?

What’s, what’s that engagement life cycle over time? And it’s interesting to watch that drop and drop and drop over time, but you’re exactly right. In that first 30 days, you really just have to have people completely engaged because it drops off so quickly.

Adrian Savage: And that’s, that’s the challenge. I mean, if someone has signed up to your email list, in theory, they are…

that is the most likely they are ever to engage with you. The only other time they’re gonna be as likely to engage is when they just bought something. Right. So that means you’ve got a, you’ve got a golden opportunity to actually get that engagement there and then. And if you don’t manage that in that moment, then you’ve lost them.

Keith Perhac: When you’re talking about that first 30 days and you’re talking about that f- 75%, that 90%, is that at the end of that 30 days or is that at some point? ‘Cause everyone’s opening probably the first email or the second email, but from the beginning to the end, how high do you wanna keep that engagement there?

Adrian Savage: I’m not actually measuring that, believe it or not, because all we’re looking at is just that average 30 days because-

Keith Perhac: Got it. Okay …

Adrian Savage: you know, I, I… What I have looked at is the 30-day average isn’t all that different from the seven days because as you said, if they haven’t opened something in the first few days, they’re probably never gonna open anything in most cases.

Yeah. So we’re measuring at 30 days because that’s how far back we’re looking as well, so that way then if there was a contact that was added 30 days ago, then that’s still catching those ones as well. But, you know, on the whole, I would agree that it’s getting them to open that first email is what matters, and then the hardest thing after that is to get them to keep opening because- Yeah

you know, normally in a typical… I know, I’ve, I’ve seen someone in the past, they had a 12 email welcome sequence, and by the time they got to email number 12, then the engagement rate had dropped off from like, you know, 70% open to the first one down to about 15% for the last one because it was- Yeah … it became irrelevant.

So the thing that is definitely different to years ago is that human attention has dropped massively, so you need to get, get to the point quicker, get your message out quicker, make the emails shorter, and make sure there’s always something to keep them engaged. Because the other challenge is that three or four years ago, people would keep opening your emails, and then they might stop for a while.

They might have a couple of weeks before they see your emails. They might have a month, and then they come back and open another one. These days, what I call the point of no return has dropped to about 13 or 14 days. Yeah So if someone hasn’t opened your emails for two weeks, the chances are they will never, ever open email ever again.

Keith Perhac: I actually see that with my own emails as well, the, like the emails that I get. Like, I’ll sign up for someone’s newsletter, it’s like, oh, I’m really excited about this, and I’ll read a few of them, and then life gets in the way- Yep … and then I stop reading, and after two weeks of not reading, it’s just like it’s sitting there in my inbox, and I’ll get to that later is like- It never happens

the killer of that. Yeah. It never happens. It never happens. The… So this is something, uh, Laura Roeder at, uh, MeetEdgar was talking about her, her nurture sequence for people, uh, when s- they join the list, and she’s like, “We give out whatever they opted in for, that magnet, and then it’s hard sell for 10 days.”

Adrian Savage: Yep.

Keith Perhac: And it is every single day, every, uh, every email is a hard sell, and then it goes into a soft sell after that. But she knows that if they do not make that sale in the first 14 days, that the chance of them continuing to engage and to do anything just drops like a rock.

Adrian Savage: Yep, totally.

Keith Perhac: I want to go back to one thing that you had mentioned before, which…

And I’m just maybe tugging at a thread that doesn’t exist, but that reply to I feel is such a valuable metric, and you said there’s nothing that tracks it. Why, why is… Can it be tracked? Is… If we could track it, would that be something good to track?

Adrian Savage: It’s, it’s useful to track, but here’s the thing. If you tamper with the reply address so that it goes into some kind of system that’s going to track it for you, then you’re immediately watering down the effectiveness of it because Google’s looking for a reply to the same address that sent it.

And beyond that, if you’re replying to some kind of software that’s tracking it, that likely won’t even be on your domain. So I know that ActiveCampaign used to do a reply tracking thing, and the reply to address became some- something @activecampaign.com- Oh, that’s horrible … or- Yeah, no. No So that doesn’t work.

Um, I know that some CRMs will take control of the outbound and the inbound email, and they can report at that level. So as long as you are using that as your primary customer communication tool, then that’s one way of doing it and measuring it, but- Mm-hmm … that will only work if you’re using a CRM or some kind of email platform that does actually handle all the incoming email as well- Right

and it’s got some kind of way of marrying up the subject line or some kind of ID in there or something because, you know, the only other way you can do it is just if you’re using some kind of, um, support system for the replies, then you can probably train it to look for particular subject lines and things like that and do metrics based on that.

But it’s not… It, it’s certainly not an exact science, but getting a gut feel for what your replies are is probably useful, but let’s face it, the reply rate is likely to be even lower than a click rate. Right,

Keith Perhac: which is already minuscule- Yeah … 3%, 2%. Yeah. Yeah. And so interestingly enough You know, y- you touched on something which is that the email that you are sending from, the email, the actual address, is generally different than your standard email address, right?

You’re not gonna be sending from Adrian at, most likely. Right. Um, same domain, but different email address. Yep. We do the same thing ’cause otherwise I get so many, uh, so many emails when, when the wrong email goes out. Does that email address then, are there things you can do for that email address or does it not even matter?

Like, ’cause I’ve seen outbound email addresses where they’re warming the domain and they’re having like bots talk to each other, blah blah blah, what- whatever. But if an email address is only for your marketing, does… Is that a limiter, right? You, you get what I’m saying? Yeah. I don’t know if I’m asking a smart or a dumb question.

Adrian Savage: It’s not that much a limiter because as far as Google are concerned, the domain is the domain. Um-

Keith Perhac: Got it …

Adrian Savage: now, if you start splitting things out into subdomains, so you might have a subdomain for your marketing emails, then that does start to have a little bit more of a bearing on it. Mm-hmm. But the, there’s what I call reputation bleed, which is whatever the main domain has as a reputation and the subdomain, there’s gonna be an overlap.

If you’re, if you’ve got a subdomain that you’re sending all your marketing from and you get loads of complaints and the reputation drops for that, it will still bleed back into your main domain. So you can’t completely protect things. And, you know, interestingly, I’ve seen cases where people have spun up a whole new domain, almost a burner domain if you like, to try and just, you know, send junk mail and things like that.

Keith Perhac: Mm-hmm.

Adrian Savage: When they’ve done that, you know, or they’ve done it because they’d already trashed their main domain and thought, “Oh, I’ll spin up a new, a new domain and that’s gonna solve my problems,” unfortunately it isn’t because Google is too clever for them. Google’s very good at joining the dots and working out, hey, so this person who’s created, um, SegMetrics.com and they’ve trashed their, their domain reputation, they’ve now got this new domain called welove, welovemetrics.com, but look, they’re talking about SegMetrics.

It must be the same thing. So within a matter of days- Mm … Google will link the domains together because the AI is all-seeing, and it doesn’t matter how you- Right … try and game the system and, and, and pull the wool over Google’s eyes, they are going to, they’re gonna spot that and you will see that the reputation of the new domain you’ve just spun up will very quickly crash down the same way that the original one did.

So you, you can’t game the system. I think that’s… You know, I mean, I, I’m gonna go off on a quick rant there about gaming the system as well because you see people selling software or solutions and say, “Put this magic code into your email and I’m gonna get your email out of the promotions tab into the inbox,” or whatever.

The crazy thing is it works. But it only works for a few days until Google work out what you’re up to. Yep. Because as soon as you start stuffing code or changing what’s hidden inside the emails, then that’s a technique that’s normally known as hash busting, and that’s something that spammers have been doing for years to try and make emails look like something that they’re not.

You know, kind of a- Mm-hmm … a wolf in sheep’s clothing, if you will. And Google and Yahoo, I think Y- Yahoo in particular, their, their main email guy, he posted on… There’s, there’s a, there’s a Slack group called Email Geeks, which is awesome, and there’s this guy, Marcel Becker, he posted in that publicly saying if he sees this pers- any time that Yahoo see this person’s software attached to someone’s sending, they will block that particular sender as soon as they find it, um, because- Oh,

Keith Perhac: gosh

Adrian Savage: they don’t agree with it. It’s, it’s, it’s gaming- Right … the system. So, so be very wary if you ever see these too-good-to-be-true solutions that are gonna add magic code in to save your emails because actually, yes, it will work because they, they sell the solution to you and it works for a little while, but then sooner rather than later, then- You get caught

you get caught out. There’s, you know, there ain’t no such thing as a free lunch in the, you know, there’s def- there’s no silver bullets in this game.

Keith Perhac: I mean, getting into, like, even SEO stuff, it’s the same thing. Like, if you suddenly make 1,000 pages with almost the same content, but it’s slightly spun, you’re gonna have a huge SEO boost-

Adrian Savage: Yeah

Keith Perhac: for about a month.

Adrian Savage: Yep.

Keith Perhac: And then it’s going to tank because Google’s gonna say, “Wait a minute, all these pages are exactly the same. They were created exactly the same time,” and, like, oh, look at this. And, and your, and your entire site may go down, right, in ranking because it, Google’s been, “Well, these guys are scammers.”

It’s, it’s very, it’s very frustrating. So I wanna, I wanna, um, I wanna touch on something that you said because I don’t, I don’t know if this is true or not, and I’d love to get your opinion ’cause I don’t know if people know this or not. So a lot of people, we also did cold outreach. We did the, okay, we’re gonna create seven new domains.

We’re gonna warm them up, and then we’re gonna do cold outreach through them. What you’re saying, though, is that, and correct me if I’m wrong, creating those domains, and if we burn that domain, it might affect our main domain because Google says- Yeah … oh, look, both of these domains have the word SegMetrics in them.

Yeah. They both have… They both talk about SegMetrics, and this one’s obviously spam. Yeah,

Adrian Savage: absolutely. There’s always that risk. Now, the way that people get away with cold outreach is by staying under Google’s radar. Mm. Because let’s face it, if a domain is only sending 50 emails a day out, Google aren’t gonna care about that.

Keith Perhac: Google doesn’t care.

Adrian Savage: Yeah. So that’s why people get away with cold outreach because as long as you keep the volumes low enough, you are less likely to get into trouble. I’m not saying you won’t get into trouble because- Right … you know, any, any kind of cold email, unsolicited email is still considered spam by Google.

It’s just that as long as you keep it low enough and as long as you can craft it in such a way that you’re less likely to get spam complaints and you’re more likely to get- good results- Mm-hmm … obviously, okay. So a lot of that is be very careful how you build your list of prospects you want to reach out to.

Be very careful how you word the emails. Make sure there’s something really compelling in there. So it’s, you know, so it’s, it’s the usual marketing principles. On top of that, then it’s just be very cautious and don’t send many of them.

Keith Perhac: It almost sounds antithetical to what a lot of the outbound people say, which is like, “This is a numbers game.”

So you want to be sending 5,000 emails a day or whatever, and I’m like, man, that just feels like, A, burning the domain, burning the list- Yeah … burning your reputation, and at some point it’s just like, okay, this is just spam, right? We’re not having a conversation. We’re not creating good content and good outreach.

Adrian Savage: We’re just spamming. It’s a numbers game until you get caught. But I, I guess the way the world is evolving, it’s becoming easier to make it more personalized. So I think, you know, there’s this, the… At some point, my prediction is that traditional email marketing is gonna disappear because it will be possible to personalize every single email that goes out, at which point the game’s gonna change again.

But the engagement trend is still gonna matter, it’s just we are going to be able to make it more possible to engage at an individual level rather than these email broadcasts and, you know, even the automations we’ve got today, they’re not exactly personalized, are they? There’s a couple of, maybe a couple of custom fields in there and a few bits like that.

But the more you can truly personalize each email you send, the more luck you have of, of a successful engagement, and that applies to cold email as well.

Keith Perhac: Man, there’s just so much, there’s so much to unpack here. And it’s interesting because you, you know, we’re getting into this, the zero-click marketing world right now where, um, Amanda from SparkToro was talking about the percentage of Google searches that turn into a click-

Adrian Savage: Mm-hmm

Keith Perhac: is now less…

It, sorry, 84% of Google searches do not end in a click is the, is the number right now. And she was talking specifically about owning your platforms. All, all the social platforms are locking down. You, they are changing algorithms at any point. They are changing what you can do with their data, and at the end of the day, the only thing you can control is your email list.

Adrian Savage: Yeah.

Keith Perhac: And, you know, you talk to people who have large email lists, uh, you talk to Brendan Dunn, you talk to Ramit Sethi, you talk to Eben Pagan, you talk to any of these people, I’m like, “Would you rather lose all your money or lose your email list?” And everyone’s like, “Lose the money.” Right? Like, w- because with the list, you can build that back because- Yeah

the list is the o- in this day and age, it’s the only thing you own. No one can take that away from you and show your stuff in a different way, and it’s probably the most valuable asset we have.

Adrian Savage: Absolutely. With, and I, I can’t see that changing because even, you know, my prediction is that a year from now, AI is gonna be controlling what people see in their inbox because you won’t get a traditional list of here’s all the emails you’ve received today.

Instead, there’ll be some kind of AI curated list saying, “Here’s the things you need to know today.” But the, the, the transport mechanism underneath that is still gonna be email. There’s still gonna be an SMTP connection from the mail platform to Gmail or something like that. It’s just what sits on top of it is gonna be different, and our role as marketers is going to be to change the way that we communicate and engage with that audience and measure the engagement because again, deliverability is gone.

It’s purely gonna be visibility, and it’s not just did your email get seen by the person, but can it get recognized as important by the AI? So there’s a whole- Right … you know, the same way that SEO has already transformed. Now we’ve got AEO, and- Mm-hmm … email is gonna go the same way. There’s no doubt about that.

So, and again, this is where I’m very fortunate to have Evan as my co-founder because he’s all over the like, he’s, you know, he’s all over that like a rash already. Um- Right … working out how, how we can advise people what they’re gonna need to do when Gmail launches their first AI inbox that’s in beta at the moment, and so on.

There’s, you know, we’ve got to be ready for the changes because the, the world is gonna change, and we have to keep up with it. The people that don’t, the people that… I mean, right now we’ve got people who are emailing like it’s 2012 still. You know, they’ll build the biggest list they can, and they’ll just mail the hell out of it until people buy it, they die, or they unsubscribe, and they wonder why they’re getting filtered all the time.

Now, those, those folks, when AI comes in, they’re gonna be even more dead than they are. But the people that are still being, they’re, they’re being pretty good by today’s standards, they’re gonna need to evolve as well. You know, on the one hand, Google is gonna make life more difficult because they’re gonna enforce things like quarantining and things like that- Mm-hmm

that they’ve come at some point. And if people aren’t reporting on the technical side of their emails at that point, then they’re gonna, they’ll have no idea what emails are getting through and what emails aren’t. And then beyond that, it’s like, okay, so we will do all the things to keep Google happy at the technical and the kind of the, the compliance level.

But then from a content perspective, how do we craft our emails so that the AI will recognize the relevance to the audience enough that they will show it to them? And then we’ve still got to get our audience to want to read it, open it, and, and act on it. AI is making it so easy to create the content now that r- when someone used to get 100 emails a day, they’re now getting 1,000 emails a day.

So we-

Keith Perhac: I, I was at a conference last week, and I came back to 520 emails. Oof. So yeah, and not even the promotions one. Like, that was in the inbox- Yeah … 520 emails, and most of them are crap. But like, it’s just, it’s so much. I actually use an AI assistant to scan my emails, see what I need, like categorize them.

Is this urgent? Is this like an update? Blah, blah, blah. You know, we talk about that email like 2012, right? And it’s interesting ’cause I’ve worked with people with giant lists, I’ve worked with people with small lists, and the value per person is so different. So if you have a million, two million people on your list, they’re probably worth a penny a piece, right, on average.

Your lead value for anyone on your list is probably a penny. And I’ve talked to people who have 20,000, 30,000 people on the list, and their value per person is like $79.

Adrian Savage: Yeah.

Keith Perhac: Right? Like-

Adrian Savage: Ah, I’ve got, I’ve got a distinction for you there as well, Keith, okay

Keith Perhac: Oh, yes please.

Adrian Savage: Obviously a lot of people look at their list size, and you know that term, there’s…

What is it? Uh, revenue is vanity, profit is sanity. Um- Mm.

Keith Perhac: Mm …

Adrian Savage: now in the world of email, list size is vanity, engagement is sanity. So they might talk about a 30,000 list, but what percentage of those are engaged? Now, if your entire list is engaged, the value per lead or the value per contact on that list is fi- between five times and 10 times more than if you have an unengaged list.

So it really is in your interest to make sure you are managing that engagement really tightly and only mailing those folks that have opened recently because the value of your list grows by an order of magnitude. It’s not just like a 2X increase, it is five to 10 times.

Keith Perhac: Do you think that it’s FOMO? Like, it, it’s almost like a hoarding, isn’t it?

Yep. Because people are like, “Well, it doesn’t hurt to send it to someone who hasn’t opened in six months, and maybe if they open it and buy, that’s an extra 50, 100, $300,” right? It’s- Yeah,

Adrian Savage: and that’s a mindset people have, and I have to remind them of two things. Firstly, that Einstein’s rule of insanity, which I’m sure you know what that one is.

Keith Perhac: Is it the one to do the same thing, uh, and expect different results?

Adrian Savage: You nailed it. So if, if someone hasn’t emailed the last 25 emails you’ve sent them, then what in the name of all things sacred means they’re gonna open the next one? It is not gonna happen. It’s as simple as that. So that’s the first thing.

Um, and then the other side of that as well is if you’re mailing the people that haven’t engaged for weeks or months or whatever, then we’re already saying that Einstein’s definition of insanity is doing the same thing, expecting different results. They are not gonna open that next email. But this is where we go back to what I said about the point of no return.

We have got the data now that for a typical list, if someone hasn’t opened an email in the last 14 days, 16 days, something like that, they’re gone forever. So there’s just, there’s no point. Um, one of the tools that I released this year is called My Reputation Checker, and one of the things it’s got in there, kind of a by-product of what I built, is what I call the sending behavior and the results of that.

So on a day-by-day basis, I can plot a graph showing how recently did they engage from green through to red. So green is the last seven days, yellow is the last 30 days, then it goes to different shades of red. Oh, I like it. And there’s a separate graph that shows how many people opened those emails. And the headline news is this: You can barely ever see any of the red folks opening anything.

Hardly ever. There’s specific cases where you might, but on the whole you’ve got to accept that if someone hasn’t opened in the last 30 days, there’s, there’s, there’s more chance of there being, you know, a second moon up there around, going round the Earth than- … than that email getting opened. So it’s like why- Agreed

why, why bother, why bother doing it?

Keith Perhac: So for someone who is not in this every day- What should they be doing to see, oh, I have a problem, I need to go talk to Adrian, or I have no idea, or I’m doing great? Or like, what’s, what’s kind of the cadence on that? You

Adrian Savage: need to be checking monthly as a minimum. Um- Okay

and preferably weekly. I mean, it’s, it’s crazy. One, one of, one of the, one of the software tools we’ve got, we call it Smart Alerts, and it emails people when something falls off a cliff. But guess what? 97% of our users haven’t set that up. So i- How

Keith Perhac: many people are off the cliff at the beginning, though?

That’s, that’s the other question. It’s,

Adrian Savage: it’s, it’s not as many as it used to be. I think there’s a lot more email savvy senders out there now. But-

Keith Perhac: Mm-hmm …

Adrian Savage: yeah, I think, you know, this is why I’m, I’m very keen to make it easier for people to spot the problems and spot where they’re at. Okay. I’ve got a lot of new ideas that I’m working through at the moment.

They’re gonna make it easier for people to just get a, a better idea of how well their visibility is shaping up. So that way then as the world does start to shift, you know, I think it’s gonna shift very quickly over the next year, I wanna make sure people have got the right tools to make it easy for them to check it without having to be a, a deliverability nerd.

They can just literally say, “Right, tell me if there’s a problem with my visibility,” and then we’ll just send them an email when something goes wrong.

Keith Perhac: I think this i- this is interesting ’cause, you know, we, we were talking a little bit about before we started about AI and how it’s affecting software and how it’s affecting-

Adrian Savage: Yeah

Keith Perhac: the world in general, like content and email and everything, and I think a lot of the benefits that you and I both have in this new kind of AI-focused world is that these are hard things to do, and they’re not things that AI, A, can do now-

Adrian Savage: Mm-hmm …

Keith Perhac: and probably won’t be able to do later on because they have to have this structure behind them.

And as I have found out with my AI, just tossing data into AI, they are lazy bastards who, uh, will not, will not follow directions and just make up stuff constantly. Yeah. Yeah. And so it’s interesting to me ’cause this is, this is something that is so- key and fundamental to all of our businesses. Email is the fundamental way to build and market our types of businesses- Yep

creators, coaches- Yep … SaaS even, and it’s not something that you can stick an AI at and go like, “How’s it going?” Right? Because there is so much behind the scenes that needs to be understood and calcul- and it’s always changing, and

Adrian Savage: yeah.

Keith Perhac: Interesting

Adrian Savage: you say that because I’ve had a, a lot of people now saying, “Oh, I’ve asked Claude what to do to help my deliverability.”

And I hate to say this, but what it comes up with is third rate at best. Even, you know, well, I, I have tried training different AI models with some of my own frameworks and tools, and it still gets it wrong.

Keith Perhac: Yeah.

Adrian Savage: So someone without that detailed training and context to start with is gonna struggle even more.

So, you know, it gets a few bits right, but in some cases it, you know… I’ve seen one case where the AI was telling people to do things that was actually gonna tank their deliverability and not improve it. You know,

Keith Perhac: you should, you should stuff your email with all of the, all this extra metadata.

Adrian Savage: Crazy.

Keith Perhac: We, we had the same thing.

So we were doing… I, I was testing out some AI analytics, ’cause everyone does, and I tossed in about 800 records of trials that had come in, and I was like, “Analyze these records. Let me know how, where they’re failing on these onboarding, or, like, what’s the biggest trouble that they’re having,” blah, blah, blah, just looking at the raw data, right?

So I uploaded CSVs, and it had this weird number in it, and I was like, “That’s a, that’s a really weird number. Why? What’s with that number?” And it said, “Oh, you uploaded 800 records. I just looked at the first 10.” And I’m like, “You did what?” Like…

Adrian Savage: Yeah. Like you say, lazy.

Keith Perhac: It is. It is, and that’s… It’s not smart.

It’s just taking in It’s just regurgitating everything, and sometimes that’s great. But if you’re looking for something that is constantly changing, like email deliverability, if you’re looking at something that is so nuanced, like email deliverability, like it just, it can’t keep up and it’s going to lead you astray.

Adrian Savage: Yeah, totally.

Keith Perhac: Right? Like, I wanna finish this off with one final question, and it, this is supposed to be lightning round, but I, I have a feeling this might go a little bit long. What’s the biggest email mistake that you see businesses make? Like, w- what’s, what’s the one thing that you see people time and time again and you’re just like, “Oh my God.

Why are you doing that?”

Adrian Savage: It’s very simple actually. People don’t realize that every single email you’re gonna send is either gonna help your reputation or harm it, and people don’t realize that. They see their entire email list as an asset, whereas if you’re mailing the wrong people on your email list, it becomes a liability.

So it’s as simple as that. You know, mailing the wrong people is the biggest mistake, and the wrong people are the ones that haven’t opened for a while.

Keith Perhac: It, it, it ties in a lot with what I’ve been seeing and thinking, especially in the realm of AI content, where people have removed that personalization.

Yeah. Everyone’s just a number. I have a list of 50,000 or 100,000 whatever, and I’m just looking at those percentages. I’m not looking at the people behind them. Mm-hmm. I’m not making sure, am I sending to people who are engaged, or am I sending this message to the right customer demographic within my list?

And the, the more specific you can be and the more targeted you can be, the better o- opt-in rates you’re gonna have, the better open rates you’re gonna have, the better everything you’re gonna have.

Adrian Savage: It’s… It is, it’s, it’s always just about, you know, sending the right things to the right people at the right time, you can’t go wrong.

Keith Perhac: Yes. It’s, it, it is 200, 300-year-old advice, and it’s still the main piece of business marketing- Absolutely … and we just keep forgetting it. And everyone, like, comes back to it and they’re like, “You know, we should really segment down and send our emails to only the people that are, that are really interested.”

I’m like, “Yeah. Yeah, you thi- yeah, you think?” Like, oh. Adrian, thank you so much. It, this has been such a pleasure. My pleasure. I always love talking to you. Where can people find you and, uh, got anything interesting for them?

Adrian Savage: So emailsmart.com is the place to go, and in the next few days, so probably by the time this, this podcast is visible, there’s gonna be something new called the Visibility Center.

Um, and that’ll be front and center on the EmailSmart website at that point. Um, I’m not gonna say any more than that, but it is gonna be something big and something very important, and it’s gonna be free. So that will be the thing to look out for, and I’m sure we’ll have to make sure that we share the, the URL of that and a little more details when the podcast gets published.

Keith Perhac: It will be in the, the notes below, and we’ll include that, and then we’ll send that out in email and hope that it gets delivered.

Adrian Savage: Awesome. We will make sure it does.

Keith Perhac: Adrian, thank you so much for your time. Yeah. It’s a pleasure. And, uh- Oh, yeah … as always-

Adrian Savage: Great …

Keith Perhac: have a great one.

Adrian Savage: Thanks.

Keith Perhac: Cheers.

Why Use Marketing Attribution Software When AI Can Just Pull My Data?

Denise Cullom — Mon, 27 Jul 2026 18:46:27 GMT

Short answer: AI can reason about marketing data. It cannot record it. So will AI marketing attribution work for me? A language model cannot tell you that today’s buyer first clicked a Facebook ad 90 days ago, because nothing in your Stripe, CRM, or ad platform exports contains that connection. Someone has to capture and store the link at the moment of the click, months before the sale exists. Attribution software is that recording layer. AI is the reasoning layer. We tested this against our own library of 174 marketing analytics prompts: about a third can be answered by AI working from exports alone or MCP server connections, and roughly half cannot be answered at all.

Can AI do marketing attribution?

Partly. AI can analyze attribution data extremely well. It cannot create attribution data.

The distinction matters because attribution has two separate jobs, and people collapse them into one:

Recording. Capturing every click, visit, opt-in, and purchase, and tying them all to one identifiable person over weeks or months.
Reasoning. Reading that record and telling you what it means and what to do next.

AI is now better at the second job than most dashboards and many analysts. It is structurally incapable of the first. Not because models are not smart enough yet, but because the information has to exist before anything can reason about it.

What can AI actually do with your marketing data today?

More than most attribution vendors will admit. Here is the honest list.

Interpret and explain. A dashboard hands you a number. AI tells you why it moved and what to do about it, in plain language, formatted for whoever needs to read it.

Answer questions nobody built a report for. Every reporting tool answers a fixed set of questions. The one you actually have on a Tuesday night is usually not in the set. AI has no fixed set.

Work from exports. Drop a Stripe CSV into Claude or ChatGPT and you can get revenue trends, MRR, churn by plan, cohort tables, refund rates, and average order value. This is real, and it is genuinely good.

Remove the interface. No dashboard to learn, no date picker, no export to wrangle.

If you were using attribution software mainly as a monthly revenue report, AI replaced that. We would rather say so than pretend otherwise.

Why can’t AI tell me where a customer came from?

Because the connection between the first click and the eventual sale does not exist in any of your systems unless something recorded it at the time.

Walk through a normal journey for a coach, course creator, or SaaS business.

In April, someone taps a Facebook ad on their phone. They read the page and leave. In June, they Google a question, land on your blog from a laptop, and join your email list. In July, they click a link in your nurture sequence and buy a $997 program.

Now look at what each system holds when you go to export it.

System	What it knows	What it does not know
Stripe	A charge succeeded in July for an email address	That April or June ever happened
Your CRM or ESP	An address subscribed in June, clicked a link in July	That a Facebook ad existed
Facebook Ads	An ad got a click in April from an anonymous device	Any email address, any purchase
Google Analytics	Sessions occurred, aggregated	Which session belonged to which customer

Read that table again and look for the join. There isn’t one. No shared key connects the April click to the July charge. The Facebook click carries no email address. The Stripe charge carries no click ID.

Hand all four exports to an AI and ask which channel drove the sale. It can only work with what is in front of it, and what is in front of it says the email sequence drove the sale. That is last-touch attribution wearing better prose. Facebook gets zero credit for starting the whole thing, and you cut the ad budget that was working.

Will a smarter AI model fix this?

No. This is a data availability problem, not a reasoning problem.

The link between the April click and the July sale had to be created and stored in April, before anyone knew a sale would ever happen. No amount of intelligence recovers information that was never written down.

A model reading your Stripe export in 2028 will be far more capable than one reading it today. It will still be reading a file that does not contain the click.

You cannot reason your way to a fact nobody recorded.

We tested this against 174 real analytics prompts

We publish a library of 174 marketing analytics prompts in 18 categories, built for asking real questions of real marketing data. So we ran an audit on our own library. For each prompt we asked: could an AI answer this correctly using only Stripe, CRM, ESP, and ad platform exports, with no attribution recording layer underneath?

Three outcomes:

Answerable. The exports contain everything needed.
Misleading. AI will return a confident answer that is wrong in a predictable direction, usually by crediting the last touch.
Impossible. The data required does not exist anywhere.

Category	Prompts	Answerable	Misleading	Impossible
Cohort analysis	7	7	0	0
Sales team and demos	10	9	0	1
Subscriptions and churn	12	10	1	1
Trial funnel	8	5	1	2
Product and offer analysis	9	5	2	2
Audience and segmentation	8	5	0	3
Forecasting and trends	9	5	2	2
Revenue and LTV	13	5	2	6
Funnels	10	3	3	4
Email attribution	9	2	2	5
Lead quality	8	2	1	5
Reporting	10	1	4	5
Setup and config	8	0	0	8
Attribution	11	0	0	11
Ad performance	13	0	3	10
Affiliate and partner tracking	8	0	4	4
Agency and multi-account	7	0	1	6
Diagnose	10	0	3	7

Roughly a third of the prompts work with AI alone. Roughly half are impossible. The remainder produce an answer that looks credible and points you the wrong way, which is arguably the worst outcome of the three.

The pattern in that table is the whole argument

Look at which categories score well and which score zero.

AI alone does fine when the question is about money and time. Subscriptions, churn, cohorts, MRR, refunds, average order value, retention by plan. All of it lives in Stripe with timestamps attached. Ten of twelve subscription prompts work. Seven of seven cohort prompts work.

AI alone scores zero when the question is about origin. Six categories return nothing usable: attribution, ad performance, setup and config, affiliate tracking, agency reporting, and diagnostics. Every one of those is a question about where a person came from and what they touched on the way in.

That is the line. Not intelligence. Not model quality. Whether the answer depends on something being recorded at the moment of the click.

What does attribution software actually do that AI can’t?

It runs the recording layer. In practice that means:

A first-party script that fires on every visit and assigns a persistent visitor ID
Capture and storage of click IDs (fbclid, gclid, ttclid) and UTM parameters at arrival, not at checkout
Cross-device identity resolution so a phone visit and a laptop purchase resolve to one person instead of three
Cookie persistence that survives Safari’s seven-day cap and browser privacy defaults
Server-side tracking so ad blockers do not silently delete a third of your data
An identity step binding the anonymous visitor to an email at opt-in, then to a customer ID at checkout
A database holding all of it across your full sales cycle, which for most infoproduct and coaching businesses runs 60 to 180 days
Maintained integrations for the week Stripe changes an endpoint or your CRM rotates its auth model

None of that is analysis. All of it is plumbing, and every piece has to be running before the customer you care about ever arrives.

Can I use AI and attribution software together?

Yes, and this is the version that actually works. The SegMetrics MCP Server connects your live SegMetrics account to Claude, ChatGPT, Gemini, or any MCP-compatible AI tool, so you keep the plain-English workflow and put real journey data underneath it.

Setup takes about a minute. You copy your MCP Server URL from Account Settings, add it as a connector in your AI tool, and authorize through OAuth. Full instructions are in the MCP Server setup guide, and the launch announcement covers why we built it.

Three things worth knowing:

It is read-only. The AI can pull and analyze your data. It cannot change anything in your SegMetrics account or your connected tools.

Every answer links back to its source report. You are never taking the AI’s word for it. Open the underlying report and check the numbers yourself, right down to the line-by-line activity ledger.

It investigates rather than retrieves. The server exposes eleven tools across 16 report types and 370+ metrics. Some let the AI discover the shape of your business (products, tags, segments, traffic sources) and others let it query. Ask “why is my webinar funnel underperforming” and it chains several queries: check your integrations, find webinar-related segments, look at where registrants came from, run a funnel report to locate the biggest drop-off, drill into contacts at that stage, then compare against a prior period.

The difference in what you can ask:

Stripe export plus AI: “Which email campaign preceded the most purchases last quarter?”

SegMetrics plus AI: “Of the customers who bought in July, what was the first channel that ever brought them to my site, how many days passed between that first touch and the purchase, and what is their lifetime value by that first channel?”

Same conversational workflow. The second question is answerable because something recorded the first touch and kept it.

Can I build my own attribution with Claude Code?

Yes. Some people should, and we would rather say so than pretend the option does not exist.

Claude Code will write you a working pipeline: API pulls from Stripe and your CRM, normalization, a Postgres schema, a dashboard. What used to be a three month contractor project is now a serious weekend.

Two things to weigh before you start.

The code is the easy part. The hard part is the Tuesday in November when a provider deprecates an endpoint and your numbers go quietly wrong for three weeks before anyone notices.

Click data is the one thing you cannot backfill. Records that already exist elsewhere can be imported. SegMetrics has historical data access for exactly this reason, so your orders, contacts, tags, and email engagement come in with their original dates and you get insight from day one. What cannot be recovered is the ad click nobody captured. Facebook does not store an fbclid for you and hand it over later. If you start recording clicks today, your first trustworthy multi-touch answer arrives one full sales cycle from now. For a 90-day consideration window, that is a quarter of spend decisions made on nothing.

When do you not need marketing attribution software?

Some people ask this question and the honest answer is that they should skip it. If any of these describe you:

You run one channel. If everything comes from one podcast or one newsletter, there is nothing to attribute.
People buy in the session they arrive in. Impulse ecommerce with no consideration gap. Last click is close enough to true.
You spend nothing on ads. Attribution mostly buys better spend allocation. No spend, no decision to improve.
You only need subscription metrics. MRR, churn, retention, and cohort LTV all live in Stripe. Our own audit found AI handles those categories nearly perfectly without any attribution layer at all.
Your volume is too low for the math to mean anything. Twelve sales a quarter will not produce a reliable pattern. Go get more customers first.

Attribution software earns its cost when you run multiple channels, spend real money, and your customers take weeks or months to decide. That is when the gap between what you believe and what is true starts costing you.

How do I know if I need it?

Look at your last twenty customers. For each one, name the first thing that brought them to you.

If you can do that and you are confident you are right, you do not need this.

If you are guessing, the guessing is the thing you are being asked to pay for. An AI reading your Stripe export will guess the same way you do, just faster and with more confidence.

Frequently Asked Questions

Q. Can AI replace marketing attribution software?

A. It can replace the reporting and interpretation layer. It cannot replace the tracking and identity resolution layer. If you were using attribution software mainly to read a monthly revenue chart, AI does that better and cheaper. If you need to know which channel originally brought in a customer who bought 90 days later, AI has no access to that unless something recorded it.

Q. Why can’t ChatGPT or Claude tell me which ad drove a sale?

A. Because no export contains the connection. Your Stripe charge has an email address and no click ID. The Facebook click has a click ID and no email address. Nothing in either file links them. The AI has to either guess or default to the last touch it can see, which is usually an email.

Q. What is the difference between AI analytics and marketing attribution?

A. Marketing attribution is the practice of recording and connecting every touchpoint in a customer journey to one identifiable person. AI analytics is the practice of interpreting data you already have. Attribution produces the dataset. AI reads it. They solve different problems and work best together.

Q. Can AI calculate true ROAS?

A. Only with attributed revenue underneath it. Ad platforms report their own conversions, which they have an incentive to over-count and which miss anything outside their attribution window. Without a recording layer, AI can compute spend and clicks accurately and will get cost per acquisition and ROAS wrong. In our prompt audit, none of the 13 ad performance prompts were fully answerable from exports alone.

Q. What is the SegMetrics MCP Server?

A. It is a read-only connection between your live SegMetrics account and any MCP-compatible AI tool, including Claude, ChatGPT, and Gemini. You ask marketing questions in plain English and the AI queries your actual attribution data to answer them, linking back to the source report for every finding. Setup instructions are in the help center.

Q. Do I need a paid AI plan to use it?

A. Yes. You need an active SegMetrics account and a paid plan on an AI tool that supports MCP connections, such as Claude Pro or ChatGPT Plus.

Q. Is it safe to connect my marketing data to AI?

A. The SegMetrics MCP Server is strictly read-only. It can pull and analyze your data but cannot modify anything in your SegMetrics account or any connected tool. Every answer links back to the underlying report so you can verify each finding rather than trusting the AI.

Q. Can I just build my own attribution system with AI coding tools?

A. You can build the pipeline. What you cannot do is backfill the click data. Records that live in other systems can be imported with their original dates. Ad clicks that were never captured are gone permanently, so your first reliable multi-touch answer is one full sales cycle after you start recording.

Q. How long does attribution data take to become useful?

A. Roughly one sales cycle. If your customers typically take 90 days to decide, you need 90 days of recorded touchpoints before multi-touch answers mean anything. This is why the cost of switching or cancelling is higher than it looks.

Q. Which is better, AI or attribution software?

A. The question assumes they compete. They handle different halves of the same job. Attribution software records what happened. AI explains what it means. Used together through an MCP connection, you get the conversational workflow with a real dataset behind it.

Want to see it on your own data? Start a 14-day free trial, connect your tools, and point your AI at it. Or book a demo and we will map your specific customer journey with you.

Your Forms Know Who Filled Them Out. Now They Know Who Turned Into Revenue.

Denise Cullom — Thu, 16 Jul 2026 19:05:44 GMT

Most lead-gen forms are great at one thing: counting responses. You get a number, a spreadsheet fills up, and everyone nods at the growing pile of submissions.

But a submission is not a sale. And a form that collects 200 leads a month tells you nothing about which of those leads actually became paying customers, or which campaign sent them your way in the first place.

That gap is exactly what we set out to close with our newest integration.

Weavely Integration with SegMetrics

A Weavely user asked whether the two platforms could talk to each other. So the Weavely team built it, and we are glad they did, because the pairing solves a problem that shows up in nearly every marketing stack we look at.

Weavely is an AI form builder. You describe the form you want in plain language, or hand it a document or voice note, and it produces a polished, working form in seconds. It handles multistep flows, conditional logic, answer piping, and, most relevant here, lead qualification quizzes.

SegMetrics does the part that comes after the form. We connect every submission back to the campaign, channel, and touchpoint that produced it, then follow that lead all the way to revenue.

Put the two together and your forms stop being a place where data goes to sit. They become the front door to a revenue attribution engine.

Where the lead quiz functionality changes the game

This integration is more than a data pipe. It pairs especially well with Weavely’s scoring quizzes.

Here is the difference. A standard form captures contact details. A scoring quiz captures intent. It asks the questions that separate a curious browser from a serious buyer, then scores and segments the respondent right inside the form.

On its own, that scoring tells you who looks promising. What it cannot tell you is whether those promising-looking leads ever paid you, or which of your channels is quietly delivering them. That is the piece SegMetrics adds.

So the flow becomes:

A visitor lands on your Weavely quiz and gets scored and segmented at the moment of capture.
That submission flows into SegMetrics, tagged with the full attribution story behind it.
You see not just which leads scored well, but which ones turned into revenue, and which channel earned the credit.

You are no longer guessing whether your high-scoring leads are the ones actually opening their wallets. You can see it.

What you can do with the two together

With Weavely and SegMetrics connected, you can:

Qualify leads at the point of capture. Weavely’s scoring quizzes sort and segment respondents while they are still filling out the form, so a lead arrives already ranked.
Attribute every submission to the campaign that drove it. SegMetrics ties each form fill back to its true source, first touch through last, instead of collapsing everything into a vague “direct” or “referral” bucket.
See which forms and which leads actually generate revenue. Not responses. Not clicks. Revenue. You will know which lead-gen forms pay for themselves and which ones just look busy.

Why this matters for how you spend

The reason attribution is worth the effort is simple: it tells you where to put your next dollar.

If you are running lead quizzes across several channels, some of those channels are sending you leads that convert and some are sending you volume that never pays off. Without revenue attribution, both look identical in your form dashboard. A lead is a lead is a lead.

Once Weavely data flows into SegMetrics, that illusion breaks. You can finally separate the channels that deliver revenue from the ones that only deliver responses, then move your budget accordingly. That is the whole point of measuring in the first place.

Getting started

If you are already capturing leads with Weavely, connecting it to SegMetrics takes just a few steps, and it is especially worth doing if you are running scoring quizzes. Channel your form data from Weavely directly into SegMetrics, and let your attribution reporting do the rest.

Then the next time someone asks which of your forms is actually driving the business, you will have an answer that goes deeper than a response count.

Not a SegMetrics customer yet? Start your free trial and see which of your lead sources are actually profitable.

AI Marketing Prompts for Attribution & Analytics (174 Prompts)

Denise Cullom — Tue, 07 Jul 2026 18:33:02 GMT

These 174 prompts let you ask AI real questions about your marketing data, from revenue by source to trial-to-paid conversion by channel, and get answers pulled straight from your own numbers. They are organized into 18 categories, and every one is built to run on live data through an MCP server or AI inside analytics. You copy a prompt, you paste it, you get an answer. No exporting, no spreadsheets, no waiting on an analyst.

Most AI marketing prompt lists are about making content. Write this email. Draft that ad. Build a persona. Those are useful. This is not that. These prompts are about understanding what already happened in your business and deciding what to do next.

These prompts aren’t exclusive to SegMetrics nor written explicitly for SegMetrics. They can be used with any AI assistant connected to your marketing data through an MCP or similar integration. Some prompts may need to be adapted depending on which platforms are connected and what data your AI can access. The more complete your marketing data, the more powerful the answers become.

Take me to the prompts!

The difference between content prompts and analytics prompts

A content prompt asks AI to make something new. An analytics prompt asks AI to tell you the truth about your marketing.

“Write three Facebook ad headlines” is a content prompt. “Show me ROAS for all active campaigns using my actual revenue data, and tell me which to cut” is an analytics prompt. The first one guesses. The second one reads your numbers.

The reason analytics prompts have been rare is simple. Generic AI tools do not know your business. They cannot see your revenue, your attribution, your churn, or your funnel. Ask a normal chatbot to analyze your campaign performance and it will invent something plausible and wrong. To get a real answer, the AI needs access to your real data, and it needs to understand marketing structures like attribution models and lifetime value.

That is what the SegMetrics MCP gives it. The AI connects to your attribution data and answers from your account, not from thin air.

How it works

The SegMetrics MCP is a connection between an AI assistant and your SegMetrics account. Once it is connected, you ask questions in plain language and the AI queries your data to answer them.

You do not need to know SQL and you do not need to build a report. You type the kind of question you would ask a data analyst, and you get the kind of answer a good analyst would give you, in seconds.

The prompts below are starting points. They work as written, and they work better when you add your own dates, campaign names, and offers.

The 18 categories

Setup and config

Start here. Garbage in, garbage out. These prompts check that your integrations are connected, your UTM tracking has no gaps, your pixel is firing, and your attribution model is configured correctly. One good first prompt: “Give me an overview of my SegMetrics account, integrations connected, data flowing, and anything that looks off.”

Revenue and LTV

Understand where your money actually comes from. Go past the first sale to see lifetime value by source, revenue per lead, and which channels bring in the customers who spend the most over time. A favorite: “What is the average customer lifetime value broken down by traffic source? Which sources bring in the highest LTV customers?”

Attribution

Find out what is really driving conversions. Compare first-touch, last-touch, and linear models on the same date range and watch which channels gain or lose credit. See the assisted conversions that never get last-click recognition. Trace the full multi-touch path a customer takes before buying.

Ad performance

Get true ROAS from your revenue data, not the inflated numbers the ad platforms report. Pull spend, clicks, CPL, CPA, and ROAS in one table. Find the campaigns to scale and the ones to kill. “Which campaigns have a ROAS below 2 or a cost per acquisition above my target? List them and tell me which to cut or pause.”

Email attribution

See which emails drive revenue, not just opens. Find the single email that generated the most sales by last-touch attribution, down to the individual send. Break revenue down by campaign, by list, by medium.

Funnels

Trace the journey from opt-in to purchase and find where people fall off. Compare offer pages. Measure upsell take rates. Map webinar funnel conversion from registration to sale.

Trial funnel

Track every step from trial start to paid customer. Measure trial volume, trial-to-paid conversion by channel, and pinpoint exactly when a trial decline started. Split true churn from trials that never paid.

Subscriptions and churn

Know your MRR, churn, and retention cold. See new versus churned MRR by month. Break churn down by plan. Build cohort retention tables. Find out whether your churn is an onboarding problem or a long-term value problem.

Cohort analysis

See how each customer vintage performs over time. Group customers by first purchase month, measure LTV per cohort, and find whether your older customers are worth more than your recent ones.

Sales team and demos

Track demos, trials, and conversions by rep. Measure demo volume, demo-to-trial conversion, and demo-to-paid conversion for each person on the team. Find the demo contacts who went cold and never trialed.

Lead quality

Stop optimizing for leads that never buy. Rank your lead sources by downstream revenue, not by volume. Find the sources sending buyers and the ones sending tire-kickers. Calculate your effective cost per lead that actually converts.

Affiliate and partner tracking

See which partners drive real revenue, not just clicks. Compare affiliate conversion rates and LTV. Check commission paid against revenue generated to find out who has the best ROI and where your payouts are unfair.

Product and offer analysis

Know which offers carry the business. Break revenue down by product and price point. Find your highest refund rates. Compare subscription revenue against one-time purchases. Measure upsell attach rates by offer.

Audience and segmentation

Find the segments worth going all in on. Profile your highest-value customers by source, funnel entry, and behavior. Compare buyers to non-buyers. Measure revenue by tag. Find how much overlap exists between buyers of different products.

Agency and multi-account

Run the same report across every client account. Built for agencies, these prompts pull consistent ROAS, lead, and revenue reports across all your clients without rebuilding queries each time. Swap in a client name and go.

Forecasting and trends

See where things are heading before they get there. Calculate your run rate. Spot which sources have momentum and which are fading. Estimate revenue from your current pipeline. Measure how far you are from a quarterly goal and what would need to change to hit it.

Diagnose

When the numbers look wrong, find out why. Track down unattributed revenue, null UTM fields, pixel failures, and platform mismatches before they mislead a budget decision. “How much of my revenue is showing as unattributed, and how do I fix it?”

Reporting

Build the report your team actually needs. Pull clean, decision-ready numbers for a weekly review, a monthly plan, a board update, or a client debrief. Ask for a period-over-period comparison and get it formatted to share.

The diagnostic sequence: your launch QA partner

One use case deserves its own section. When you launch a new funnel or campaign, you need to know one thing fast: is it tracking at all?

No analytics dashboard answers that in real time. The SegMetrics MCP does. The diagnostic sequence is a six-step run that takes you from “is anything tracking” down to “which specific contact came from which specific ad creative.”

You start by confirming the system is receiving any data at all and you check whether new leads match what you see in your CRM. You find where the leads came from and how many have null UTMs. Then check whether attribution is working independent of those UTMs. Then you attribute individual contacts to individual campaigns. Finally you confirm your tags are firing.

This sequence came out of a real launch. A founder ran twenty-three prompts across several days while debugging a live quiz funnel, confirming the pipeline was flowing and finding exactly which Meta creative each lead came from. It was not analytics. It was live QA. The MCP worked as a debugging partner for a launch in progress.

How marketers are actually using this

The prompts here are refined from real sessions, not invented in a vacuum. A few patterns stand out.

The most sophisticated users build things that used to take an analyst a week. One user ran dozens of month-by-month queries to assemble a full customer cohort table. Another ran the entire demo pipeline across every sales rep, cross-referencing by company domain to catch attribution misses.

Agencies found a repeatable weekly rhythm. One agency runs the same two or three reports, ROAS by campaign, new leads per campaign, leads with a call-booked tag, across four client accounts every week. Same questions, different accounts, no rebuilding.

The most granular work drilled all the way down to a single email. One user found the exact email driving the most revenue by last-touch attribution, then broke down which product that one email sold.

Where to start

If you are new to this, do not try to run all 174 at once. Start with three.

Run the account overview to confirm your data is clean and run revenue by source to see where your money comes from. Run ROAS by campaign to see what your ads are really doing. Those three answer the questions every marketer has every time they sit down to look at their numbers.

Then work outward into the category that matches whatever you are trying to decide this week. The prompts are here. Your data is ready. All you have to do is ask.

AI Marketing Prompts for Your SegMetrics Connected Account

SETUP & CONFIG: Connect and configure before you analyze anything.

Get integrations, UTM tracking, pixel health, and attribution logic working correctly. Garbage in, garbage out — start here.

Account overview

Give me an overview of my SegMetrics account — integrations connected, data flowing, and anything that looks off.

Integration status check

Check which integrations are connected to my SegMetrics account and flag any that are missing data or throwing errors.

UTM coverage audit

Review my UTM parameter setup and tell me where there are gaps that cause traffic to show up as unattributed.

Tracking pixel health

Check whether my SegMetrics tracking pixel is firing correctly on all the key pages in my funnel.

CAPI pipeline check

Check whether my CAPI pipeline is active and flowing. Are leads from my ad campaigns being captured and attributed correctly?

Attribution model setup

Walk me through choosing the right attribution model for my business and help me configure it in SegMetrics.

Data sync schedule

How often is my data syncing from each connected integration? Are there any integrations that are behind or stalled?

Onboarding checklist

Walk me through the SegMetrics setup checklist and tell me which steps I have completed and which are still outstanding.

REVENUE & LTV: Understand where your revenue actually comes from.

Go beyond last-click to see which sources, funnels, and segments drive the most lifetime value — not just the first sale.

Revenue by source

Show me total revenue by traffic source for the last 30 days, sorted highest to lowest.

Customer LTV by source

What is the average customer lifetime value broken down by traffic source? Which sources bring in the highest LTV customers?

Revenue per lead

What is my revenue per lead by traffic source? Which sources are most efficient at turning leads into revenue?

YTD revenue summary

Give me a year-to-date revenue summary — total revenue, leads, customers, and refunds so far this year.

LTV trend over time

Show me how average customer LTV has trended over the last 6 months. Is it going up, down, or flat?

Top revenue-generating campaigns

Which campaigns have generated the most total revenue over the last 90 days? Include both first-touch and multi-touch attribution.

High-LTV customer profile

What do my highest-LTV customers have in common? Look at source, funnel entry point, offer, and any other patterns in the data.

Revenue by offer or product

Break down revenue by offer or product. Which products are driving the most total revenue and the highest average order value?

One Purchase vs total revenue

For customers acquired in the last 90 days, how does one purchase revenue compare to total revenue including upsells and rebills?

Revenue concentration risk

What percentage of my total revenue comes from my top 3 traffic sources? Am I too dependent on a single channel?

Refund-adjusted revenue

What is my revenue after accounting for refunds? Which sources or campaigns have the highest refund rates eating into my numbers?

Week-over-week revenue

Compare my revenue, orders, AOV, and refunds for this week versus last week. What changed and why?

Pre-launch baseline

Establish a 30-day baseline of leads and revenue before my new campaign launches. Show me the weekly trend so I have a clean benchmark to compare against.

ATTRIBUTION: Find out what is actually driving conversions.

Compare attribution models, trace the customer journey, and stop making budget decisions based on last-click data.

Top converting sources

What are my top converting traffic sources over the last 30 days? Show me leads and customers by source.

Channel performance overview

Show me total leads, customers, and revenue by traffic channel — direct, search, paid, social, email, referral — for the last 90 days.

First-touch vs last-touch

Compare first-touch and last-touch attribution for my top 10 channels. Where do they disagree most and why does it matter?

Multi-touch path analysis

What does the typical customer journey look like before a purchase? Walk me through the most common multi-touch paths.

Attribution model comparison

Run the same date range through first-touch, last-touch, and full funnel attribution models. Show me which channels gain or lose credit depending on the model.

Assisted conversions

Which channels are regularly in the customer journey but rarely get last-touch credit? Show me my top assisted conversion sources.

Time to conversion by source

How long does it take for leads from each traffic source to convert to customers? Which sources have the longest sales cycles?

Organic vs paid attribution

Compare the lifetime value and conversion rate of customers acquired through organic channels versus paid channels.

This year vs last year by channel

Compare total leads and customers by channel this year versus the same period last year. Which channels are up and which are down?

Touchpoint frequency analysis

On average, how many touchpoints does a lead have before converting? Does that number vary by traffic source or offer?

Top pages by traffic

Which pages on my site drive the most visitors? Group by page path over the last 90 days and rank them.

AD PERFORMANCE: Know which ads are worth keeping.

Get true ROAS using SegMetrics revenue data — not platform-reported numbers — and find exactly which campaigns and creatives to scale or cut.

ROAS by campaign

Show me ROAS for all active campaigns using SegMetrics revenue data. Which are profitable and which are losing money?

Cost per acquisition by campaign

What is my cost per acquisition by campaign and ad platform for the last 30 days?

Full ad metrics table

Pull spend, clicks, impressions, CTR, CPC, CPL, CPA, and ROAS by campaign for the last 30 days. Give me everything in one table.

Google vs Facebook comparison

Compare spend, leads, customers, revenue, CPL, CPA, and ROAS grouped by ad account — Google versus Facebook — for the last 90 days.

Ad spend vs attributed revenue

Pull my ad spend alongside SegMetrics attributed revenue for the last 60 days. Where are the biggest gaps between what I spent and what I got back?

Underperforming campaigns

Which campaigns have a ROAS below 2 or a cost per acquisition above my target? List them and tell me which to cut or pause.

Creative-level performance

Break down performance to the individual ad level within my top campaigns. Which specific creatives have high spend but low conversion or poor ROAS?

Campaign scaling candidates

Based on ROAS, CPA, and LTV, which campaigns are the strongest candidates to scale with more budget right now?

Month-over-month campaign trends

Show me month-over-month ad performance by campaign. Which campaigns are accelerating, declining, or newly underperforming?

Lead-to-customer rate by campaign

For each campaign, show me the lead-to-customer conversion rate and revenue per lead. Where is the funnel converting well post-click and where is it leaking?

Top of funnel vs retargeting

Compare the downstream LTV of customers who came in through awareness campaigns versus retargeting or bottom-of-funnel campaigns.

New campaign check

I launched a new campaign 2 weeks ago. Show me its attributed leads, customers, revenue, and CPA compared to my account average.

Leads attributed to ads

How many new leads were attributed to ads in the last 30 days? Break it down by campaign so I can use it for my ads report.

EMAIL ATTRIBUTION: See which emails are actually driving revenue.

Go beyond sequence-level stats to find the specific emails and campaigns generating the most attributed revenue.

Email sequence performance

How are my email sequences performing? Show me leads entering, attributed revenue, and conversion rate for each sequence.

Top revenue-generating email

Find the single email that generated the most direct revenue in the past 90 days using last-touch attribution — the email clicked immediately before purchase.

Email UTM check

Do my email link clicks carry UTM parameters that identify individual emails? Check whether utm_campaign or utm_content is being used to track email-level clicks.

Revenue by email campaign

Show me revenue attributed to each email campaign over the last 90 days using the UTM campaign dimension. Which campaigns are driving the most direct revenue?

Revenue week over week

What is my revenue for the last 7 days compared to the prior 7 days? Show me the week-over-week change.

Email medium revenue breakdown

Break down revenue by email medium and source over the last 90 days. Which email channels and lists are generating the most attributed revenue?

Re-engagement campaign results

How did my re-engagement email campaign perform? Show me how many lapsed leads came back and converted to customers.

Email unsubscribe impact

Are unsubscribes concentrated in any particular sequence or time window? Am I losing leads who could have converted with better nurture?

Best product per email

For my top revenue-generating email, which product or offer did it drive the most sales for? Break down attributed orders by product name.

FUNNELS: See where leads convert and where they fall off.

Trace the full journey from opt-in to purchase across your funnels, offer pages, and sequences.

Funnel conversion rates

Show me the conversion rates at each stage of my main funnel — from opt-in to sale. Where am I losing the most people?

Lead to customer time

How long does it typically take a lead to convert to a customer? Does it vary by traffic source or funnel entry point?

Drop-off analysis

Where in my funnel are leads dropping off most? Which steps have the worst conversion rates and what might be causing it?

Offer page performance

Compare the conversion rates and average order value across my different offer pages or sales pages.

Upsell and order bump performance

What percentage of customers take my upsells or order bumps? Which offers have the highest upsell attach rate?

Webinar funnel attribution

How much revenue has been attributed to leads who entered through my webinar funnel? What is the conversion rate from registration to purchase? You may have to use tags, lists, or deal stages to help determine the steps in the webinar funnel.

Lead magnet quality comparison

Compare the downstream conversion rates of leads who came in through each of my lead magnets. Which attracts the best buyers?

Funnel A/B test results

I ran two versions of my opt-in page last month. Show me which performed better on conversion rate and downstream revenue.

Weekly webinar cohorts

Show me weekly webinar registration volume, show-up rate, conversion rate, and revenue per week over the last 90 days. Which weeks were strongest?You may have to use tags, lists, or deal stages to help determine webinar funnel data.

Lead nurture cohort timeline

For leads who opted in in the last 6 months, show me week-by-week when conversions happen after opt-in. When does conversion drop off?

TRIAL FUNNEL: Track every step from trial start to paid customer.

Measure trial volume, trial-to-paid conversion, and which channels are actually filling your trial pipeline.

Trial volume this year vs last

Show me monthly trial signup volume this year compared to the same months last year. When did the trend change?

Channels driving trials

Which channels are driving trial signups this year versus last year? Show me where the increase or drop-off is coming from.

Trial-to-paid conversion rate

What percentage of trial signups convert to paying customers? Break it down by the month trials started.

Trial conversion by channel

Which traffic sources produce trial signups that convert to paid customers at the highest rate? Compare trial-to-paid conversion across channels.

Trial decline inflection point

My trial signups are down. Show me the monthly trend over the last 12 months and identify exactly when the decline started.

Trial starts by month

Show me the monthly breakdown of trial starts over the last 12 months. Is volume growing, flat, or declining?

Contacts with demo and trial tag

Count unique contacts who have both the demo-scheduled tag and the trial tag. How many demo prospects actually started a trial?

Trial abandonment vs true churn

How many trial cancellations were from contacts who never made a payment versus contacts who paid at least once? Split these into two groups.

SUBSCRIPTIONS & CHURN: Know your MRR, churn, and retention numbers cold.

Track subscription health, find when churn accelerated, and understand which plans and cohorts retain the best.

MRR trend overview

Show me my monthly MRR trend over the last 24 months — new MRR, churned MRR, and net MRR. When did the trend change?

Churn rate by month

What is my monthly user churn rate and revenue churn rate over the last 12 months? Show me the trend.

Active subscription breakdown

How are my contacts distributed across subscription statuses — active, trialing, canceled, expired? Give me the current counts.

Churn rate by plan

Break down churn rate by subscription plan. Which plans have the highest churn and which retain best?

Subscription cohort retention

Group subscribers by the month their subscription started. For each cohort, show how many started, how many are still active, their average subscription age, and total revenue generated.

Cancellation timing analysis

How long do subscribers stay before churning? Show me average subscription age at cancellation to understand if churn is an onboarding problem or a long-term value problem.

New vs churned MRR by month

For each month over the last 24 months, show me new MRR added versus MRR lost to churn. Is new MRR keeping pace with churn?

Plan-level subscription health

For each subscription plan, show me active subscribers, monthly starts, cancellations, churn rate, and average LTV. Which plans are healthiest?

Annual vs monthly billing retention

Do annual subscribers retain longer and generate higher LTV than monthly subscribers? Compare billing cycle against churn rate and average subscription age.

Paying churn vs trial churn

Split my cancellations into two groups: contacts who had at least one successful payment, and contacts who cancelled without ever paying. Show me counts and trends for each.

Acquisition channels for new subscribers

Where are my new paying subscribers coming from? Break down new subscription starts by UTM source over the last 24 months.

Subscription health by account manager

Show me active subscriptions, trials, new starts, and cancellations for contacts tagged to each account manager. How is each account book performing?

COHORT ANALYSIS: See how each customer vintage performs over time.

Build true cohort tables by first purchase month, measure LTV per cohort, and find which customer vintages retain best.

New customers by first order month

Group customers by the month of their first order going back 24 months. Show me customer count, average order value, and total cohort revenue for each month.

Customer cohort LTV table

Build a cohort table: for each month’s new customers, show how many started, their average customer age in days as of today, average LTV, and total revenue generated by that cohort.

Cohort retention comparison

Which monthly customer cohorts are retaining best? Compare average subscription age and total revenue across cohorts from the last 24 months.

New customers this month vs prior

How does this month’s new customer count and average order value compare to the same month last year and the prior month?

Contact status breakdown

Show me how all my contacts are distributed by status — lead, customer, cancelled, expired, and any other status in my account. Give me current counts.

Early cohort vs recent cohort LTV

Do customers who signed up 2 or more years ago have significantly higher LTV than customers from the last 6 months? Compare older cohorts against newer ones.

Monthly new customer trend

Show me the count of new customers per month for the last 24 months. Is my customer acquisition rate growing, flat, or declining?

SALES TEAM & DEMOS: Track demos, trials, and conversions by rep.

Measure each rep’s demo volume, trial starts, and paid conversions to understand where your sales pipeline is working and where it is leaking.

Demo volume by rep

How many demo contacts does each sales rep have in 2026? Show me counts by rep tag.

Demo-to-trial conversion by rep

Of each rep’s demo contacts, how many started a trial? Show me demo count and trial starts side by side for each rep.

Demo-to-paid conversion by rep

Of each rep’s demo contacts, how many became paying customers? Show me demo count and paid conversions for each rep.

Full demo pipeline table

Give me a full demo pipeline breakdown for 2026: total demo contacts per rep, how many trialed, how many converted to paid, and how many went cold.

Time from demo to trial start

For contacts who booked a demo and then started a trial, how many days on average passed between the demo and trial start? Does it vary by rep?

Demo contacts who never trialed

Show me the demo contacts who never started a trial. What tags do they have? Did they engage with follow-up emails or go completely cold?

Demo leads by source

Which traffic sources produced my demo contacts this year? Show me lead source breakdown for all contacts who booked a demo.

Demo contacts full export

Give me a full list of all demo contacts in 2026 with name, email, demo date, rep tag, trial status, and paid status so I can export it.

Monthly demo volume trend

Show me monthly demo bookings over the last 12 months. Is demo volume growing or declining?

Rep account health check

For contacts tagged to a specific account manager, show me active subscriptions, trials, new starts, and cancellations over the last 90 days.

LEAD QUALITY: Stop optimizing for leads that never buy.

Qualify your lead sources by downstream revenue so you stop feeding the funnel with the wrong people.

Lead quality by source

Which of my lead sources produce the highest quality leads — the ones most likely to buy and spend more over time?

Lead-to-customer rate by source

Compare the lead-to-customer conversion rate across my top traffic sources. Which sources send buyers versus tire-kickers?

Lead magnet to revenue ranking

Rank my opt-in forms and lead magnets by downstream revenue generated. Which ones actually attract buyers? Ask if you need the lead magnet urls

Low-quality lead sources

Which traffic sources or campaigns generate lots of leads but almost no revenue? Show me where I am wasting money on lead volume.

Lead lag by source

For leads who eventually became customers, how long did it take on average? Does lag time vary by source or funnel?

Cost per quality lead

What is my cost per lead for each paid source? Now adjust for quality — what is my effective cost per lead that actually converts to a customer?

Contacts with specific tag combination

Count unique contacts who have both [tag A] and [tag B] applied. How many contacts meet both criteria?

Leads by tag over time

How many contacts have been tagged with [tag name] over the last 30 days? Show me the daily or weekly trend.

AFFILIATE & PARTNER TRACKING: See which partners are driving real revenue.

Track affiliate performance beyond clicks and leads — see which partners bring buyers, and which bring noise.

Affiliate revenue overview

Show me total revenue attributed to each affiliate or partner over the last 30 days, sorted by revenue generated.

Affiliate conversion rates

Compare the lead-to-customer conversion rate across my affiliates. Which partners are sending the highest quality traffic?

Top affiliates by LTV

Which affiliates are sending customers with the highest lifetime value, not just the most initial sales?

Affiliate commission vs revenue

For each affiliate, show me what I paid in commissions versus the revenue they actually generated. Who has the best ROI?

New affiliate performance

I onboarded new affiliates last month. Show me their attributed leads, customers, and revenue so far.

Affiliate funnel drop-off

At which funnel stage are leads from affiliates dropping off most compared to my direct traffic?

Partner campaign attribution

A partner ran a dedicated promotion last week. How much traffic, how many leads, and how much revenue did it generate?

Affiliate payout fairness check

Based on actual attributed revenue, are my current affiliate commission rates fair? Where am I over- or under-paying relative to the revenue each partner generates?

PRODUCT & OFFER ANALYSIS: Know which offers are carrying the business.

Break down performance by product, price point, and offer type to find where to double down and what to cut.

Product revenue breakdown

Break down my total revenue by product or offer for the last 90 days. Which products are driving the most revenue?

Offer conversion rate comparison

Compare the conversion rates across my different offers and price points. Which converts best from the same traffic?

Product refund analysis

Which products have the highest refund rates? Show me refund rates by product and whether they have changed over the last 6 months.

Price point performance

How do my different price points perform in terms of conversion rate, average order value, and downstream LTV?

Upsell take rate by product

For each core offer, what percentage of customers take the upsell? Which products have the highest upsell attachment rate?

Product launch attribution

I launched a new product last month. Show me attributed revenue, conversion rate, and source breakdown for that launch.

Subscription vs one-time revenue

How does revenue from subscriptions compare to one-time purchases? Is my recurring revenue growing as a share of total revenue?

Order bump price test

I am A/B testing two price points on my order bump. Compare take rate, total revenue, and AOV for each variant week over week.

Best offer for each traffic source

Which traffic sources convert best to which offers? Is there a clear match between source intent and offer type?

AUDIENCE & SEGMENTATION: Find the segments worth going all in on.

Slice your audience by behavior, source, tag, and value to find the patterns that make your best customers different.

High-value segment profile

Who are my highest-value customers? Describe their common traits — source, funnel entry, first offer, engagement behavior.

Segment revenue comparison

Compare revenue and LTV across my main audience segments or tags. Which segment is most valuable?

Buyer vs non-buyer comparison

What separates contacts who bought from those who never did? Show me behavioral and source differences between buyers and non-buyers.

New vs returning customer split

What share of my revenue comes from new customers versus repeat buyers? Is that ratio changing over time?

Tag performance analysis

Show me conversion rates and revenue broken down by contact tags. Which tagged segments are most valuable?

Audience overlap between products

How much overlap is there between buyers of my different products? Which customers have bought more than one offer?

Churned customer analysis

Who are my churned customers and where did they come from? Is there a source, offer, or segment with a higher churn rate?

Contacts by domain

Find all contacts at a specific company domain. Show me their tags, subscription status, and purchase history.

AGENCY & MULTI-ACCOUNT: Run the same report across every client account.

Built for agencies managing multiple SegMetrics accounts — pull consistent performance reports across all your clients without rebuilding queries from scratch.

ROAS by campaign for a client

Show me the best and worst performing ad campaigns by ROAS over the last 30 days for the [Client Name] account.

New leads per campaign for a client

Show me total new leads per campaign in the last 30 days for the [Client Name] account.

Leads with call-booked tag by campaign

Of leads attributed to ads in the last 30 days, how many also received a call-booked or call-scheduled tag? Break it down by campaign for the [Client Name] account.

Full ad metrics for a client

Pull spend, clicks, leads, customers, revenue, CPL, CPA, and ROAS by campaign for the [Client Name] account for the last 30 days.

Client account health overview

Give me a health overview for the [Client Name] account — active integrations, recent lead volume, revenue trend, and any data gaps I should flag.

Cross-client ROAS comparison

Compare ROAS across all my client accounts for the last 30 days. Which clients are seeing the strongest ad performance and which need attention?

Monthly client report

Build a monthly performance report for the [Client Name] account: leads, customers, revenue, top sources, and ROAS by campaign — formatted to share with the client.

FORECASTING & TRENDS: See where things are heading before they get there.

Use historical trends and current pipeline data to anticipate revenue, spot momentum shifts, and plan ahead.

Revenue trend this quarter

How is revenue trending this quarter compared to the same period last quarter and last year? Am I ahead or behind pace?

Lead volume trend

Is my lead volume growing, flat, or declining? Show me the trend over the last 6 months by source.

Revenue run rate

Based on the last 30 days, what is my current monthly run rate? If trends hold, what would my next 3 months of revenue look like?

Conversion rate trend

How have my lead-to-customer conversion rates trended over the last 6 months? Is my funnel getting better or worse?

Traffic source momentum

Which traffic sources are growing in volume and conversion rate right now? Which are declining? Show me the momentum signals.

Churn risk forecast

Based on current subscription retention trends, what percentage of my recurring revenue am I at risk of losing in the next 90 days?

Pipeline to revenue estimate

Based on leads currently in my funnel and historical conversion rates, what is my estimated revenue from this pipeline over the next 60 days?

Goal gap analysis

I am trying to hit a revenue target this quarter. Based on my current run rate and conversion rates, how far am I from that goal and what would need to change to hit it?

Best time to launch

Looking at historical performance data, when have my launches and campaigns performed best? Are there clear seasonal patterns?

DIAGNOSE: When the numbers look wrong, find out why.

Track down data gaps, attribution mismatches, UTM nulls, and pixel failures before they mislead a budget decision.

Unattributed revenue

How much of my revenue is showing as unattributed in SegMetrics? Help me figure out where it is coming from and how to fix it.

Revenue drop investigation

My revenue dropped last week compared to the week before. Help me figure out which source, campaign, or funnel stage is responsible.

Lead volume spike or drop

My lead volume changed significantly this week. Help me identify the source causing the shift — is it one channel or across the board?

New leads source check

I see new contacts in my CRM but I cannot tell where they came from. Pull their UTM source, channel, and any click-attribution data to identify the actual source.

Tag not appearing on contacts

Some contacts are not showing up in a segment I expected them to be in. Help me figure out whether it is a tagging issue, a sync issue, or a tracking gap.

Conversion event not firing

One of my conversion events does not seem to be recording correctly. Help me diagnose whether it is a pixel issue, a mapping issue, or something else.

Platform number mismatch

My ad platform is showing different numbers than SegMetrics for the same campaign. Help me understand why they disagree and which number to trust.

Integration data gap

Check whether my email platform and ad accounts are syncing correctly. Flag any gaps or delays in the data flow.

Duplicate contact check

Are any contacts being counted more than once in my attribution data? Help me find and clean up duplicates that might be inflating my numbers.

Unusual traffic spike

I see a sudden spike in traffic from an unfamiliar source. Help me figure out if it is legitimate, bot traffic, or a tracking anomaly.

REPORTING: Build the report your team actually needs.

Pull clean, decision-ready numbers for your weekly review, monthly planning, or client debrief.

Weekly performance summary

Give me a weekly performance summary: top revenue sources, best converting campaigns, and anything that changed significantly from last week.

Monthly attribution report

Summarize last month’s attribution data — which sources drove the most leads, customers, and revenue.

Ad agency debrief

Build a report I can share with my ad agency: spend, attributed revenue, ROAS, and CPA by campaign for the last 30 days.

Board or investor summary

Give me a high-level revenue summary for the last quarter — total revenue, revenue by channel, LTV trends — suitable for a board update.

Period-over-period comparison

Compare this month to the same period last month and last year. What improved, what declined, and what stayed flat?

Channel ranking report

Rank all my active channels by revenue, conversion rate, CPA, and LTV in a single report. Which channels deserve more investment?

Top 10 campaigns report

Show me my top 10 campaigns over the last 90 days ranked by attributed revenue. Include spend, ROAS, and CPA for each.

Funnel health report

Give me a funnel health report: opt-in rate, lead-to-customer rate, average order value, and refund rate — compared to last period.

Annual performance review

Summarize my full-year performance: total revenue, leads generated, customers acquired, best and worst performing channels, and key trends.

Custom date range report

Pull a performance report for a specific date range. Show me revenue, leads, customers, and top sources for that window.

Frequently Asked Questions

Q. Can AI actually analyze my marketing data?

A. Yes—but only if it has access to your data. A generic AI chatbot can’t see your CRM, ad platforms, payment processor, or attribution data, so it can only make educated guesses. When connected through the SegMetrics MCP, AI can answer questions using your actual marketing data, including revenue, attribution, customer lifetime value (LTV), ROAS, churn, and funnel performance.

For an in-depth look at this, check out this workshop:

Q. What is the SegMetrics MCP?

A. The SegMetrics MCP (Model Context Protocol) securely connects AI assistants like ChatGPT or Claude to your SegMetrics account. Instead of manually building reports or exporting spreadsheets, you simply ask questions in plain English and the AI retrieves answers directly from your live attribution data.

Q. What kinds of questions can AI answer about my marketing?

A. Almost anything you’d normally ask a marketing analyst. For example, AI can identify your highest-ROAS campaigns, compare attribution models, calculate customer lifetime value by source, analyze trial-to-paid conversion rates, identify churn trends, diagnose tracking issues, compare funnel performance, and generate executive-ready marketing reports—all using your own data.

Q. How are analytics prompts different from regular AI prompts?

A. Most AI prompt libraries focus on creating content, such as writing emails, ads, or social posts. Analytics prompts are different—they ask AI to analyze existing business data and provide insights. For example, instead of asking AI to write a Facebook ad, you might ask, “Which campaigns generated the highest revenue over the last 90 days?” or “Which traffic sources produce the highest lifetime value customers?”

Q. Do I need to know SQL or build reports to use these prompts?

A. No. The prompts are written in plain English. Instead of writing database queries or building dashboards, you simply ask questions the same way you would ask a marketing analyst. The AI handles the data retrieval, calculations, and analysis for you.

Q. Can AI calculate true ROAS instead of platform-reported ROAS?

A. Yes. When connected to SegMetrics, AI calculates ROAS using attributed revenue from your actual customers rather than relying solely on Facebook or Google Ads reporting. This gives you a much more accurate picture of campaign profitability, especially for businesses with longer sales cycles or repeat purchases.

Q. What types of businesses benefit most from AI-powered marketing analytics?

A. AI-powered analytics are particularly valuable for SaaS companies, agencies, coaches, consultants, course creators, membership businesses, subscription companies, and e-commerce brands that use multiple marketing channels. Any business that wants to understand which marketing activities actually generate revenue can benefit from connecting AI to attribution data.

Q. How many AI prompts are included?

A. The SegMetrics prompt library includes 174 marketing analytics prompts organized into 18 categories, covering everything from attribution and revenue analysis to trial funnels, churn, cohort analysis, agency reporting, forecasting, diagnostics, and executive reporting.

Q. Can agencies use these prompts for multiple clients?

A. Yes. Several prompts are specifically designed for agencies managing multiple client accounts. You can run the same revenue, attribution, ROAS, and lead-quality reports across different SegMetrics accounts simply by changing the client name, making recurring client reporting much faster.

Q. What are the best prompts to start with?

A. If you’re new to AI-powered analytics, start with three questions: “Give me an overview of my SegMetrics account,” “Show me revenue by traffic source,” and “Show me ROAS for all active campaigns.” These provide an immediate snapshot of your account health, revenue sources, and advertising performance, and often uncover insights that traditional dashboards miss.

The Bottom Line

There are good tools for every use case in marketing analytics. The key is matching the tool’s core design to how your business actually generates revenue.

If you sell digital products, run email-driven funnels, or operate a subscription model and want a clear, unified view of which marketing activities are creating your most valuable customers, SegMetrics is worth exploring. Its combination of CRM integration, email platform connectivity, and lifetime value tracking makes it one of the few tools that can answer the question every serious marketer eventually asks: not just who bought, but who kept buying — and where did they come from?

Start a free trial of SegMetrics or book a demo to see how it maps your specific customer journey.

Introducing Metric Snapshots

Ryan Johnson — Wed, 01 Jul 2026 22:49:10 GMT

Data no one sees is worthless

That’s the whole problem with a dashboard.

You built it. It’s accurate. But dashboards don’t tap you on the shoulder. They don’t ping your phone. They just wait for someone to log in, navigate over, and actually look.

But…people are busy. So here’s what really happens:

You mean to check it every Monday. Some Mondays, you do. Some you don’t.
A client is waiting on their weekly report, so you screenshot the dashboard, paste it into an email, and hope you didn’t crop out anything important. And how you remember to do it again next week.
Your CEO asks for “that number” in Slack. Then you go dig around for ten minutes to find it. Again.

None of this is because the data’s wrong. It’s because getting the data in front of the right person — on time, every time — is a manual job. And manual jobs are the first thing that gets skipped when the week gets busy.

Today, we fixed that.

Introducing Metric Snapshots

With Metric Snapshots, you can:

Pick any dashboard in SegMetrics
Set a sharing schedule: daily, weekly, or monthly
Choose exactly who gets it by email inbox, a Slack channel, or both
Deliver it as a clean PDF or PNG (with a link back to the live dashboard if someone wants to dig deeper)

No SegMetrics login required for the people receiving it. Set it once, and it just keeps showing up — whether you’re at your desk or not.

Your reporting is now automated.

See Metric Snapshots in action

What if your report just showed up?

For your CEO. For your client. Or just for you, on a Sunday morning with coffee.

That’s the actual shift here. Metric Snapshots doesn’t just save you a few minutes of screenshotting. It takes the reporting off your to-do list entirely and puts it on a schedule instead.

How can you use this?

For agencies

Set up a weekly snapshot for each client with their ROAS, funnel performance, or revenue summary. Now it lands in their inbox every Friday afternoon, automatically. Your client gets a clean, professional report with the numbers they actually care about. All with no login, no digging through dashboards, and no chasing you for last week’s numbers. Best of all, you don’t have to carve out time each week to update and share reports. This saves time every week with every client.

For teams

Set up snapshots so the right data reaches the right person without you being the middleman. Your CEO gets the monthly revenue overview. Your media buyer gets daily ad performance. Your sales team gets lead quality by source, every week. Set it up once per person, and you’re out of the loop — in the good way. Now you can focus more time on growing your numbers instead of sharing them.

For you

This is the simplest use case but it might be my favorite. We all have metrics we mean to check…and then get distracted. Sometimes logging and pulling up reports is just too much work. With snapshots, you can get the data you want delivered straight to your inbox or phone, right when it’s best for you.

A dashboard nobody’s watching isn’t insight. It’s baggage. Metric Snapshots is what makes sure someone’s actually looking.

Try Metric Snapshots for yourself

Setup takes about thirty seconds per snapshot, and it updates automatically any time the underlying dashboard changes.

Start your 14-day free trial today.

Data beats opinion. 🚀

Introducing Conversion Feeder

Ryan Johnson — Thu, 11 Jun 2026 21:51:30 GMT

You’re not overspending on ads. You’re under-informing the algorithm.

Facebook, Google, TikTok, Bing — they all run on algorithms that guess who to show your ads to. And those algorithms are only as smart as the data you give them.

Right now, most marketers do one of two things:

They let the ad platform guess based on web pixel data alone. This doesn’t work well since ad platforms can’t really see what happens after the opt-in.
They download a CSV of customers, upload it to the ad platforms, hope the format didn’t break, and maybe remember to do that again next week.

Both options have the same problem: the algorithm gets weak, incomplete data. Even in the best case, you’re telling Facebook, “Here are people who bought” …but that’s an average. When the algorithm optimizes for averages, your Cost Per Acquisition goes up because you’re paying to attract more of your weakest customers, not your best ones.

We rebuilt SegMetrics’ Conversion Feeder to solve this.

It sends your filtered, segmented conversion data to Facebook, Google, TikTok, and Bing — every day, automatically. You decide what “your best customer” means. Filter by tags, customer value, products purchased, custom fields, or any combination. Only those conversions train the algorithm.

Better data in. Better ads out.

See Conversion Feeder in action

Three ways to use Conversion Feeder

First things first: just sending your conversion data back to your ad platforms automatically is a huge win. Most marketers aren’t doing even that. If you set up your Conversion Feeder and never touch a filter, you’re already ahead. Your ad platforms get daily, accurate conversion data tied to the specific ad click that drove each sale, with zero manual work.

But the real edge is in the filtering. Here are three ways to use it:

1. Train your webinar ads on webinar buyers

If you’re running ads to a webinar, you don’t want Facebook learning from your entire customer base. You want it learning from the people who came through a webinar and bought. Those are the people your webinar ads should find more of, right?

To do this, set up a SegMetrics Conversion Feeder filtered by your webinar tag. Now your webinar campaigns are trained on webinar buyers (not your general customer list). The algorithm stops guessing which of your customers came from webinars and starts knowing.

2. Find more customers like your best customers

Not all customers are equal. Some buy once and disappear. Some stay for years and buy again and again. Which type do you want more of?

Set your filter to only send in customers above a certain value threshold — say, $1,000. Now you’re not telling the algorithm “Find me more buyers.” You’re telling it, “Find me more buyers like the ones who actually stick around and spend.” That’s a very different signal with better results.

3. Target by persona, not just by purchase

Let’s say you know that copywriters are your best customers. You can filter your Conversion Feeder to only send in customers who identified as copywriters.

Facebook and Google already know who’s a copywriter in their database, but they don’t know which copywriters would buy from you. Your data does. Now the algorithm can match those two things together to get more focused on the kinds of copywriters they show your ads to.

This works for any persona or demographic you track: industry, job role, company size. Anything in your custom fields. You decide how targeted you want to get.

Setup takes two minutes

Pick your ad account, pick your pixel, set your filters, click save. That’s it.

Conversion Feeder runs every day in the background. That means no spreadsheets, no weekly uploads, no stale data training your ads on customers from two months ago. And it ties each conversion to the specific ad click that drove it, which is something manual CSV uploads can’t do.

The most expensive targeting mistake

The most expensive targeting mistake isn’t a bad audience; it’s a good audience diluted with everyone else. A bad audience, you notice and fix. A diluted audience, you scale and every dollar you spend scales the waste right along with it.

Stop training your ads on the wrong customers.

Start your 14-day free trial today.

Data beats opinion. 🚀

Ishita Agarwal: The new retention playbook (Episode 13)

Ryan Johnson — Wed, 03 Jun 2026 21:00:22 GMT

Is your retention making or breaking your lifetime revenue?

After the first purchase, most e-commerce brands go quiet — or start blasting discount codes. But the brands that win long-term treat every purchase as the beginning of a relationship, not the end of one.

In this episode of Data Beats Opinion, SegMetrics founder Keith Perhac sits down with Ishita Agarwal — founder of Tapp — to dig into why retention is the biggest untapped lever in business, why CAC is breaking for most brands, and what it actually looks like to turn one-time buyers into loyal fans.

Watch the full episode →

Meet Ishita Agarwal

Ishita Agarwal is the founder of Tapp, a post-purchase engagement platform that helps e-commerce brands turn one-time buyers into loyal fans. With roots in M&A consulting and emerging technology, she went on to study design and behavioral science at Stanford before zeroing in on the post-purchase gap in commerce. Ishita brings a tech-world playbook — onboarding flows, habit loops, and gamification — to the e-commerce brands that need it most.

Connect with Ishita at Tapp or on LinkedIn.

Show Notes

[00:00] Meet Ishita Agarwal [01:15] How she got into e-commerce [04:59] Why retention is so broken in e-com [07:08] The math isn’t mathing — CAC vs LTV [09:01] Customers vs members [11:14] What Tapp actually does [14:38] Engagement numbers and what they mean [17:01] NFC tags and the omnichannel opportunity [20:14] What brands do with the data [22:06] How personalization works over time [27:36] How engagement turns into sales [28:55] The rewards model (and why discounts don’t cut it) [32:03] Stories that make it click — Solidcore and Nintendo Club [36:05] Connecting with customers through Amazon’s black box [40:20] The biggest mistake brands make [45:42] The Weber Grill principle [47:32] Where to find Ishita

About Data Beats Opinion

Try SegMetrics Free

Start your 14-day free trial today.

Data beats opinion. Growth beats guesswork. 🚀

Data Beats Opinion — Ishita Agarwal

Transcript

Keith: Here’s an interesting question: How would your business change if your success depended on whether or not people bought more than once? That era is getting closer. Customer acquisition costs are skyrocketing much faster than you can raise your prices. That means each new customer you bring on is worth less and less — and for some businesses, they’re already losing money on every new customer unless they buy a second time.

In today’s episode of Data Beats Opinion, we’re diving into this wild new landscape where retention is king. Our guest is Ishita Agarwal, founder of Tapp. She’s on a mission to transform how brands engage customers after the purchase. We explore how to shift your customers from liking you to loving you, how to treat your customers like members and not just customers, and why the first time someone uses your product is such an underutilized touchpoint.

If you’re ready to unlock a new profit lever in your business, let’s start the show.

Keith: Thanks for joining us, Ish. I’m really excited that you’re here.

Ishita: Thanks so much for having me, Keith. I’m pumped to be here.

Keith: So for people who are tuning in — we’ve done the intro, so they should know who you are — but give me a little background. Who are you? What is Tapp, and how did you come into this?

Ishita: Great question. My background is a little different than most people in commerce. I started my career in consulting — very traditional consulting, M&A, all that stuff. I learned what it felt like to be a professional in the corporate world and honestly enjoyed it a lot.

I eventually ended up on the emerging technologies team, which was truly a dream role for me. I was doing projects ranging from 3D printing and drones to AR, VR, and AI — back when AI was simulation and not all the LLM stuff we’re doing now.

I really fell in love with that space. I realized I loved operating at the intersection of technology, design, and behavioral science — disciplines that, together, can create really great experiences for consumers. I did that for a couple of years, then wanted to keep building expertise at that intersection, and ended up going to business school at Stanford.

While I was there, I took design classes from some incredible professors, did internships at places like Google X, and it was truly the best two years. I became more and more curious about how all three of those disciplines come together to create the greatest experiences that you and I have probably had when it comes to technology and the world we live in.

It was actually in one of our startup classes — called Startup Garage, where the whole point is to figure out how to start a business — that things clicked. We started with a completely different idea, but as we were researching it, I stumbled into the world of commerce.

I had never really been in e-com. I’m a consumer, but outside of that, I didn’t know much about the economics or the problems or what was and wasn’t working. Something I became curious about through a bunch of customer interviews was that people kept talking about how excited they were when they first purchased from a brand — but then, once they’d bought, they felt kind of distant from it. Like there was nothing really happening in terms of engagement. I became obsessed with that. What does that mean? Why is there an issue there? What’s happening?

That curiosity eventually turned into what Tapp is today.

Keith: That’s very cool. It’s one of my biggest pet peeves with e-commerce — once you buy the product, so many brands just don’t think about the post-purchase experience. A lot of times it’s, “Oh, we have a great post-purchase experience — we’ll send them a card on their birthday, and we’ll send them sales information every time we’re doing a Christmas sale.” And I’m like, great, just like every other e-com brand out there.

At SegMetrics, we really focus on that nurture sequence — on the post-purchase, on how do you get the data to turn customers into lifetime loyalists. That’s one of the reasons I was so interested to talk with you and with Tapp, because that’s essentially what you’re doing: going beyond just sending a postcard every six months. So what does Tapp actually do? In a perfect world, what does it allow e-commerce brands to do to create that engagement?

Ishita: Do you mind if I step back and explain why it’s important first? I think that helps frame the conversation.

Keith: Let’s do it.

Ishita: So when I came to commerce, the first thing I did was look at the tech stack. I come from tech, so I’m always focused on tooling. I literally looked at a landscape of e-com, and it was very clear — even visually. On the acquisition side of the funnel, there were thousands of tools that existed for brands. On the retention side, there really wasn’t a lot. It’s much more bare when you put it on a screen. There’s email and SMS, and subscriptions kind of form the foundation of what retention looks like today.

Beyond that, there are loyalty programs and referral programs — but what those are really targeting is the top 10% of your customers. Those are the people already referring you, already involved in your communities, already in love with your brand.

There really isn’t much happening for the rest of your customers. So the first thing I realized was: A) there wasn’t a lot happening in retention overall, and B) what retention was happening was aimed at the top 10%. Coming from tech, that’s not how we approach things. In tech, everything is about retention. We’re tracking onboarding flows, habit loops, MAU, DAU. If you’re working on Instagram, you’re not just looking at the top 10% of users — you’re looking at every single user and figuring out how to get them to engage with you, because

Keith: In tech, we live and die by retention.

Keith: Exactly. The idea is that everyone’s buying month after month, and retention and engagement are how we stay in business. If we lose that, we lose everything.

Ishita: Exactly. And I knew that from the tech world. The next thing I always do is look at the economics. Maybe retention doesn’t matter in commerce — maybe it truly just isn’t as important. But as you know, that’s not true.

Keith: LTV to CAC is always the key metric for any business — how good is this business model for you?

Ishita: Right. And the math just isn’t working in e-com anymore because CAC is as high as it’s ever been. In the beauty industry, for example, the average cost of acquisition — at least a couple of months ago — was $61. That’s often higher than the purchase price of the product, which means these brands are losing money on acquiring a customer. If you don’t buy from them again, they’ve literally lost money on you.

At that point I felt I’d validated a couple of things: retention is an enormous part of the math for any e-com company, and there wasn’t much happening to increase the LTV side of that equation — and what was happening was only focused on a very small subset of customers. Things were starting to come together, and the next question was: why does the problem exist?

Because I’m never one to assume people don’t know these things — people are smart and they understand their businesses.

Keith: I would often assume they don’t know, or don’t care.

Ishita: I think people are very limited by the tooling that’s available to them.

Keith: Yes.

Ishita: 100%. If all you have access to is a loyalty program that rewards people for buying more stuff, that’s all you can do. There’s not much more you can do beyond that.

But as we dug deeper, we found what we think is the core problem: brands are treating their customers like customers instead of treating them like members of their brand. What I mean is, when someone’s a customer, you have a transactional relationship. You’re sending them five emails a day to stay top of mind. You’re throwing discount codes at them because you want them to keep purchasing. But you’re not cultivating a relationship. When you’re acquiring someone, you’re courting them — “Look at all this cool stuff we have, we’re solving all your problems.” But then once they buy, that relationship stops. You’re just throwing stuff at them and hoping they come back.

Keith: It’s almost like the purchase is treated as the finish line instead of the starting point of a relationship.

Ishita: Exactly. And there’s a crazy stat: in every single category today, there are six times as many brands as there were just five years ago. So when someone buys your product, they’ve chosen you over the other 50 or 100 brands they could have purchased from. That means your product has to be good — but at this point, a good product is basically table stakes. The real reason they’re buying is that something about your brand resonates with them — the ethos, the mission. So why, instead of inviting them into a membership experience exclusive to buyers, are you just broadcasting at them? Throwing emails, SMS, and discounts, without inviting any real engagement?

What we discovered is the real problem: brands don’t have a way to make customers feel special and continue that relationship.

Keith: So what’s the solution? How do you fix something like that?

Ishita: We know brands are very busy and don’t have a lot of time and resources to spend on new things. So we take content that brands already have — social media, email campaigns, ad campaigns, websites. We run it through AI models that analyze what your brand is, what problems you’re solving, who your customers are, and what journeys they need to go on.

What comes out on the other end is a web-based interactive hub of challenges. We turn those images and videos into tutorials, games, quizzes, polls, and community modules that people can actively engage with — all wrapped in a programmatic experience.

So if we know you’re a supplement brand and people need to stay consistent with using your product for 90 days to even start to see benefits, you’d get a 90-day program where every 30 days there’s a new set of challenges to complete. Customers go through polls, games, and quizzes designed to help them get more out of the product. They’re fun and delightful — a more interesting way to engage. You earn points for completing them, and those points can be redeemed for rewards as long as you continue to stay subscribed or purchase from the brand. Instead of random communications after a purchase, you’ve created a program for the customer to follow.

That’s kind of what the Tapp experience is — an interactive way to communicate with customers in a way that’s much more engaging than what emails and SMS can do on their own.

Keith: It’s interesting, because I’ve seen strategies like this before — not for e-com, but for info products — where you give people access to a free course or some kind of membership welcome experience. Not a PDF, not something small, but something valuable that builds community. There’s a psychological trigger that goes off when you receive something like that. It’s that give-and-take — quid pro quo. I receive something, now I have a better feeling toward you. Almost like the parasocial relationships we see on YouTube and social media.

So what kind of results have you seen? I think a lot of people listening are thinking, “That sounds great, but how do you get people into it?” What’s the engagement rate, and how does it translate to sales?

Ishita: There are a few parts of the funnel we look at: how many people are engaging at all, how many are engaging actively and earning rewards, and how many stay engaged, subscribed, or buying.

We have a couple of different ways for customers to enter the experience, and we talk about them from simplest to most effective. The simplest is through email and SMS, because brands are already doing a lot with that — we can easily plug in through Klaviyo. Funny thing is, we tried to stay away from email and SMS for a long time. I was not a believer. I thought it needed to go away. Current CTRs for normal e-com emails are around 3%. But our emails, which focus on the program and the rewards you can earn, are getting around 20% engagement. That was shocking for us — for a long time I was saying, “Nope, we’re not doing email.” Wrong decision. It ended up working really well.

Keith: And it was the data that showed you that, right?

Keith: For me, it’s kind of obvious, because I come from the creator and info product world where email rates are 25 to 50% — that’s how creators connect with their audiences. That’s always the divide I see between e-commerce and the creator world: e-com is so focused on the front end, and because their emails get 2 to 3% because they’re all sales, they assume there’s no value in the back of funnel. But what you’re showing is that there definitely is.

Ishita: Yeah. 50% is like a dream. I love your face right now — “Email?” I was like, “Whoa, I had no idea people were actually opening these.”

We also have even better rates with SMS, but it varies so much brand to brand that we don’t talk about those metrics publicly. And then the most effective entry point is actually why the company is called Tapp. We believe in a future where the software experience is coupled with the physical product experience.

I’m sure you’ve seen QR codes on packaging — they’re not effective. Scan rates are around 1 to 2% for a lot of reasons. But I come from an emerging technology background, and I’m a big believer in NFC tags. For anyone who’s not familiar, it’s the same technology as Apple Pay — little stickers that can go anywhere on your packaging. You just tap your phone on it, and it launches the web-based experience we discussed.

Keith: I do want to mention — a lot of people who haven’t used NFC tags think they’re expensive. You can buy 1,000 of them for close to a dollar. They’re very cheap, programmable, and

Ishita: they’re stickers, so they’re easy to put on your packaging. And unlike QR codes, you can print anything on top of them. Brands using NFC tags with us are seeing tap rates of about 40%.

Way higher than QR codes. And what it unlocks for us is omni-channel, because 90% of commerce in the US is not done through a brand’s website — meaning brands don’t know who 90% of their customers are, let alone trying to have a relationship with them.

This unlocks a really interesting space where you can start to create a brand relationship even when you don’t know who that customer is. So we always say: simplest entry point is email, most effective is the tags. Our goal is to move everyone into the Tapp experience at some point — though we’ve realized the tags are something to work toward rather than lead with in our selling proposition.

You can also use packaging inserts and other touchpoints. Brands sometimes want to put us on their websites too, and they can — we’re a web app — but we actually think post-purchase deserves its own separate space. Your website is meant for shopping. This is meant to be a space for engagement. Those are two different things.

In terms of active engagement within the experience, around 20 to 30% of people complete everything. There might be 13 or 14 modules to get through — they’re quick, not hours of content — but people are completing the quizzes, community modules, games, and tutorials.

Keith: That’s impressive.

Ishita: And beyond the engagement, the brand is getting zero- and first-party data they’ve never had access to before. Something as simple as “why did you buy this product?” — most brands have no idea. We make it fun: take this little poll and see what other people answered. But the brand is getting real data on why people are buying, and they can segment their communications from there.

Our north star metric is the number of points earned versus the total points available across all users. We want that as close to a 1:1 ratio as possible. Still working toward it, but it’s a good starting point.

Keith: And are you then pushing that data back into Tapp’s system? Are you pushing it back to Klaviyo?

Keith: Is that something brands then use in their email sequences?

Ishita: All of the above. The data belongs to the brand — we’re not going to gate it the way Meta and others do. It goes to Klaviyo, it’s your data to own. But we also know brands are busy, so the Tapp experience personalizes over time on its own. As we learn that Keith bought this product because he’s trying to solve problem A, and Ish bought it because she’s trying to solve problem B — the content, the challenges, the framing should be relevant to each of them. Otherwise it feels generic.

What we’re building toward is something like Instagram — where it just feels like it knows you. You’re completing challenges that feel genuinely relevant to you, so you keep coming back.

Keith: And this is a drum I beat all the time — personalization in marketing. Ramit Sethi always talked about “the special snowflake syndrome”: everyone thinks their unique situation is completely unique and doesn’t relate to anyone else. But the core of sales is overcoming those objections — “You’re a 40-year-old schoolteacher in Kansas. This product is for you because this other 40-year-old schoolteacher in Kansas got success with it.” It’s about understanding what problems people are trying to overcome, what their objections are, and presenting that in a way that naturally leads to the next purchase.

Ishita: Exactly. And I think it seems like a small thing, but where a lot of companies go wrong is that once they have that data, they hand it off and expect the brand to act on it. Building a website is a perfect example — you’ve made it easy for me, but it is still so much work.

Keith: As many drag-and-drops as you have, as much AI as there is, it’s still a lot of work.

Ishita: The weekend I had to build our website, I was in such a bad mood. It’s just so much work to figure out. There’s always too much functionality and not enough functionality at the same time. So with Tapp, we thought hard about how to make it feel seamless for the brand. We’re creating a new category — brands don’t have time and budget earmarked for something like this. They’ll only do it if we make it feel like, “You don’t have to do anything. It’s very easy to get up and running.” That’s why we’ve built what’s essentially a white glove service — but on our end, that white glove service is powered by AI so we can deliver as much as we promise in the timeframe we promise.

Keith: If you give brands all the data and say “personalize however you want,” you’re now relying on them to find time to rewrite all their copy. And then you come back and say, “Here are the different customer avatars we’ve identified — now write eight versions of every piece of copy.” That’s where AI shines. Whatever you believe about AI, taking existing content and turning it into something else is where it really delivers.

Ishita: Exactly. Which is why we want the Tapp experience to self-personalize. The brand doesn’t have to do a lot, but it feels tailored. The special snowflake syndrome — I love that term. That’s what we’re trying to make people feel: I’m engaging with this brand, and it feels like they know me. They understand the problem I’m having, they want to help me through it, and they’re giving me content that’s actually relevant and useful. That’s what we’re building toward.

Keith: That’s something we learned early at SegMetrics too. We started as just a tool with support — no services, no agency layer on top. That was a mistake. People know they need this stuff. They know they should have it. But they don’t have the time, the effort, or the know-how. If you go to an e-commerce brand and say, “You need a back-end engagement platform with quizzes and community modules,” they’ll look at you blankly — they don’t have the knowledge set to implement that. That’s where Tapp comes in and says, “You know you need it. We know you need it. Here’s how we string it all together into a package for you.”

Ishita: At the end of the day, it’s all about experience —

Keith: Exactly.

Ishita: — the end user experience, yes, but also your customer is the brand, so how do you make their experience great?

Keith: Exactly. So we’ve talked about why it’s important to keep engagement, why it’s important to keep the brand top of mind, and how to do that in an organic, personalized way through something like Tapp. How does this then translate to sales? Is it just top of mind? Is it educational? In your ideal world, what’s that next engagement step — from “I’m a part of this community” to “I want to buy everything”?

Ishita: The structure varies by brand, but let’s use a supplement brand with subscribers as an example. Every month, there’s a fresh set of challenges that follow the customer’s journey — month one looks different than month three. As you complete those challenges, you’re earning points, and those points can be redeemed for rewards. And we always tell brands: don’t make it a discount code. No one cares about discount codes anymore. Make it merch. Make it something they can’t buy anywhere else — something they can only earn by engaging in meaningful ways. And they get it with their next purchase.

That’s the retention mechanism. You’re earning something you’re genuinely excited about, but you can only unlock it with your next purchase.

Keith: That’s a very visceral thing. Do you know Nintendo Club, by any chance?

Keith: So Nintendo Club — I thought it was only in Japan, but I think it might be worldwide now. Basically, when you buy a Nintendo product, you register it to your account and earn points.

Ishita: My cousin is really into video games, so I know all about that world.

Keith: Everything in the Nintendo Club Store is only available in the Nintendo Club Store. It can be small things — I have a Goomba handkerchief, a clear Zelda file folder. But I also had a Super Famicom-branded Wii controller that was only released through the store. They also did a special release of Legend of Zelda I and II on the GameCube — also store exclusive. And here’s the thing: when you’d buy a used game, people would always take the code out first. Everyone wanted the code because you could get things you can’t get anywhere else. It built real behavior — “I want to buy this game new, not used, so I get the code, earn the points, and get something from the Nintendo Club Store.”

Ishita: I love it. And I give a very different example, but same idea. There’s a workout studio in New York — and a few other big cities — called Solidcore. It’s like Pilates meets HIIT, it’s intense. Every holiday season they run something called The Solidays, where in two weeks you have to complete 10 classes. Full price, no discount. All you get if you accomplish it is a hoodie you cannot purchase anywhere. The only way to get it is to earn it. And I have never been able to book a class during those two weeks — the waitlist is full, and the waitlist for the waitlist is full. That’s how much people love exclusive things they can only earn.

Keith: People really underestimate the fervor that goes into programs like this — The Solidays, Nintendo Club. People hear “post-purchase experience” and think, “Oh, it’s just more emails.” But more than data, more than ideas and explanations, people respond to stories. The Solidays story, the Nintendo Club story — these are tangible things that people get genuinely excited about. And they’re not that hard, right? That’s the magic.

When someone thinks, “What does a post-purchase content experience actually look like?” — it doesn’t have to be complicated. You just need the right tool to leverage it, something like Tapp, and something to pull in the data and understand your customer base.

Ishita: When you don’t have the tools for it, it’s hard to do.

Keith: It is hard, and I think people overthink it. People go crazy over something as simple as a sweatshirt. We give out Yeti tumblers with our logo and “Data Beats Opinion” on them, and people love them. I have a giant sack of them I give away. I also have an Apple sweatshirt you can only buy inside the Apple Store. Just knowing you can’t get it anywhere else makes it feel special.

Ishita: For sure. One of the brands we work with is a nootropics company — they help people focus. One of their first prizes is a hat that says “Thinking Cap.” It’s just so good. It needs to be on brand, and it doesn’t take a lot. Where a lot of brands go wrong is they lean on discounts, and the new version of that — milestone rewards — has the same problem. “Make five purchases and you get a reward” — the concept is fine, but people aren’t surprised or delighted by it anymore. They feel like they deserve it. As a consumer, it feels like it’s already priced into the subscription or the product. It doesn’t feel like something extra.

But when you have to earn a reward through engagement, that changes everything. People get excited: “I earned this. Not everyone got it. Only the people who showed up got it.”

Keith: It’s the IKEA effect — something you work for, you value more. There’s something about putting together the furniture yourself that makes you like it more than something you just bought and brought home.

One thing I find interesting: do you know what percentage of e-commerce happens on Amazon versus off Amazon?

Ishita: I looked at these numbers about a year ago, so they might be different now — but back then it was around 40% of e-comm done through Amazon.

Keith: So Amazon is notoriously a black box. This seems like a way to connect with your customers outside of that — where you’re no longer just part of a fulfillment machine. You can keep your brand experience even through a mass-market channel like Amazon or Walmart, where you normally don’t get that.

Ishita: Absolutely. And just to clarify, that 40% is just e-commerce, not physical retail. And actually, that’s where Tapp started — the problem we wanted to solve was that 90% of commerce is not done on your website, meaning you don’t know who most of your customers are. That’s why we developed the tag functionality.

And there’s a huge opportunity there. When someone buys from you on Amazon, there’s no relationship. And with six times as many brands in every category, if you’re emailing customers five times a day, so are your competitors — everyone fighting for the same attention with the same discounts. What we want to create is a gravitational pull toward the brand. Ideally, the Tapp experience becomes the brand’s real home. Websites are kind of old school for this purpose — they’re great for shopping, but not for brand engagement.

Keith: Brand websites, yeah. They’re meant for shopping.

Ishita: Exactly. Brands have a very limited set of places to bring their brand to life — social media, website, and that’s about it. But how many people are actually following you on social? What we hear from our customers is, “Your team just brought our brand to life. I can engage with the brand in a way I’ve never been able to. Before, customers were just buying from us — that’s it.”

Keith: There’s a Bain & Company quote I love: “An increase in customer retention rates of 5% increases profits by 25 to 95%.” It doesn’t take much to move the needle. People who love you really love you and will keep buying from you. Go look at any brand’s subreddit and you’ll see a passionate community constantly talking about those products. Those are the people you want to cultivate.

Ishita: I’d take it one step further — those people will always buy from you. The real question is how you increase LTV

Keith: across the board.

Ishita: Across the board. Exactly. And those superfans are great, but what we want Tapp to do is focus on the middle 30 to 40% — the people who like you because they bought from you at some point, but don’t love you yet. They’re not in the subreddits. But how do we nudge them from liking you to loving you? How do we slowly move them toward becoming a true fan?

Keith: How do you up-level them to someone who’s just a fanatic about your brand?

Ishita: That’s where a lot of tools today fall short — they’re focused on the top 10%. But the low-hanging fruit is that middle group. Maybe you move them from 10% to 20%, maybe 30%. That’s what we’re building toward.

Keith: I have one final question: other than brands doing nothing on the back end — which we’ve covered — what’s the other biggest mistake you see direct-to-consumer brands making?

Ishita: Man, we’ve talked about so many of them. But honestly, I don’t think it’s the brand’s fault — the tooling just doesn’t exist. You can’t expect brands to build this from scratch.

Keith: The tooling, yeah.

Ishita: The tooling just doesn’t exist. So you can’t expect them to figure it out on their own.

I think the biggest mistake brands make is assuming their customers don’t care about them — that customers want a purely transactional relationship. So they think the thing that’s going to incentivize a repeat purchase is a 60% discount. In the world of acquisition, that works beautifully — 60% off, sure, I’ll try the product. But once I’ve already bought it, a discount is not going to make me fall in love with your brand.

What would make me fall in love is this: you know for certain that I bought your product because I’m trying to solve a problem. So become the trusted source I turn to for everything related to that problem. You’re the expert, and there’s so much misinformation out there. Take magnesium — it’s a hot product, I hear about it all day on TikTok. But once I’ve bought from you, Magnesium Brand, become my source of truth. Educate me. Make it fun. Make it interactive. Help me engage with this problem in a way where I feel better walking away from it. Defaulting to a discount assumes I don’t care — but I do. I spent my money to buy this product and solve this problem.

Keith: I fully agree. There’s a societal assumption that consumers are fickle — that they’ll go wherever the price is lowest. But I think you’re right: fickleness is really a pre-purchase phenomenon. Before I’ve made a decision, there are so many similar products and I’m comparing. But once I’ve decided and I’m in, I’m not looking to jump ship unless something goes wrong. What I’m looking for is help getting more out of this product.

Weber Grills did a great job of this — they released a Weber cookbook. All it is is how to use your Weber grill as well as possible. But they know: the more someone is grilling, the more they want a rotisserie, then new brushes, then a smoker. You up-level through the experience once you’re committed to the ecosystem. I’m in the Weber world now — teach me how to use it.

And this is exactly the same in tech. When I sign up for a piece of software, I’ve made the decision, I’m in. I want you to teach me how to use it so I’m getting the most out of it. We signed up for a bug and project tracking tool, and all I wanted was a call with someone to say, “I’ve already made the decision — what’s the best way to use this so we can be productive?” We’ve been using their software for six-plus years. In tech, this is obvious. But it doesn’t always grok to people when they’re dealing with a physical brand.

Ishita: Totally. And with your Weber example — wouldn’t it be so cool if they put out monthly challenges? “Here’s the grill challenge of the month — upload a picture or video of you doing it with your family.” Now it’s a community. These are all the people who chose Weber. It doesn’t take a lot.

Keith: It really doesn’t. Here’s a great example — there’s a person on Reddit who’s just been cutting up chives and posting it every single day, asking Reddit to approve or disapprove of their chive-cutting quality. They’re at day 70. And it is consistently at the top of Reddit. You log out, go in as a regular user, and it’s at the top of the feed every time they post. It’s crazy. But it’s something like that — just showing what you’re doing. People love talking about themselves. And when you tie that to a brand, it all connects.

Ishita: Makes a lot of sense.

Keith: Ish, thank you so much. This has been an absolutely wonderful conversation. I love that you’re doing something about back-end nurturing in e-com — it frustrates me so much that people don’t focus on it. It genuinely warms the cockles of my cold, dead heart. Where can people find you?

Ishita: You can always go to our website — thetapp.io, that’s T-A-P-P dot io. You can email me at ish@thetapp.io, and definitely find me on LinkedIn.

Keith: Awesome. Ish, thank you so much for your time.

Ishita: Thank you so much, Keith. This was so fun. Really appreciate you having me.

Keith: Definitely. Talk soon.

Ishita: Cheers!

Introducing SegMetrics MCP Server

Ryan Johnson — Wed, 27 May 2026 23:50:29 GMT

Your marketing data just learned to talk back.

If you’ve ever tried to get a straight answer about what’s actually working in your marketing, you know how hard that is.

Platforms don’t talk to each other. Your ad dashboard says one thing, your CRM says another, and asking an AI to sort it out usually gets you an answer that’s technically correct and completely useless. And of course it’s useless! Your AI doesn’t have the full-funnel context or the optimization methodology to know what the numbers actually mean.

We just fixed this problem.

Introducing the SegMetrics MCP Connector

Now you can connect SegMetrics to Claude, ChatGPT, or any MCP-compatible AI tool. Gone are the days of handing your AI raw data to guess at. You can give it a live connection to your actual account — parsed, normalized, and structured by experts with decades of funnel analysis experience.

With the SegMetrics MCP Servier, when you ask your AI a data question, it doesn’t just pull a surface-level metric. It determines how to best query the data, creates reports, reads the data, spots anomalies, goes one level deeper, and comes back with a real finding you can take action on.

See the SegMetrics MCP Connector in action 👇

What makes the SegMetrics MCP Server different

Yes, the SegMetrics MCP Server works with your actual SegMetrics data. That means you’re working accurate, full-funnel, lifetime attribution data that’s already parsed and normalized. No exports, no uploads, no guessing.

But there’s even more baked into our MCP Server:

The analysis methodology is baked in — The AI doesn’t just read your numbers, it knows how to analyze a funnel: where to look first, what to segment by, and what question to ask after the obvious one.
It runs multi-step queries on its own — In other words, it can spots an anomaly in the first report, pull a second report to go deeper, and surface the finding. AKA, it follows the same process an advanced analyst would run, automatically.
Every answer links to the underlying report — No mystery numbers. You always get access to the report so you can verify what the AI found and drill in further yourself.
Read-only — The MCP Server can see your data, but it cannot change anything in your account or connected systems.
Works with any MCP-compatible AI — Claude, ChatGPT, or whatever tool you’re already using.
Setup takes about a minute — Connect it quick and SegMetrics shows up as a data source in every conversation going forward.

All of it, ready the moment you open a conversation with your AI.

How to get started with the SegMetrics MCP Server

1. Stop hunting for answers you should already have

“What’s my CPA this month?” “Where are my best leads coming from?” “Which product is selling to repeat customers?”

These are questions you shouldn’t have to spend 20 minutes chasing down. With the SegMetrics MCP Connector, you don’t open a dashboard, set a date range, or export anything. You just ask — and get a real answer from your live data in seconds.

You probably have a list of questions about your marketing you’ve never gotten to. Which funnel step is bleeding the most. Which segment quietly stopped converting. Which traffic source produces buyers who actually stick around. You had the data. You didn’t have the analyst. Now you don’t need one because the SegMetrics MCP Server is like having a senior marketing analyst, on call, 24/7, who already knows your data.

2. Diagnose problems you can see but can’t explain

Your webinar funnel isn’t converting the way it used to. You can see the drop-off. You just can’t see why.

Ask something like: “Walk me through my webinar funnel and tell me where I’m losing people.” The MCP Server doesn’t just pull the drop-off report — it chains reports together automatically. You’ll see where people are falling off, who those people are, where they came from, and whether the problem is new or has been quietly building for months.

That kind of follow-up analysis used to mean opening three more reports, filtering manually, and comparing tabs. Here it takes 90 seconds and you never leave the conversation.

3. Make marketing review part of your weekly rhythm

Instead of opening your dashboards and hoping you notice something important, try building a standing question into your week. Monday morning: “What changed in my marketing last week?” Or: “What do I need to know about my funnel this week?”

You’re not replacing your dashboards. You’re adding a layer that points at what actually deserves your attention so you spend your time deciding instead of looking.

And if you’re already comfortable running AI automations, the next step is handing this off entirely: set up an agent that runs the analysis on a schedule and pings you when something needs your attention.

Bottom line: What would you ask if a senior SegMetrics analyst was available every time you opened Claude?

Try the SegMetrics MCP Connector for yourself

To test-drive the SegMetrics MCP Connector with your own data, start your 14-day free trial today.

Data beats opinion. 🚀

SegMetrics Alternatives: A Practical Guide to Choosing the Right Marketing Attribution Tool

Denise Cullom — Tue, 19 May 2026 19:49:28 GMT

You’re evaluating marketing analytics tools and want to know what alternatives exist to SegMetrics. That’s a smart place to start. The right platform will give you the clarity to grow your business. The wrong one will cost you time, money, and the confidence to make data-driven decisions.

SegMetrics is purpose-built for digital businesses that need to connect their marketing spend directly to revenue and customer lifetime value (LTV). But not every tool fits every business model. This guide explains the full landscape so you can make the right call.

First: Define the Problem You’re Actually Solving

The most common mistake marketers make when evaluating analytics tools is comparing features before defining the core problem they need to solve. Start here instead.

Ask yourself: What is the single most important question I need my analytics tool to answer?

“Which ad campaigns are actually generating revenue?” → You need a marketing attribution tool
“How are users behaving inside my product?” → You need a product analytics tool
“Which products are most profitable on my Shopify store?” → You need an e-commerce analytics platform
“How are marketing activities influencing long B2B deals?” → You need an account-based analytics tool

Once you know which category fits your core question, evaluating alternatives becomes much simpler. The sections below are organized exactly this way.

Top SegMetrics Alternatives by Use Case

1. Marketing Attribution & ROI Tracking

These are the most direct SegMetrics alternatives. They focus on connecting ad spend and marketing activity to actual sales, and they compete most closely with SegMetrics’ attribution reporting.

Wicked Reports A solid choice for subscription-based businesses. Wicked Reports tracks customer LTV and recurring revenue back to the original marketing source. It’s particularly well-suited for businesses where a customer’s first purchase is a loss leader and real value comes over time.

Hyros Hyros is built for high-ticket funnels — think webinars, phone sales, and long-form video sales letters. It uses AI-powered device matching to track customers across multiple touchpoints and platforms. If your average order value is high and your funnel has multiple steps before a sale, Hyros is worth evaluating.

Ruler Analytics A strong option for lead-generation businesses, especially service companies and agencies. Ruler tracks phone calls, form submissions, and live chat conversations back to the marketing source that generated them. If your primary conversion is a lead rather than a direct online sale, Ruler fills a real gap.

How SegMetrics differs from these alternatives: While Wicked Reports, Hyros, and Ruler Analytics all do strong ad attribution, they focus primarily on the acquisition side. SegMetrics goes deeper by integrating directly with your CRM and email marketing platform, giving you visibility into the full customer journey — including post-purchase behavior, email engagement, and how different audience segments perform over their entire lifetime with your brand.

This is why SegMetrics is a particularly strong fit for businesses that sell through email marketing, run continuity programs, or want to understand which lead sources produce customers with the highest long-term LTV — not just the highest first-sale conversion rate.

2. Product & In-App Behavior Analytics

These tools are not direct SegMetrics alternatives — they solve a different problem. They’re listed here because they frequently appear in the same search comparisons, and understanding the distinction will save you from choosing the wrong tool.

Mixpanel Mixpanel is an event-based analytics tool that tracks user behavior inside your application. It’s excellent for funnel analysis, retention cohorts, and feature adoption tracking. Product managers and engineers are the primary users.

Amplitude Similar to Mixpanel in purpose, Amplitude focuses on understanding which user behaviors predict better outcomes — higher retention, conversion to paid, or feature adoption. It’s widely used by product teams at SaaS companies.

Heap Heap takes a different approach: it automatically captures every user interaction (clicks, taps, form fills, page views) without requiring you to define events upfront. This gives you a retroactive dataset to analyze as questions emerge.

The key distinction: These tools answer “how are users behaving inside our product?” SegMetrics answers “which marketing campaigns are creating our best customers?” If your primary need is to optimize ad spend, understand lead source quality, or track LTV by acquisition channel, product analytics tools won’t solve that problem. Most businesses growing past a certain stage need both — and SegMetrics integrates with your tech stack rather than replacing it.

3. E-Commerce & Shopify Attribution

If you run a direct-to-consumer brand on Shopify, a specialized category of tools has emerged to serve your specific needs — real-time ad attribution, creative-level reporting, and contribution margin tracking.

Triple Whale Triple Whale is one of the most popular dashboards for Shopify stores. It pulls data from your ad platforms (Meta, Google, TikTok), email platform, and Shopify backend into a single view of profitability. Its “Pixel” tracks post-iOS attribution using first-party data. Triple Whale is built for e-commerce — if that’s your only model, it’s a capable tool.

Northbeam Northbeam is positioned as a more sophisticated attribution platform for high-growth e-commerce brands spending heavily across multiple ad platforms. It offers multiple attribution models and is designed for brands where the question “which channel actually drove this sale?” is genuinely hard to answer.

Where SegMetrics fits: SegMetrics also supports e-commerce attribution and Shopify integration, but it’s built for businesses that sell through a combination of e-commerce, digital products, subscriptions, or coaching — not purely transactional retail. If you run a Shopify store and sell a course, a continuity program, or have a significant CRM or email marketing component, SegMetrics gives you a unified view that Triple Whale or Northbeam can’t provide. The key SegMetrics advantage: LTV tracking across your entire business, not just your Shopify orders.

4. B2B Account-Based Attribution

For B2B companies with complex, multi-stakeholder sales cycles, a distinct category of tools exists to track marketing influence across long deal timelines.

Dreamdata Dreamdata connects marketing activities to revenue at the account level. It’s purpose-built for B2B SaaS companies where deals involve multiple contacts, multiple touchpoints over months, and a CRM (usually HubSpot or Salesforce) as the system of record. It focuses on answering: “how did our marketing contribute to this deal closing?”

Bizible / Adobe Marketo Measure An enterprise-grade multi-touch attribution platform that integrates tightly with Salesforce and Adobe Marketo. It’s built for large marketing and sales teams with sophisticated reporting needs and a significant technology investment already in place.

Where SegMetrics fits in B2B: SegMetrics is a strong fit for B2B businesses that sell primarily through digital funnels — webinars, content marketing, email sequences, and online courses — rather than through a traditional enterprise sales motion. If your B2B sales cycle involves a lead opting into an email list, nurturing through automated sequences, and converting through a checkout or discovery call, SegMetrics tracks that entire journey with precision. Explore SegMetrics’ CRM and email marketing integrations to see how it connects to your existing stack.

How SegMetrics Compares: A Quick Reference

Use Case	Best Alternative	SegMetrics Advantage
Ad attribution for subscription businesses	Wicked Reports	Full customer journey including email + CRM
High-ticket funnel tracking	Hyros	Deeper LTV visibility, no AI black box. Affordable at $57 mo.
Lead generation attribution	Ruler Analytics	Works for online sales, not just leads
In-app product behavior	Mixpanel / Amplitude	SegMetrics tracks acquisition, not in-app
Shopify DTC brands	Triple Whale	Better for mixed-model businesses (e-comm + digital products)
Enterprise B2B	Dreamdata / Bizible	SegMetrics is simpler and more affordable for digital-first B2B

How to Make Your Decision: A 3-Step Framework

Step 1: Inventory Your Integrations

Write down every tool you use to run your marketing: your CRM, email marketing platform, ad platforms, payment processor, and course or membership platform. Any analytics tool you choose must integrate with these seamlessly — otherwise you’ll spend more time importing data than using it.

SegMetrics integrates with over 130 platforms, including ActiveCampaign, ConvertKit, Keap, HubSpot, Stripe, ThriveCart, Shopify, Facebook Ads, and Google Ads.

Step 2: Identify Your North Star Metric

Every business has one metric that, if it went up, you’d know things were working. For most digital businesses, that’s one of:

Return on Ad Spend (ROAS): Are my campaigns profitable?
Customer Lifetime Value (LTV): Which customers stay and buy more?
Cost Per Acquisition (CPA): How much does it cost to get a customer?
Lead-to-Customer Rate: Which traffic sources produce buyers, not just leads?

Choose the tool that’s architecturally designed to report on your metric best. SegMetrics is purpose-built for LTV and revenue attribution across the full customer journey — a capability that most alternatives don’t offer natively.

Step 3: Match the Tool to Your Business Model

The most important question isn’t “which tool has more features?” — it’s “which tool was built for how my business actually makes money?”

Selling digital products, courses, or coaching? SegMetrics was built for this model.
Running a subscription or continuity program? SegMetrics’ LTV tracking is purpose-built for this.
Running a Shopify DTC store with no digital products? Triple Whale or Northbeam are strong fits.
B2B SaaS with enterprise sales cycles? Dreamdata is worth evaluating.
Need to understand in-app behavior? Mixpanel or Amplitude are the right tools.

Frequently Asked Questions

Q. What is the best alternative to SegMetrics? A. The best alternative depends on your business model and primary goal. For direct marketing attribution with deep CRM and email integration, the closest alternatives are Wicked Reports and Hyros. And for Shopify e-commerce brands, Triple Whale or Northbeam are commonly used. For B2B companies with long sales cycles, Dreamdata is a strong fit. However, if your business involves digital products, courses, subscriptions, or any mix of sales channels, SegMetrics’ combination of attribution tracking and lifetime value measurement is difficult to replicate with other tools.

Q. How does SegMetrics differ from Google Analytics? A. Google Analytics tracks sessions, page views, and aggregate website behavior. SegMetrics tracks individual customer journeys from first marketing touchpoint through to purchase, repeat purchase, and lifetime value. They answer different questions: Google Analytics tells you how many people visited a page; SegMetrics tells you which marketing campaigns produced customers who spent the most money over time. See a full comparison of SegMetrics vs. Google Analytics.

Q. Is SegMetrics only for e-commerce? A. No. SegMetrics is used by digital course creators, membership site owners, coaches, agencies, SaaS companies, and e-commerce brands. Its strength is tracking revenue and LTV across any business that uses email marketing, a CRM, and online payments — regardless of what you sell.

Q. Does SegMetrics work with iOS privacy changes? A. Yes. SegMetrics uses first-party data from your CRM, email platform, and payment processor to track attribution — not third-party browser cookies. This makes it resilient to iOS privacy changes and browser-level tracking restrictions that have significantly degraded the accuracy of pixel-based tools like Facebook’s native attribution.

Q. What platforms does SegMetrics integrate with? A. SegMetrics integrates with over 90 marketing tools including ActiveCampaign, ConvertKit, Keap, HubSpot, Drip, Klaviyo, Stripe, PayPal, ThriveCart, Shopify, WooCommerce, Facebook Ads, Google Ads, and many more. Browse the full integrations list here.

Q. How long does it take to set up SegMetrics? A. Most businesses can connect their core integrations and start seeing data within a day. Because SegMetrics pulls historical data from your connected platforms, you don’t have to wait months to see meaningful reports — many users see LTV and attribution data from their past 12–24 months of activity shortly after setup.

Q. Is SegMetrics suitable for agencies? A. Yes. SegMetrics offers white-label reporting capabilities that allow agencies to provide clients with branded attribution dashboards. This makes it a strong choice for agencies that manage ad spend or email marketing on behalf of digital businesses and need to demonstrate clear ROI.

Q. What’s the difference between first-touch and multi-touch attribution in SegMetrics? A. First-touch attribution credits the first marketing touchpoint a customer ever had with your business. Multi-touch attribution distributes credit across every touchpoint in the customer journey before a purchase. SegMetrics supports multiple attribution models, allowing you to see your data through different lenses — which is critical for understanding both what drives awareness and what drives conversion.

The Bottom Line

There are good tools for every use case in marketing analytics. The key is matching the tool’s core design to how your business actually generates revenue.

Start a free trial of SegMetrics or book a demo to see how it maps your specific customer journey.

The Best Marketing Attribution Software: How to Find What’s Actually Driving Revenue

Denise Cullom — Tue, 19 May 2026 18:58:05 GMT

Most marketing teams are drowning in data and starving for answers.

You have Google Analytics telling you your traffic is up. Your email platform says open rates are strong. Your ad dashboard is showing decent click-through rates. But when your CEO asks which of these activities is actually making money, you freeze, because none of those tools can tell you.

This is the attribution gap, and it’s one of the most expensive problems in modern marketing. When you can’t connect your marketing activities to actual revenue, you’re essentially flying blind. You keep spending on channels that feel productive but can’t prove their value. You cut budgets from campaigns that were quietly driving your best customers. You optimize for the wrong metrics entirely.

The solution isn’t more analytics. It’s the right kind of analytics: specifically, marketing attribution software built to answer the revenue question.

This guide breaks down why traditional analytics tools fall short, what to look for in attribution-focused platforms, and how to choose the right one for your business.

The Real Problem: Web Analytics Was Never Built for Revenue Attribution

Google Analytics is the world’s most popular analytics tool, and for good reason. It’s free, powerful, and excellent at answering traffic questions: How many people visited my site? Where did they come from? Which pages did they view?

But there’s a fundamental limitation baked into its design. It was built to measure website behavior, not revenue outcomes.

Here’s what that looks like in practice:

A potential customer clicks a Facebook ad, reads your blog post, subscribes to your email list, opens three emails over two weeks, clicks a link in your fourth email, and then purchases. GA4 will likely attribute that sale to email (the last click) or direct traffic, completely missing the Facebook ad and the blog post that started the relationship.

For e-commerce businesses with simple, single-session purchase journeys, this might be acceptable. But for digital businesses with longer sales cycles (courses, SaaS products, coaching programs, memberships), this kind of attribution is dangerously misleading.

It gets worse in the post-iOS privacy era. Browser-based cookie tracking, which most analytics tools rely on, is increasingly unreliable. Ad blockers, Safari’s Intelligent Tracking Prevention, and iOS privacy changes all create gaps in the data. The result is that standard analytics tools are undercounting conversions while overcounting or misattributing revenue.

This is why a dedicated category of marketing attribution software has emerged: tools specifically designed to bridge the gap between marketing activities and business revenue.

What Marketing Attribution Software Actually Does

Marketing attribution software is built around a single purpose: connecting every dollar of marketing spend to every dollar of revenue generated, across the full customer journey.

Rather than relying on last-click or session-based models, these tools track the complete path a customer takes from first touch to purchase and beyond. They integrate data from multiple sources, including your ad platforms, email service provider, payment processor, and CRM, and stitch it together into a coherent picture.

The key capabilities that differentiate true attribution software from general analytics include:

Multi-touch attribution models. Instead of crediting one channel for a sale, attribution software distributes credit across every touchpoint that influenced the purchase. This gives you a more accurate picture of which channels are working together to drive revenue.

Cross-channel data integration. Attribution platforms pull data from Facebook Ads, Google Ads, your email platform, your checkout system, and more, creating a unified view that no single-channel tool can provide.

Customer lifetime value tracking. Good attribution software doesn’t stop at the first purchase. It tracks customer behavior over time, so you can see which traffic sources generate your highest-value customers, not just your most frequent purchasers.

Revenue-based reporting. Instead of reporting on clicks, impressions, or open rates, attribution platforms report on actual revenue generated, allowing you to calculate true ROI for every marketing channel.

Privacy-resilient tracking. Most attribution platforms use server-side tracking and first-party data to maintain accuracy even as browser-based tracking becomes less reliable.

The Main Categories of Marketing Attribution Software

The attribution software market has expanded significantly, with different tools designed for different business types. Here’s a breakdown of the major categories and who they’re best suited for.

E-Commerce Attribution Tools

This category has grown rapidly in response to the iOS privacy changes that devastated Meta ad tracking for e-commerce brands.

Northbeam, SegMetrics Cometly and Hyros are the main players in this space. They use server-side tracking and proprietary attribution models to give e-commerce brands a more accurate picture of their ad performance than the native dashboards provide. They’re particularly strong at multi-touch attribution across paid social and search.

Best for: Direct-to-consumer e-commerce brands running significant paid ad budgets (typically $10k+/month) who need to know which ads are driving purchases.

Limitations: These tools are heavily optimized for e-commerce with short sales cycles. They’re less suited for businesses with long buyer journeys, recurring subscriptions, or complex funnel structures.

General Web Analytics Platforms

Google Analytics 4 (GA4) remains the default starting point for most businesses. It’s free, integrates deeply with Google Ads, and provides solid traffic analysis. For businesses with simple purchase funnels and primarily Google traffic, it can work well, especially if you invest time in proper event tracking setup. For a more revenue-focused alternative, see how SegMetrics compares to Google Analytics.

Adobe Analytics is the enterprise alternative, offering deep customization and real-time data processing. It’s best suited for large organizations with dedicated data teams and significant technical resources.

Best for: Businesses primarily focused on web traffic analysis, content performance, and Google Ads optimization.

Limitations: Neither tool provides meaningful multi-source attribution for businesses with complex, multi-channel funnels. Connecting email, paid social, and organic content to specific revenue outcomes requires significant custom development, and still yields incomplete data.

Product Analytics Platforms

Mixpanel and Amplitude are built for SaaS and mobile app companies that want to understand what happens after a user signs up. They’re excellent at tracking feature adoption, user flows, retention cohorts, and in-app behavior.

Best for: Product-led growth companies that need to understand how product usage drives retention and expansion revenue.

Limitations: These tools focus almost entirely on the post-conversion product experience. They provide little insight into the pre-conversion marketing journey: the channels, content, and campaigns that drove the signup in the first place. If you need revenue attribution on top of behavioral analytics, see how SegMetrics compares to Mixpanel.

CRM-Integrated Platforms

HubSpot Marketing Hub offers “closed-loop” reporting that connects marketing campaigns to deals and contacts within the HubSpot CRM. For businesses fully committed to the HubSpot ecosystem, this provides genuine revenue attribution within that walled garden.

Best for: B2B companies running primarily inbound marketing strategies with their full tech stack on HubSpot.

Limitations: The attribution is limited to what happens inside HubSpot. If your payment processing, email marketing, or advertising happens outside the platform, the picture is incomplete.

Multi-Touch Attribution Platforms for Digital Businesses

This is the category built for digital-first businesses (course creators, coaches, SaaS companies, membership sites, and agencies) that have complex funnels spanning multiple channels and long customer relationships.

SegMetrics sits firmly in this category. It was built specifically for businesses that need to connect marketing activities across the full customer journey: from the first ad click or organic visit, through email nurture sequences, to the initial purchase, and all the way through renewals, upsells, and long-term lifetime value.

Where most attribution tools ask “which ad drove the sale?”, SegMetrics asks a deeper question: “which traffic source generates the highest-value customers over time?” The difference matters enormously. A Facebook ad campaign might drive a high volume of initial purchases but attract customers who churn after 30 days. Meanwhile, a slower-burn email nurture sequence might drive fewer initial sales but attract customers who stay for years. Standard attribution tools will optimize toward the first campaign and cut the second, which is the exact opposite of what you should do.

SegMetrics surfaces this by integrating your ad platforms, email service provider, and payment processor to give you lifetime value data by source, by campaign, by email sequence, and even by individual email.

Key Factors to Evaluate When Choosing Marketing Attribution Software

Feature lists are easy to compare. Here’s what actually matters when you’re making the decision.

1. Does It Integrate With Your Existing Tech Stack?

Attribution software is only as useful as the data it can access. Before evaluating any platform, map out your current stack: Which ad platforms do you run? Which email service provider do you use? What’s your payment processor? Does the attribution tool have native integrations with all of them?

Gaps in integration mean gaps in attribution. A tool that integrates with Facebook Ads and Stripe but not your email platform will still leave you blind to a critical piece of the customer journey.

2. How Does It Handle Privacy Changes?

Browser-based cookie tracking is an increasingly unreliable foundation for attribution. Look for platforms that use server-side tracking, first-party data, and probabilistic matching to maintain accuracy as privacy regulations and browser restrictions evolve.

Ask specifically: How did their tracking accuracy change after iOS 14.5? What percentage of conversions can they attribute without relying on third-party cookies?

3. What Attribution Models Does It Support?

Different businesses need different attribution models. First-touch attribution is useful for understanding where awareness comes from. Last-touch attribution (what most tools default to) tells you what closed the deal. Multi-touch models (linear, time-decay, position-based, or data-driven) give you a more complete picture of what’s working across the funnel.

The best attribution platforms let you compare models side by side, so you can see how your channel performance changes depending on how you allocate credit.

4. Can It Track Lifetime Value, Not Just First Purchases?

This is the single most important differentiator for subscription businesses, course creators, and anyone with repeat purchase potential. If your attribution software only looks at initial conversion data, it’s missing most of the story.

A traffic source that drives low-value one-time buyers looks great on a first-purchase attribution report. The same source looks terrible when you factor in 12-month LTV. Without LTV data by source, you’re optimizing your marketing for the wrong outcome.

5. How Good Is the Support?

Analytics implementations are never plug-and-play. You will hit questions, edge cases, and configuration challenges, especially around tracking complex funnel events and integrating disparate data sources.

The difference between a platform with genuine expert support and one with a chatbot and a knowledge base can be months of wasted time. Look for platforms that offer direct access to people who understand both the technical implementation and the marketing strategy behind it.

6. Does the Pricing Scale Predictably?

Marketing analytics pricing models vary wildly. Some tools charge per contact, which gets expensive fast as your list grows. Others charge based on revenue tracked or events processed. Still others have flat monthly fees.

Map out what the tool will cost you at 2x your current scale, not just today. Surprises in your analytics bill are the last thing you need when you’re trying to grow.

Building a Complete Attribution Stack

No single tool covers everything perfectly. The most effective marketing analytics setups combine a core attribution platform with specialized tools for specific use cases.

For SEO and content performance: Ahrefs or SEMrush provide the keyword ranking, backlink analysis, and competitive intelligence that attribution platforms don’t cover. These tools tell you what’s happening in search; attribution software tells you what that search traffic is worth in revenue.

For qualitative behavior analysis: Hotjar or FullStory add session recordings and heatmaps to your quantitative data. When attribution tells you that a particular landing page is converting poorly, qualitative tools help you understand why.

For social media performance: Native analytics within each platform, or tools like Sprout Social, provide engagement and audience data that complements your revenue attribution picture.

The core of the stack, the platform that ties marketing spend to revenue, is where the most important investment decision lies. Get that right first, then layer in the specialized tools.

From Data Overload to Revenue Clarity

The marketing analytics problem most businesses face isn’t a lack of data. It’s an abundance of disconnected data that obscures more than it reveals.

Web analytics tell you traffic is up. Email analytics tell you opens are strong. Ad dashboards tell you your CPM is down. But none of them tell you whether any of it is actually building a profitable business.

Marketing attribution software exists to answer that question. The right platform connects your channels, tracks your customers across their complete journey, and tells you, with genuine confidence, which activities are driving revenue and which are just burning budget.

For digital businesses with complex funnels and long customer relationships, that clarity isn’t a nice-to-have. It’s the foundation of every smart marketing decision you make.

Start by identifying where your current analytics stack breaks down. Is it the inability to see cross-channel performance? Is it missing lifetime value data? Is it unreliable tracking after iOS changes? The answer to that question will point you toward the attribution platform built to solve it.

Ready to see what your marketing is actually worth? SegMetrics connects your ad platforms, email provider, and payment processor to give you true multi-touch attribution and lifetime value data, without the data science team. Start your free 14-day trial.

Frequently Asked Questions About Marketing Attribution Software

Q. What is marketing attribution software? A. Marketing attribution software is a category of analytics tools designed to connect your marketing activities to actual revenue. Unlike standard web analytics, attribution software tracks the full customer journey across multiple channels (ads, email, organic search, and more) and assigns credit to the touchpoints that influenced each purchase. This lets you see which campaigns, channels, and content are genuinely driving revenue rather than just traffic.

Q. How is marketing attribution software different from Google Analytics? A. Google Analytics is built primarily to measure website behavior: traffic sources, page views, and on-site engagement. It uses session-based, last-click attribution by default, which means it typically credits only the final touchpoint before a conversion and ignores everything that came before it. Marketing attribution software integrates data across your entire tech stack (ads, email, payments) and applies multi-touch models to give you a complete, revenue-focused view of your customer journey.

Q. What is multi-touch attribution? A. Multi-touch attribution is a method of distributing conversion credit across every marketing touchpoint a customer interacted with before purchasing, rather than crediting just the first or last interaction. Common multi-touch models include linear (equal credit to all touchpoints), time-decay (more credit to recent touchpoints), and position-based (more credit to the first and last touch). Multi-touch attribution gives marketers a more accurate understanding of how their channels work together to drive revenue.

Q. What is the best marketing attribution software for small businesses? A. The best marketing attribution software for small businesses depends on your funnel structure and primary channels. For digital businesses like course creators, coaches, and SaaS companies, a platform like SegMetrics is well-suited because it connects ad platforms, email providers, and payment processors to show lifetime value by source without requiring a dedicated data team. General web analytics tools like GA4 are free but limited in their ability to connect marketing activity to multi-channel revenue.

Q. How does marketing attribution software handle iOS privacy changes? A. The best marketing attribution platforms have shifted away from relying on browser-based cookies and instead use server-side tracking, first-party data collection, and probabilistic matching to maintain attribution accuracy. Server-side tracking sends conversion data directly from your server rather than the user’s browser, making it less susceptible to being blocked by ad blockers or Apple’s Intelligent Tracking Prevention. When evaluating tools, ask specifically how their tracked conversion rates changed after iOS 14.5 was released.

Q. What is customer lifetime value (LTV) attribution? A. Customer lifetime value attribution goes beyond measuring the revenue from a customer’s first purchase. It tracks the total revenue a customer generates over their entire relationship with your business and connects that value back to the original marketing source that acquired them. This is especially important for subscription businesses, membership sites, and course creators, where the most valuable customers often come from sources that look average on a first-purchase basis. Without LTV attribution, you risk defunding the channels that are actually generating your best long-term customers.

Q. How long does it take to set up marketing attribution software? A. Setup time varies significantly by platform and the complexity of your tech stack. Simpler tools with pre-built integrations can be configured in a few hours, while more robust platforms that require custom event tracking, server-side implementation, or complex funnel mapping may take several days or weeks.

SegMetrics is designed to get you up and running fast. Most users have all their tools connected and their tracking pixel installed in under 30 minutes, thanks to 120+ native integrations with the ad platforms, email providers, and payment processors that digital businesses already use. From there, your historical data syncs automatically, so you’re not starting from zero on day one.

The key factor with any platform is whether it has native integrations with your existing tools. Building custom connections adds time, cost, and ongoing maintenance. When evaluating attribution software, ask specifically which integrations are native and which require third-party connectors or developer work before you commit.

Q. Do I need a developer to use marketing attribution software? A. Not necessarily. Many modern attribution platforms are designed for marketing teams and include no-code or low-code setup processes, especially for standard integrations. SegMetrics, for example, is built specifically for marketers rather than developers. It connects to your ad platforms, email provider, and payment processor through native integrations, and most users have their full stack connected and their pixel installed in under 30 minutes without writing a single line of code.

That said, more advanced configurations across any platform (custom event tracking, server-side tagging, or non-standard funnel structures) may require some developer involvement. When evaluating tools, ask specifically whether your planned setup requires technical resources and what support is available when you hit roadblocks. With SegMetrics, every account includes access to a real support team that understands both the technical setup and the marketing strategy behind it, so you’re not left troubleshooting alone if something doesn’t go as expected.

The Best Marketing Reporting Tools in 2026: A Marketer’s Complete Guide

Denise Cullom — Tue, 19 May 2026 17:52:56 GMT

If you’re running paid ads, email campaigns, or content marketing, you already know the frustration: data is everywhere, but answers are nowhere. Your ad platform says one thing and your CRM says another. Your email tool has its own numbers. And none of them talk to each other.

Marketing reporting tools exist to solve exactly this problem. The right tool connects your data, surfaces what’s actually working, and helps you make faster, smarter decisions… without hiring a data engineer.

This guide breaks down the best marketing reporting tools available today, what each one is actually good at, and critically, which type of marketer each tool is built for.

Quick Answer: The best marketing reporting tool depends on your needs. For general BI and visualization, Power BI or Looker Studio work well. For marketers who need to track revenue attribution, customer lifetime value, and funnel performance across platforms, a specialist tool like SegMetrics is purpose-built for the job.

What Are Marketing Reporting Tools?

Marketing reporting tools are software platforms that collect, organize, and visualize data from your marketing channels, including paid ads, email, CRM, and checkout platforms, into dashboards and reports you can actually act on.

A good marketing reporting tool should answer questions like:

Which campaigns are generating the highest return on ad spend (ROAS)?
What is the lifetime value (LTV) of customers who came from a specific traffic source?
Which email sequences convert leads into paying customers most effectively?
Where are leads dropping off in my funnel?
How long does it take for a lead to become a customer?

Most general analytics tools can answer some of these questions — but rarely all of them. That’s why choosing the right type of marketing reporting tool matters as much as choosing a specific platform.

Types of Marketing Reporting Tools

Before diving into specific platforms, it helps to understand the main categories:

1. Business Intelligence (BI) Tools

These are general-purpose data platforms like Tableau, Power BI, and Looker. They can connect to almost any data source and build highly customized dashboards — but they require significant technical setup and often a dedicated analyst to maintain.

2. Native Platform Analytics

Google Analytics, Meta Ads Manager, and similar tools give you reporting within a single platform. They’re useful for channel-specific insights but create data silos that make cross-channel attribution nearly impossible.

3. Marketing Analytics & Attribution Platforms

These are purpose-built for marketers. They connect multiple data sources (ads, email, CRM, payments) and focus on revenue attribution, funnel analysis, and customer lifetime value — without requiring engineering resources.

4. Dashboard & Visualization Tools

Tools like Google Looker Studio and Databox specialize in building visual dashboards from existing data sources. They’re great for reporting but limited in their analytical depth.

Pro Tip: If your primary question is ‘which marketing activities are making us money?’ — you need an attribution platform, not a general BI tool. The setup cost and complexity of tools like Tableau is rarely worth it for marketing teams without a dedicated data engineer.

The Best Marketing Reporting Tools Compared

Tool	Best For	Pricing	Limitation
Google Looker Studio	Google channel reporting	Free	Limited cross-channel attribution
Power BI	Enterprise BI teams	From $10/user/mo	Requires technical setup
Tableau	Data analysts & large orgs	From $75/user/mo	Steep learning curve
Databox	Marketing dashboards	Free–$299/mo	Surface-level metrics only
Triple Whale	Shopify ecommerce brands	From $129/mo	Limited to ecommerce
SegMetrics	Revenue attribution & LTV	From $175/mo	Focused on marketing (not ops)

Google Looker Studio (Formerly Data Studio)

Google Looker Studio is the default free reporting tool for marketers already in the Google ecosystem. It connects natively to Google Analytics, Google Ads, Search Console, BigQuery, and dozens of third-party sources via partner connectors. You can learn more at lookerstudio.google.com.

Best for: Marketers who primarily need to report on Google Ads and organic search performance.

Strengths: Free, easy to use, excellent Google integrations, shareable reports.

Limitations: Becomes slow with large datasets. Limited data modeling means you can’t easily calculate metrics like customer LTV or multi-touch attribution across platforms.

Looker Studio is an excellent starting point, but most growing marketing teams quickly outgrow it — especially once they need to track revenue attribution beyond a single channel.

Microsoft Power BI

Power BI is Microsoft’s flagship BI platform and one of the most widely used marketing reporting tools in enterprise environments. It integrates tightly with Excel, Azure, and the broader Microsoft 365 ecosystem. More at powerbi.microsoft.com.

Best for: Companies already using Microsoft tools, or those with a dedicated analyst to manage the platform.

Strengths: Powerful data modeling, competitive pricing for Microsoft shops, handles large data volumes well.

Limitations: Significant setup time required. Marketing-specific reporting requires custom configuration. Not plug-and-play for most marketing teams.

Tableau

Tableau is the gold standard for data visualization, famous for its interactive, beautiful dashboards. It’s used by data teams at large organizations for complex, exploratory analysis. See tableau.com.

Best for: Organizations with a dedicated data team that needs deep, ad-hoc analytical capability.

Strengths: Unmatched visualization flexibility, strong community, powerful analytical depth.

Limitations: Expensive (from ~$75/user/month). Steep learning curve. Getting marketing attribution data into Tableau in a usable format is a significant data engineering project.

Databox

Databox is a marketing dashboard tool focused on pulling KPIs from multiple sources into a single visual display. It’s popular with agencies for client reporting. Learn more at databox.com.

Best for: Agencies building marketing dashboards for clients, or teams that want a quick overview of top-level metrics.

Strengths: Easy to set up, wide range of integrations, good for real-time KPI monitoring.

Limitations: Primarily a display tool — it surfaces metrics that exist in other platforms but doesn’t calculate new ones. Not suited for attribution modeling or LTV analysis.

Triple Whale

Triple Whale is a marketing analytics platform built specifically for Shopify brands. It centralizes ecommerce data from paid ads, email, and your store to give DTC brands a clearer view of their true ROAS. Details at triplewhale.com.

Best for: DTC ecommerce brands running on Shopify.

Strengths: Strong post-iOS attribution for ecommerce, clean UI, good ad-level reporting.

Limitations: Designed specifically for ecommerce. Not suitable for SaaS, info products, coaching, or course-based businesses.

SegMetrics: The Marketing Reporting Tool Built for Revenue Attribution

SegMetrics is a marketing analytics and attribution platform built for marketers who sell online — including course creators, coaches, SaaS companies, agencies, and ecommerce businesses. It connects your email platform, CRM, ad accounts, and payment processors to show you exactly which marketing activities are generating revenue and long-term customer value. Website: segmetrics.io

What Makes SegMetrics Different

Most marketing reporting tools show you what happened — pageviews, clicks, open rates. SegMetrics shows you what those actions were worth. It tracks each lead from their very first touchpoint all the way through to a purchase (and beyond), connecting ad spend to actual revenue at the individual contact level.

Key Features

Multi-Touch Attribution: See which channels, campaigns, and touchpoints actually influence purchases — not just the last click.
Lifetime Value Tracking: Understand the true LTV of leads from different sources, funnels, and campaigns over 30, 60, 90, or 365+ days.
Funnel Analytics: Identify exactly where leads are dropping off at each stage of your marketing funnel.
Lead Source Reporting: Connect ad spend to revenue at the lead level, so you know your real cost per acquisition — not just cost per click.
Email & CRM Integration: Works natively with ActiveCampaign, ConvertKit, Drip, Keap, and other major platforms.
Payment Integration: Connects with Stripe, PayPal, ThriveCart, SamCart, and other checkout tools to pull in real revenue data.

Who SegMetrics Is Built For

SegMetrics is purpose-built for online businesses where the customer journey spans multiple platforms — a Facebook ad leads to an opt-in page, then an email sequence, then a webinar, then a sale. Most tools lose the thread somewhere in that chain. SegMetrics maintains it.

It’s particularly powerful for:

Course creators and coaches who sell through email funnels
SaaS companies with longer sales cycles and trial-to-paid conversion tracking needs
Agencies managing attribution reporting for clients
Online businesses running multi-step funnels with complex attribution requirements

SegMetrics vs. Google Analytics

Google Analytics tells you how many people visited your site. SegMetrics tells you which visitors became customers, how much they spent, and which marketing activities drove them there. It’s the difference between vanity metrics and revenue intelligence.

Example: A course creator running ads to a webinar funnel can use SegMetrics to see that leads from Facebook ads have a 90-day LTV of $340, while leads from Google Ads have a 90-day LTV of $180 — even if both have a similar cost-per-lead. That insight changes where you spend your next ad dollar.

Pricing

SegMetrics starts at $57/month and scales based on the number of contacts in your account. A 14-day free trial is available. See full pricing at segmetrics.io/pricing.

How to Choose the Right Marketing Reporting Tool

The best marketing reporting tool isn’t the most powerful one — it’s the one that answers your specific questions without requiring a data engineer to maintain it. Here’s a simple framework:

Step 1: Define Your Core Question

What is the single most important thing you need your reporting tool to answer? Examples:

“Which ad campaigns are generating the most revenue?” → You need attribution reporting.
“How is my overall marketing performance trending?” → A dashboard tool may suffice.
“What’s the LTV of leads from different channels?” → You need a dedicated analytics platform.

Step 2: Audit Your Tech Stack

List every platform your marketing data lives in — your email tool, CRM, ad platforms, and payment processor. Your reporting tool needs to connect to all of them. Verify integrations before committing to any platform.

Step 3: Consider Your Team’s Technical Capacity

Tools like Tableau and Power BI are incredibly powerful but require significant technical investment to configure and maintain. If your marketing team doesn’t have a dedicated analyst, a purpose-built platform will deliver more value faster.

Step 4: Match the Tool to Your Business Model

Ecommerce on Shopify: Triple Whale or SegMetrics
SaaS or subscription businesses: SegMetrics or Chartmogul + attribution tool
Course creators, coaches, consultants: SegMetrics
Enterprise with data team: Tableau or Power BI
Google-heavy reporting needs: Looker Studio (free)
Agency client dashboards: Databox or Looker Studio

Frequently Asked Questions About Marketing Reporting Tools

Q. What is a marketing reporting tool?

A. A marketing reporting tool is software that aggregates data from your marketing channels — ads, email, CRM, and payments — and presents it in dashboards and reports. The goal is to give marketers a clear picture of which activities are working, what’s driving revenue, and where to invest next.

Q. What’s the difference between marketing reporting tools and Google Analytics?

A. Google Analytics tracks website behavior — sessions, pageviews, bounce rates, and conversions on your site. Marketing reporting tools go further: they connect traffic data to revenue data, track leads across multiple touchpoints, and calculate metrics like customer lifetime value and true return on ad spend.

Q. Do I need a marketing reporting tool if I already use a CRM?

A. A CRM tracks contacts and deals, but it doesn’t connect ad spend to revenue or show you which marketing campaigns are generating your most valuable customers. Marketing reporting tools fill that gap by stitching together your ad platforms, email, CRM, and payment data into a unified attribution view.

Q. What is marketing attribution?

A. Marketing attribution is the process of identifying which marketing touchpoints influenced a customer’s decision to purchase. Single-touch attribution (like last-click) gives all credit to the final touchpoint. Multi-touch attribution distributes credit across the full customer journey — giving you a more accurate picture of what’s actually working.

Q. How much do marketing reporting tools cost?

A. Pricing varies widely. Google Looker Studio is free. Databox starts at free and goes up to ~$299/month. SegMetrics starts at $57/month. Enterprise tools like Tableau start around $75/user/month and can run into thousands monthly for larger teams.

Q. What’s the best marketing reporting tool for small businesses?

A. For small businesses just getting started, Google Looker Studio is a solid free option for Google-channel reporting. For businesses that need to connect ad spend to actual revenue — particularly those running email funnels or paid traffic — SegMetrics offers the most value relative to complexity.

The Bottom Line

There’s no single best marketing reporting tool. The right choice depends on your business model, your tech stack, and the questions you most need answered.

If you’re a large enterprise with a data team, Power BI or Tableau gives you maximum flexibility. Or if you need free dashboards for Google data, Looker Studio works well. If you’re an ecommerce brand on Shopify, Triple Whale is purpose-built for you.

And if you’re an online business — course creator, SaaS, coach, or agency — who needs to understand the real revenue impact of your marketing across multiple platforms, SegMetrics is built for exactly that job.

→ Start your free 14-day trial at segmetrics.io

Which Marketing Tools Actually Perform? A Data-First Guide

Denise Cullom — Wed, 29 Apr 2026 15:13:08 GMT

You’re asking one of the most common questions in marketing: “Which marketing tools perform the best?” It’s a great question, but searching for a single list of “best” tools is like asking a chef for the single “best” ingredient. The best ingredient depends entirely on what you’re trying to cook.

The truth is, a top-performing tool for a B2B SaaS company might be useless for a creator selling online courses. The “best” tool isn’t about brand names or feature lists; it’s about what drives measurable results for your specific business.

Before you invest in another piece of software, let’s reframe the question. Instead of asking which tools are best, let’s ask: How do we build a marketing stack that we can prove is performing?

Before You Choose: Define Your “Performance”

The first step isn’t shopping—it’s strategy. “Performance” is a vague term until you attach it to a specific business goal. What does high performance look like for you?

For Lead Generation: Performance might be measured by Cost Per Lead (CPL) and the conversion rate from lead to customer.
For Brand Awareness: You might track share of voice, social media engagement, and direct website traffic.
For Customer Retention: The key metric is Lifetime Value (LTV) and a reduction in churn.

Your goals dictate your needs. A team focused on LTV needs a tool that can track customer behavior over months or years, not just the first click. A team focused on lead gen needs tools that integrate seamlessly with their CRM and sales process. The most important tool you can have is a clear definition of what you’re measuring.

The Core Categories of the Marketing Tools That Perform Best

With your goals defined, you can explore the tools designed to achieve them. Most high-performing marketing stacks are built from a combination of tools across these essential categories.

All-in-One & CRM Platforms

These are the central nervous systems of your marketing. They manage customer relationships, track interactions, and often bundle email, landing pages, and automation into one platform.

HubSpot: A powerful choice for B2B businesses, known for its deep integration between marketing, sales, and service hubs. Its strength is providing a unified view of the customer journey within its own ecosystem.
ActiveCampaign: Excellent for small to medium-sized businesses, especially in e-commerce and digital products. It combines a flexible CRM with some of the most powerful and intuitive email automation sequences available.

SEO & Search Marketing

These tools help you understand how customers find you on search engines and give you the data to improve your visibility.

SEMrush: An all-around workhorse for SEO. It’s fantastic for keyword research, tracking your rank for important terms, and running technical site audits to find and fix issues.
Ahrefs: Particularly strong for its backlink analysis. Ahrefs has a massive index of the web, making it one of the best tools for understanding who is linking to you (and your competitors) and why.

These platforms streamline the process of publishing content, engaging with your audience, and measuring your impact across multiple social networks.

Buffer: Known for its clean interface and straightforward scheduling. It’s a great tool for teams who want to plan and publish content efficiently without a lot of complexity.
Sprout Social: A more robust platform geared toward larger teams. It offers advanced analytics, social listening features, and collaboration tools to manage customer interactions at scale.

Email Marketing & Automation

Directly reaching your audience’s inbox remains one of the highest-ROI marketing activities. These tools help you build your list, segment your audience, and send targeted messages.

Kit (Formerly ConvertKit): Built from the ground up for creators, coaches, and infoproduct businesses. Its tagging system is flexible, and its automation rules are easy for non-technical users to set up.
Mailchimp: A classic starting point for many businesses. It’s incredibly user-friendly for sending newsletters and has been adding more sophisticated automation and e-commerce features over the years.

Content Creation & Design

Great marketing requires great creative. These tools empower teams to produce high-quality visuals and videos without needing a full-time design department.

Canva: Has made professional-looking design accessible to everyone. From social media graphics to presentations and lead magnets, its templates and drag-and-drop editor are a must-have for most marketing teams.
Loom: Perfect for creating quick, shareable screen recordings. It’s ideal for product demos, team communication, and creating helpful video content for your audience.

The Missing Piece: Tying It All Together with Analytics

Here’s the challenge: you can have the “best” tools in every category, but if they don’t talk to each other, you’re left with a collection of data silos.

Your social media tool reports “engagement.” The email platform reports “open rates.” Your ad platform reports “clicks.” But which of those activities actually led to a high-value customer who stuck around for two years? That’s the question that determines real performance.

This is where a dedicated marketing analytics platform becomes the most critical tool in your stack. While tools like Google Analytics are useful for tracking website sessions, they struggle to connect those anonymous visits to specific customers and their long-term revenue impact.

A true analytics engine does three things: 1. Integrates Your Data: It pulls data from your CRM, email platform, ad networks, and payment processor into one place. 2. Tracks the Full Journey: It connects every touchpoint—from the first ad click to the most recent purchase—to a single customer profile. 3. Attributes Revenue: It shows you exactly which channels, campaigns, and even specific emails are generating the most long-term revenue.

Without this connective layer, you’re just guessing. You’re operating on opinion, not data.

Conclusion: The Top Performer is a Connected System

The top-performing marketing “tool” isn’t a single piece of software. It’s a thoughtfully chosen stack of tools unified by a powerful analytics engine that provides a single source of truth.

Start by defining your goals. Choose the best tools for each function—SEO, email, social—to execute your strategy. But most importantly, invest in a system that connects all that data back to what matters most: revenue. When you can confidently see which marketing efforts are driving growth, you’ve built a stack that truly performs.

The real performance of your marketing stack is measured by the revenue it generates. SegMetrics connects the dots between your marketing activities and your bank account, giving you a single source of truth for what’s actually working.

SegMetrics — Articles

Attribution Is Not a Panopticon: Keith Perhac on the Unscripted SEO Podcast

Key takeaways

From agency tool to SaaS

Attribution is not a panopticon: first touch, last touch and the messy middle

Chat is the new user interface for SaaS

Asking Claude instead of logging in

Trust, but verify, and confirm fatigue

Japan, America and AI as an average machine

SOPs, task software and AI agents running the project

Zero-click and the brand channel

Hidden referrers and the website as the end of the funnel

You can’t hire enough SEOs to change the opinion of the internet

SEO for bots: wallets, voice agents and APIs

Eating his own dog food: the Mahjong game retargeting leak

150 integration pages and SEO on steroids

What’s next: SegMetrics.ai and the doing layer

Ian Garlic: How top clients vet you on YouTube (Episode 19)

Your next big client is already checking you out. You just can’t see them.

Meet Ian Garlic

Show Notes

About Data Beats Opinion

Try SegMetrics Free

Transcript: Ian Garlic – Ep 19

Multi-Touch Attribution: How It Works (With Real Examples)

Last-Touch vs. Multi-Touch Attribution

How Does Multi-Touch Attribution Work?

The 5 Most Common Multi-Touch Attribution Models

Multi-Touch Attribution Example: One $997 Course Sale, Five Answers

Which Multi-Touch Attribution Model Should You Use?

What You Need to Set Up Multi-Touch Attribution

Can AI Do Multi-Touch Attribution for You?

Frequently Asked Questions

What is multi-touch attribution in simple terms?

What is the difference between last-touch and multi-touch attribution?

Which multi-touch attribution model is best?

Is multi-touch attribution worth it for small businesses?

Why do my ad platforms report more sales than I actually made?

The Best Marketing Attribution Tool for Coaches, Course Creators and Info-Product Businesses (2026)

Why do coaches and course creators need a different attribution tool?

What should an attribution tool for info products track?

How do the main attribution tools compare for course creators and coaches?

Why is SegMetrics the best fit for info-product businesses?

How much does marketing attribution cost for a course creator?

How do I set up attribution for my course or coaching funnel?

Frequently asked questions

What is the best attribution tool for Kajabi?

What is the best attribution tool for ActiveCampaign or Keap users?

Can I track which Facebook ads lead to course sales weeks later?

Is Hyros or SegMetrics better for coaches?

Is GA4 enough for a course business?

Do I need attribution software if I only sell one course?

The bottom line

What Are the Best AI Marketing Tools? A 2026 Guide by Use Case

Best AI Marketing Tools at a Glance

How Should You Choose AI Marketing Tools?

What Are the Best AI Content Marketing Tools?

Claude

ChatGPT

Jasper

What Are the Best AI Tools for SEO and AEO?

Surfer SEO

Ahrefs

Manifold MCP (the Cheapest Ahrefs and Semrush Alternative)

How Do You Optimize Content for AI Answers?

What Are the Best AI Tools for Market Research and Competitive Analysis?

Perplexity

Claude and ChatGPT Research Modes

Call Transcription and Analysis

What Are the Best AI Tools for Social Media Marketing?

Predis.ai

Descript

Canva

What Are the Best AI Tools for Paid Advertising?

AdCreative.ai

Albert.ai

Native Platform AI

What Are the Best AI Email Marketing and Automation Tools?

HubSpot Marketing Hub

ActiveCampaign