How to Test Effectively

You’ve built your funnel, crafted the perfect headline, created the ideal lead magnet PDF, and maybe even added a testimonial or two. It looks great. But is it working?

You don’t want to launch your funnel and then just hope for the best — maybe you will track opt-ins or watch a conversion number here and there. But that’s like building a race car and never checking the engine. You might be moving… but are you actually winning?

The truth is, if you’re not testing, you’re guessing. And when it comes to turning clicks into customers, guessing is expensive.

Here’s how to take control — by understanding what to test, how to do it, and where to focus first:

• Learn how testing can help boost your funnel’s performance
• Get clear on the steps your audience takes from first click to final action
• Spot the most important metrics to track—like opt-ins, conversions, and where people drop off
• Discover how to run A/B tests that actually tell you what’s working
• Figure out which tests to run first based on what’s easiest and most likely to move the needle

STEP 1: Map the customer journey

• Identify all funnel touchpoints: ad → landing page → opt-in → thank-you page → email → purchase
• Visual funnel mapping
• Emphasize intent at each stage (e.g., click, opt-in, open email)
• Sketch the journey for your current or planned campaign

STEP 2: Identify the key metrics to track.

• Click-Through Rate (CTR): % of users who click through from an ad or email
• Opt-In Rate: % of visitors who sign up or submit their info
• Conversion Rate: % of leads that take the desired final action (purchase, booking, etc.)
• Drop-Off Rate: Where users are leaving the funnel
• Cost per Lead/Acquisition

STEP 3: What to test

• Headline and Subheadings – clarity, benefit-driven
• Call to Action (CTA) – wording, color, placement
• Form Fields – number of fields, required info
• Page Layout – flow, order of content, trust signals (testimonials, logos)
• Visuals and Media – hero images, explainer videos
• Copywriting – emotional appeal vs. logical, short vs. detailed

STEP 4: Design some A/B or Multivariate tests

• Define a clear hypothesis (e.g., “A shorter form will increase opt-ins”)
• Change ONE variable per test
• Use statistical significance calculators (e.g., VWO, Optimizely)
• Sample size matters – don’t make decisions off 10 visits
• Run test long enough (at least a week unless traffic is high)

STEP 5: Prioritizing What to Test

Use the ICE Framework

• Impact – Will it move the needle?
• Confidence – Do we believe this change will work?
• Ease – How hard is it to implement?
• Score each potential test 1–10 and focus on high-ICE tests first.

STEP 6: Interpreting Results & Next Steps

• Look for statistically significant improvements
• Consider how changes affect downstream metrics (e.g., more opt-ins but worse leads?)
• Roll out winning variants and test the next hypothesis