Multivariate Analysis for UX Designers: A Complete Guide

Posted on May 14, 2024 | Updated on May 22, 2024

User experience (UX) design involves a lot of ongoing testing. The only way to know if a site’s design works well is to monitor its performance and keep comparing alternatives. Multivariate analysis can be a huge help in this endeavor.

Most UX designers are already familiar with A/B testing, but multivariate approaches offer a promising alternative. Here’s everything you need to know about this kind of analysis and when to implement it.

What Is Multivariate Analysis?

Whereas A/B testing typically compares one element at a time, multivariate analysis compares multiple. In a UX context, these variables are design changes, like using gifs instead of static images, different button placement or using a new font.

Multivariate testing is easier to understand when thinking of it in action. So, imagine an e-commerce business wanted to compare the impact of a few design changes on conversion rates. They develop two different product images and two calls to action (CTAs).

A/B tests would create two different versions of the site at a time to compare the impact of a single variable. Multivariate analysis would compare different combinations by comparing the image and CTA possibilities simultaneously. 

One version would have Image A and CTA A, another would have Image A and CTA B, another would have Image B and CTA A and the final would have Image B and CTA B. Comparing all four variants at once reveals which combination is most effective.

Benefits of Multivariate Analysis

A/B testing and customer journey analysis remain the most popular research methods for conversion rate optimization (CRO), but multivariate testing has several unique advantages. Here are a few of the most significant. 

Precision

First, multivariate analysis is precise. A/B testing is only reliable when comparing single changes. Otherwise, variants in the test may feature multiple variables, making it difficult to attribute the difference to a specific factor. Multivariate helps narrow down cause-and-effect by providing more versions of the site to compare.

Achieving the same precision with A/B testing requires running multiple tests in succession to review the impact of single changes. However, this is highly inefficient. It also heightens the risk of one of the most common pitfalls of A/B testing — over-applying averages. The result of each test produces an average, which may not accurately reflect most users, so building on these results can be misleading.

Multivariate analysis produces reliable, precise results in fewer steps. Looking at each variant in isolation reduces dependence on averages, too.

Context

Similarly, multivariate testing provides crucial context to UX decision-making. Real-world trends and behaviors don’t happen in a vacuum. Rather, they’re often the result of complex relationships. These relationships are precisely what multivariate analysis compares.

Combining the most effective variable of one factor with that of another won’t necessarily produce the optimal overall outcome. Sometimes, the combination of two lower-performing factors is more impactful than that of high-performing individual changes.

These unpredictable relationships are impossible to account for in A/B testing. Multivariate analysis helps because instead of comparing individual factors, it looks for the most effective combination.

Efficiency

Multivariate testing is also far more efficient than other CRO methods. Any analysis should last for at least two weeks to provide sufficient data. However, A/B tests last an average of 35 days, and many take six weeks or more. That’s a long time in the fast-moving world of website management.

Conventional tests can take so long because multiple analyses must happen in succession when only measuring one factor at a time. Multivariate analysis minimizes this testing time by comparing multiple combinations in one test.

A single multivariate test can reveal as much as several A/B tests because they measure more variables. Even if the analysis takes a few weeks to gather enough data, it will likely only require one test period, leading to quicker changes for faster ROIs.

Potential Drawbacks

Of course, multivariate analysis has some downsides, too. It’s important to understand these limitations before applying this testing method for optimal results.

High Traffic Requirements

One of multivariate testing’s biggest drawbacks is that it requires a lot of site traffic. That’s because the possible combinations of a few factors can be exponentially larger than the number of variables themselves.

In the earlier example, the theoretical e-commerce site measured two variants of two variables, leading to four different site versions for a multivariate test. Consequently, they’d need twice as much traffic to get the same statistical significance for each version as an A/B test analyzing just one of those variants.

The number of versions needed can skyrocket as you introduce new variables. As a result, each combination will see increasingly limited sample sizes of users. Websites with low traffic may struggle to get reliable insights from this testing at a certain point.

Test Complexity

Similarly, setting up a multivariate analysis can be complicated. Perhaps the biggest advantage of A/B testing is that it’s easy to implement and adapt to different scenarios. Multivariate alternatives, by contrast, can offer more insight but at the cost of increased complexity.

Even a relatively simple multivariate test may involve creating four or more different versions of a website. That can take significant time and resources. Businesses must also contend with the difficulty of managing all four versions across the test timeline and organizing the resulting data.

Larger, more complex data sets can contain more helpful insights, but it’s harder to pull those takeaways from the data. Businesses with limited staff numbers and minimal analytics expertise may find this challenging.

When to Use Multivariate Analysis

Given these benefits and drawbacks, multivariate analysis is an ideal tool in some situations but not in others. Understanding when to apply it is key to making the most of it.

Generally speaking, multivariate testing is better for large websites to provide enough traffic to each site version. Many experts recommend getting between 1,000 and 5,000 weekly visitors for A/B testing. Considering multivariate tests need more traffic, aim for the upper end of that range or beyond.

Multivariate testing is also best for UX designers planning on making incremental changes instead of complete redesigns. This will produce more conclusive results and limit the time and resources spent on designing each site version. Similarly, this kind of analysis is best for businesses that can make design changes efficiently and have experience in managing large data sets.

Multivariate Testing Best Practices

UX designers deciding to use multivariate testing can take it further with a few best practices. Setting a sufficient test length is a good start. Allow at least two weeks to pass before comparing results to provide statistically significant data. Smaller sites may want to wait longer or try a different method.

Remember that variations don’t have to be dramatically different, especially when comparing combinations of multiple factors. Accessibility is one of the biggest UX trends of 2024 and often involves smaller changes like larger buttons or high-contrast text. Experiment with larger combinations of these incremental changes instead of fewer but bigger ones.

UX designers should also look at the details of the test results. Don’t just see which version got more traffic, but analyze the specifics of how users behaved differently across each version. This more detailed analysis will help make sense of the results and get more relevant, real-world takeaways.

Because multivariate analysis can be complex, it’s easiest to use a third-party testing tool. Compare available options and choose a service that fits your needs to simplify and streamline the testing, data gathering and analysis process.

Multivariate Testing Is an Essential UX Tool

Multivariate analysis is a helpful alternative to A/B testing, particularly for larger sites. When designers know what this method offers and how to apply it, they can make more informed UX decisions.

Making incremental changes is key to ongoing success in web design. Multivariate testing can be the ideal tool for this goal if businesses use it properly.

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