A/B testing is a powerful tool for product managers to evaluate the impact of new features on user behavior. By splitting traffic into a control group and one or more variants, it allows you to measure the impact of changes on behavioral metrics such as engagement, conversion rates, and retention.
A/B testing, also known as split testing or bucket testing, is a method of comparing two versions of a webpage, app, or other user experience to determine which one performs better. It’s essentially an experiment where two or more variants (Version A and Version B) are shown to users at random, and statistical analysis is used to determine which variation performs better for a given conversion goal.
In the context of a website, version A might be the currently used version (control), while version B is modified in some way (treatment). For example, on an e-commerce site, version B might change the color of the purchase button or alter the site’s layout. Then, half of the site’s visitors are directed to version A and the other half to version B.
The performance of each version is assessed using metrics such as the number of clicks, form completions, purchases made, or any other factor relevant to the site’s goals. By comparing these results, website owners can make data-informed decisions about which version is more effective and should be implemented broadly.
This method allows for testing of individual elements (like headlines, images, buttons) or complete website designs, and it is a crucial part of optimizing a digital product for user engagement and conversion.