This is what A/B-tests are, and this is why you want to use them

Does a website convert better with a red button or a blue one? Does the implementation of a specific algorithm lead to performance improvements? Would this web application benefit from a different navigation? When developing something new or making changes to an existing product, you want the new version to be better than the original. To check what actually works better, we use A/B-tests.

An A/B-test (also known as a split test) is a relatively simple way to determine which of your choices have a positive impact on the behavior of your end-users. Your users are randomly divided into two groups (group 'A' and group 'B'), and each group is presented with a different version of your (digital) product. By analyzing the behavior of both groups (such as click patterns, app usage, duration of engagement, loading time, etc.), you gain clear insights into which of the two versions produces better results in the areas that matter to you. It's evidence-based development at its finest.

Let's say you want to add a button to your web application that allows people to contact you. Where do you place this button? What color should the button be? What text should be on that button? Does adding an arrow to the button help? With an A/B-test, you can substantiate your choices because you have tested them in practice. Especially for large e-commerce companies, a conversion increase of 0.1% can already result in a significant increase in revenue.

A/B-tests are particularly relevant when developing new features or transitioning to a new website. We frequently use A/B-tests in such scenarios to gain precise insights into whether the modifications actually enhance performance. This way, we deliver an end product that has been thoroughly tested in the market, and we can make the exact adjustments that bring the most benefits to your company. It is essential, however, that your software or website is used by a sufficient number of people. An A/B-test conducted on a population of 20 visitors ultimately doesn't provide significant insights.

You can conduct A/B-tests on a small or large scale, depending on your preferences. For example, you can analyze the click behavior of a specific button by testing different colors, or you can create two completely different frontends for Group A and Group B. To mitigate risks (e.g., if the new frontend is unsuccessful and visitors abandon the website), you can adjust the sizes of Group A and Group B. It's also possible to track live results and set specific durations for your tests, as well as determine at what results you'll proportionally adjust the size of a particular group.

A/B-tests are a valuable tool as they enable data-driven decision-making in software or website development. By comparing the behavior of two user groups, you can determine whether a new feature or change is an improvement over the original. It eliminates guesswork about what looks "better" and provides insights based on data, allowing you to make informed decisions about future developments.