What is A/B Testing?
A/B testing (also called split testing) is a method for data-driven optimization where two variants (A and B) are compared. Traffic is randomly split between both versions, and the version with better performance (more conversions, higher click rate, etc.) wins. A/B tests can be conducted for anything: headlines, button colors, images, ad copy, email subject lines, prices. Statistical significance is important - the test must run long enough to provide reliable results. Tools like Google Optimize, Optimizely, or VWO enable A/B tests without programming. Multivariate tests test multiple elements simultaneously.
Key Points
- Change only one element per test
- Wait for statistical significance (min. 100 conversions)
- Formulate hypothesis before test
- Split traffic 50/50 between both variants
- Winner variant becomes new baseline
- Test continuously: There's always optimization potential
Practical Example
“Through A/B testing of the headline, conversion rate increased from 2.3% to 3.8% - an uplift of 65%.”
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