A/B testing, also known as split testing or bucket testing, is a research method that compares two different variants with each other. The differentiation from each other can lie, for example, in the arrangement of elements in a mobile app.
The goal of A/B testing is to determine which of the two alternatives, A or B, is preferred by the target audience. A/B testing helps to find out how the change of variants affects user behavior.
Reduce bounce rate: A/B testing helps to compare different elements of your app or website (like the arrangement of buttons or the color scheme) to find out what keeps your users active.
Save money: A/B testing can be done with the help of SaaS tools. This makes it a cost-effective testing method.
Increase conversion rate and ROI: Sometimes small changes can have a huge impact!
Solve user pain points: Your users are likely to face common pain points in your app, e.g., confusing navigation or copy. This will lead to bad UX. By analyzing and comparing user behavior, you can recognize problems and solve them.
Reduce risks: Introducing major changes to your product can be risky. What happens when the users reject the change? Test changes to see if the changes are worth it or get rejected by the users.
Split testing: The two versions of your product are stored on two different app versions or URLs.
Multivariate testing (MVT): MVT can be used when you make changes in different sections of the product (e.g., the button color.) The MVT tests all possible combinations of the changes (e.g., button1 + color1; button1 + color2; button2 + color1; button2 + color2.) After the test you know which combination gives the best results.
Multi-screen testing: With multi-screen testing, you analyze the overall conversion rate when you make changes in repeating elements like testimonials.
Funnel multi-screen testing: This is another type of multi-screen testing. Here you don't just change the repeating elements — you take all screens of the conversion funnel and redesign them. Again, you analyze the changes in the conversion rate.
Check out our article on how to enhance your experiments with qualitative data, and the best tools you can use to run A/B tests.