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What is A/B testing?

A/B testing is a method of experimental research used in marketing analytics to find out which of two options is better for achieving a certain goal.

Typically, A/B testing compares two versions of same website page, banner ad, email, or other piece of content with different variations of the same element (such as headline, colors, text, shape, button placement, etc.). At the same time, one version is shown to one group of users, and the other to another group.

Further, with the help of data analysis from these two groups, it is determined which version is more effective, that is, it gives a better result in accordance with the set goal. For example, a website with more clicks to the order button or with more sales.

A/B testing is a very effective tool for determining optimal solutions in advertising and marketing, as well as for increasing website conversions and improving interaction with the audience.

What is the purpose of A/B testing?

• Determine which of the two (or more) versions of the studied element will lead to better results relative to a certain metric;
• Determining how significant the identified differences between versions are. This usually involves using statistical methods such as t-tests or non-parametric tests to determine whether differences between samples are statistically significant or may be due to random variation;
• Reducing the risk of making decisions based on assumptions or intuition. Instead, A/B testing allows you to examine real-world user behavior and obtain objective data to make informed decisions about optimizing the website, ad, product, or other element being tested.