A/B testing (also called ) compares two versions of something to see which performs better. You show version A to half your visitors and version B to the other half, then measure which converts more.
What is A/B Testing?
A/B testing removes guesswork from website changes. Instead of assuming a new button color will increase clicks, you test it against the original and let data decide.
How it works:
- Create two versions (A = control, B = variant)
- Split traffic randomly between versions
- Run test until statistically significant
- Implement the winner
The key is changing only one element at a time. If you change the button color AND the headline, you won't know which change caused the difference.
Why A/B Testing Matters for E-commerce
A/B testing provides data to inform decisions about website changes. Example: A 0.5% difference on 100,000 visitors equals 500 different conversions.
Common e-commerce tests:
- button text and color
- Product page layouts
- Pricing display formats
- Checkout flow steps
- Navigation structure
- Trust badge placement
- Hero image variations
Statistical Significance
Don't stop tests early when one version "looks" better. Random chance causes early fluctuations. Most tools require 95% before declaring a winner.
Sample size matters. Testing on 100 visitors won't give reliable results. You typically need thousands of conversions (not just visitors) per variation.
Test duration: Run tests for at least one full business cycle (usually 2-4 weeks) to account for day-of-week and seasonal variations.
A/B Testing Tools
Free options: Google Optimize (sunset), VWO free tier
Paid options: Optimizely, VWO, AB Tasty, Convert
E-commerce specific: Many platforms like Shopify have built-in or app-based testing.
A/B Testing vs Multivariate Testing
A/B testing compares two or more separate versions of a page to see which performs better, usually changing one main element at a time. Multivariate testing changes several elements at once and tests the combinations to find the best mix. A/B testing needs less traffic, while multivariate testing needs much more.
Common Mistakes
- Stopping tests too early
- Testing too many changes at once
- Not tracking the right metric
- Ignoring segment differences
- Running tests without enough traffic
