A/B testing splits traffic between two versions of the same page or flow, usually a control and one variant, so you can measure which one actually performs better instead of guessing. Visitors are assigned to a version at random, and the test runs until there's enough traffic to trust the result rather than reading a lucky week as a trend.
On websites and mobile apps, teams typically test headlines, calls to action, page layouts, and checkout steps, then compare conversion rate, click-through rate, or another action tied to the page's job. A test can double as diagnosis, too: if a redesigned page underperforms an older one, that gap points to a specific technical or usability problem worth digging into rather than a hunch. Popular A/B testing tools include Google Website Optimizer, Visual Website Optimizer, and Optimizely Vertster, and the space has since grown to include dedicated experimentation platforms built for larger traffic volumes. Whatever the tool, the same logic holds: change one variable at a time so you actually know what moved the number.
