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Multivariate Test

UX Glossary - Multivariate Test

What is Multivariate Testing?

Multivariate Testing is a method of testing multiple variables simultaneously to determine which combination of changes produces the best results. Unlike A/B testing which tests one variable at a time, multivariate testing examines how different elements interact with each other and identifies the optimal combination of multiple page elements.

In multivariate testing, different versions of multiple page elements (such as headlines, images, buttons, and layouts) are tested in various combinations to see which combination performs best. This method helps understand not just which individual elements work, but how they work together to influence user behavior and conversion rates.

Why is Multivariate Testing Important?

Multivariate Testing is important because it reveals how different page elements interact with each other, which can't be discovered through A/B testing alone. It allows teams to optimize multiple elements simultaneously and can lead to greater improvements than testing elements individually. This method is particularly valuable for complex pages with multiple conversion elements.

Multivariate testing can uncover unexpected interactions between elements and help identify the most impactful combination of changes. It's especially useful for high-traffic sites where you can quickly gather statistically significant data across multiple variations.

How to Conduct Multivariate Testing?

To conduct multivariate testing, identify the elements you want to test, create different versions of each element, use testing tools to create all possible combinations, ensure you have sufficient traffic for statistical significance, run the test for an appropriate duration, and analyze results to identify the winning combination.

Best practices include limiting the number of variables to avoid requiring excessive traffic, focusing on elements that are likely to impact your key metrics, ensuring each variation is significantly different, having a clear hypothesis for each element being tested, and being prepared for longer test durations due to the increased number of combinations being tested.

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