A/B Testing
Definition
A method of comparing two versions of a webpage, ad, or email to determine which performs better. One version (A) is tested against an alternate version (B), and the results help optimize conversions.
Example Use Case
An e-commerce store might test two different checkout button colors—one red and one green—to see which one gets more clicks. If the green button has a higher conversion rate, it becomes the default choice for all users.
A/B testing replaces opinion with evidence. Instead of debating which headline, colour, or layout is better, you split traffic between two versions and let observed behaviour decide. The discipline matters most when a page already receives meaningful traffic, because the test is only as trustworthy as the volume behind it.
The practical constraint is traffic. A test needs enough conversions in each variant before a difference means anything, which is why small sites often get more value from fixing obvious usability problems than from running tests. Test duration matters too: stopping the moment one variant pulls ahead is the most common way to fool yourself.
Keep to one variable per test where possible. Testing a new headline and a new hero image together can produce a winner without telling you which change caused the improvement. See split testing and multivariate testing for the multi-variable case.
