What is A/B Testing?
A/B testing is showing two versions of a page (or element) to different users and measuring which drives more conversions.
Why A/B Testing matters
Requires statistical significance (usually 95%+ confidence). Tools: VWO, Optimizely, Google Optimize (sunset 2023), PostHog, Convert. Minimum traffic needed: ~1000 conversions per variant for meaningful results.
Common mistakes with A/B Testing
- 1
Calling A/B tests at 95% confidence with 200 conversions. You need at least 1,000 conversions per variant for reliable results.
- 2
Testing irrelevant micro-changes (button color) instead of high-leverage layout, value prop, and pricing changes.
- 3
Optimizing only the checkout. The biggest CVR lifts usually come from product page and pricing clarity.
How to improve A/B Testing
Prioritize tests with PXL or PIE frameworks: pages with the most traffic + biggest revenue impact + lowest implementation cost win.
Run qualitative research first — heatmaps, session replays, user interviews — before quantitative tests.
Sequence tests: pricing/value prop → product page → cart → checkout. Higher in the funnel = bigger lift.
Common questions about A/B Testing
What is A/B Testing?▾
Why does A/B Testing matter for marketing teams?▾
Related terms
Need help applying A/B Testing to your business?
Book a free 30-min audit. We will benchmark your A/B Testing against your industry and flag what to fix first.
Book a free audit