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. This allows them to construct hypotheses and to learn what elements and optimizations of their experiences impact user behavior the most. You can then determine whether changing the experience (variation or B) had a positive, negative or neutral effect against the baseline (control or A).Ī/B testing allows individuals, teams and companies to make careful changes to their user experiences while collecting data on the impact it makes.

Then, half of your traffic is shown the original version of the page (known as control or A) and half are shown the modified version of the page (the variation or B).Īs visitors are served either the control or variation, their engagement with each experience is measured and collected in a dashboard and analyzed through a statistical engine. This change can be as simple as a single headline, button or be a complete redesign of the page. In an A/B test, you take a webpage or app screen and modify it to create a second version of the same page. 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. Running an A/B 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.

A/B 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.

A/B testing (also known as split testing or bucket testing) is a methodology for comparing two versions of a webpage or app against each other to determine which one performs better.

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