A/B testing (also known as split testing or bucket testing) is a method of comparing two versions of a webpage or app against each other to determine which one performs better. AB testing is essentially an experiment where two or more variants of a page are shown to users at random, and statistical analysis is used to determine which variation performs better for a given conversion goal.
Running an AB test that directly compares a variation against a current experience lets you ask focused questions about changes to your website or app, and then collect data about the impact of that change.
Testing takes the guesswork out of website optimization and enables data-informed decisions that shift business conversations from “we think” to “we know.” By measuring the impact that changes have on your metrics, you can ensure that every change produces positive results.
In an A/B test, you take a webpage or app screen and modify it to create a second version of the same page. This change can be as simple as a single headline or button, or be a complete redesign of the page. Then, half of your traffic is shown the original version of the page (known as the control) and half are shown the modified version of the page (the variation).
As visitors are served either the control or variation, their engagement with each experience is measured and collected in an analytics dashboard and analyzed through a statistical engine. You can then determine whether changing the experience had a positive, negative, or no effect on visitor behavior.
A/B testing allows individuals, teams, and companies to make careful changes to their user experiences while collecting data on the results. This allows them to construct hypotheses, and to learn better why certain elements of their experiences impact user behavior. In another way, they can be proven wrong—their opinion about the best experience for a given goal can be proven wrong through an A/B test.
More than just answering a one-off question or settling a disagreement, AB testing can be used consistently to continually improve a given experience, improving a single goal like conversion rate over time.
For instance, a B2B technology company may want to improve their sales lead quality and volume from campaign landing pages. In order to achieve that goal, the team would try A/B testing changes to the headline, visual imagery, form fields, call to action, and overall layout of the page.
Testing one change at a time helps them pinpoint which changes had an effect on their visitors’ behavior, and which ones did not. Over time, they can combine the effect of multiple winning changes from experiments to demonstrate the measurable improvement of the new experience over the old one.