Multivariate testing, also known as split testing or A/B testing, is a method of experimentation in which multiple variables are changed and tested simultaneously to understand their combined impact on a given outcome. As such, it is often used to optimize the performance of websites, software, and other types of digital experiences.
In a multivariate test, the tested variables are presented in different combinations to different, selected groups of users. Their goal is to identify the combination of variables that performs best on a particular metric, such as engagement, customer satisfaction, or conversion rate optimization.
For example, a multivariate test can be used to test different combinations of colors, fonts, and layout elements on a website to determine which combination performs best in terms of conversion rate or another desired outcome. Multivariate testing tools also allow development teams to test and optimize multiple aspects of an experience simultaneously helping developers to make more informed decisions about how to optimize designed products and services.
Multivariate testing can be a powerful tool for improving the performance of a product or service, but it requires careful planning and analysis to ensure that results are statistically significant and can be accurately attributed to the variables tested. However, multivariate tests can be more complex to set up and manage than other types of tests, and more resources (traffic or data) may be required to achieve statistical significance.
Updated 2023 Editor’s note: We originally published this post in July 2021, when we had just released Unleash 4.0. In Unleash 4.7 (released in February 2022) we introduced a feature we call impression data. Impression data offers more flexibility and makes gathering data for experiments much easier. Refer to the guide How to use impression data for […]
