Multivariate testing is a method of statistical testing that involves multiple variables, each of which is modified as part of the experiment to test variations of the same idea. In such experiments, sets of variations are compared to one another to determine which set performs the best.
Multivariate testing can be largely beneficial when used to achieve website conversion goals, such as getting sign-ups, clicks, or form submissions from visitors to your site.
The term multivariate testing encompasses several other types of testing which can be analyzed and selected by a research team depending on the objective of the test.
Full factorial testing is the first level of multivariate testing that involves dividing web traffic equally for each testing combination. This can be particularly useful to learn which website variation performs the best and which element had the greatest impact on achieving your optimal objective. Full factorial testing is the ideal choice for teams that want to run multiple tests due to its simplicity and straightforward methodology, but should be noted that it requires a relatively large amount of traffic and therefore may not be practical for the majority of websites and pages.
Alternatively, partial or fractional factorial testing reveals only a fraction of the results of a multivariate test. In website testing, for example, these types of tests wouldn’t analyze every element on a web page but only a fraction of them. Partial or fractional factorial testing can be wrought with complicated mathematical equations but it typically requires less website traffic so it can be a good option for newer websites.
Multivariate testing is used in statistical tests as a way to observe how radical changes in variations can have an effect on the metrics measured during the experiment. In multivariate statistics, more than one outcome variable is analyzed to learn more about the relationship between each set of variables. This may include several rounds of both univariate and multivariate analyses.
To conduct a Multivariate test, you need a minimum of two variations to reach a conclusion that reveals which variation will be most effective in reaching the optimal outcome.
Multivariate testing is particularly useful in website testing and optimization as multiple components are involved in the performance success of any given web page. If a marketing team wants to determine whether a certain web page is successful in collecting a specific number of sign-ups, the team might run a series of multivariate tests featuring alterations of different aspects of the web page.
For example, a tester could alter multiple components at once, such as the copy, imagery, and other visual elements to determine not only which elements are most likely to result in optimal performance but also how they’re configured on the page. Multivariate testing enables the most granular level of testing web page components and generally requires less effort to alter these components for each round of testing than A/B experiments.
Each variation can be tested against the others to gauge the best-performing combination of elements that is likely to lead to the ultimate goal, like form conversions, clicks, or other user actions.
Multivariate testing and A/B testing are both used to test the effectiveness of a web page’s elements to achieve conversions, but multivariate testing is often the preferred method.
Rather than running multiple tests one after another to test a single hypothesis, as would occur with A/B testing, multivariate testing only requires a single test to reach the same conclusion. With multivariate testing, a greater number of variations can be tested in a shorter amount of time.
However, the simplicity of A/B testing lies in the dividing of web visitors into two distinct groups: Group A and Group B. With multivariate testing, the groups are divided into even smaller segments depending on the number of versions tested, which can make it more difficult to achieve meaningful results.
A/B testing typically requires subsequent tests to reach the desired result, whereas multivariate testing can be completed in a shorter amount of time with less effort.
And finally, A/B testing is typically more effective for analyzing drastically different changes on two or more given web pages, while multivariate testing is best to test for varying combinations of elements.
While multivariate testing allows researchers to test individual page elements and mix and match these elements to see which combination works best, split URL testing involves a test of the entire page. In such instances, multiple URLs that have the same end goal—such as to create conversions—are tested against each other to determine the best-performing URL.
In website testing and optimization, you may find multivariate testing to be the more time-friendly option as creating an entirely separate URL to test against a control page can be a big undertaking. Another disadvantage of split URL testing is the inability to test the individual elements on a page, which would require a separate multivariate test to be run.
Follow these steps for an efficient and successful multivariate test within your conversion rate optimization strategy:
Not only does multivariate testing allow you to test multiple combinations of elements on a page, but it also provides learnings as to how these elements work with each other. When configuring combinations for testing, the possibilities are endless and it can be easy to get wrapped up in the infinite number of combinations. The learnings you take from multivariate testing can also be applied to future web page design as you build your web presence.
Additionally, for folks looking to take the guesswork out of website testing and optimization, Intellimize Continuous ConversionTM incorporates multivariate testing with a layer of machine learning that determines to whom and how often a variation is shown. This technology allows users to test an endless number of variations with speed and precision, maximizing resources and revenue.