Use this skill when planning, running, or analyzing A/B tests on landing pages.
Each A/B test should change exactly one element: the headline, the CTA copy, the hero image, or the pricing display. Testing multiple changes simultaneously makes it impossible to know what drove the result.
Test in this order: headline, primary CTA, hero image, pricing display, social proof placement. These elements have the highest potential impact. Testing button border radius or footer color wastes statistical power.
A test is not complete when you feel confident — it's complete when you reach 95% statistical significance with at least 200 conversions per variant. Stopping early because one variant looks better is the most common A/B testing mistake.
A headline that wins for paid traffic may lose for organic traffic. Always segment results by traffic source, device type, and new vs. returning visitors before drawing conclusions about a winning variant.
Maintain a test log with hypothesis, variant description, run dates, sample size, result, and what you learned. Teams that document tests compound learning over time. Teams that don't repeat the same mistakes.
ElementExpected LiftHeadline5–30%CTA copy5–15%Hero image5–20%Form length10–50%Social proof position5–15%
After applying these practices, validate with real AI-simulated user testing.
Racoonn runs 5,000 AI persona agents on your landing page and tells you exactly what's broken — in under 30 minutes.
→ API coming soon — Join the waitlist for early access: racoonn.me