A/B testing vs multivariate testing: When to use each

Here's a common scenario: Your team wants to test a new checkout flow. Should you run an A/B test comparing the old flow to the new one? Or a multivariate test that isolates which specific elements drive conversions?

Choose wrong, and you'll either spend months collecting data for an unnecessarily complex test or miss the insights that could increase your conversion rate.

Both A/B and multivariate testing help teams make better decisions. But they're designed for different situations, require different traffic levels, and answer fundamentally different questions. Let's break down when to use each—and how modern experimentation platforms make both approaches faster to execute.

An A/B test, also known as a split test, compares two versions of a feature, product update, or web page to determine which one is better at meeting a specific conversion goal. This method of testing can also be used to compare two versions of an email, app interface, or ad.

A multivariate test is used to gauge how variations across numerous versions of a feature, product update, or web page perform when combined. Very similar yet unique testing versions are made for each combination of variants to determine which one has the best conversion rate. This type of experiment can tell you which specific elements have the biggest impact on user engagement and help to optimize individual elements of a product interface or webpage. 

Examples:

An A/B test might compare two pages with completely different headlines, text, CTAs, and images. A multivariate test in the same kind of experiment might compare identical pages, except that the text is in a different font and the CTA is a different size. Four pages are created so that all the possible variant combinations are compared (Font 1/CTA 1, Font 1/CTA 2, Font 2/CTA 1, Font 2/CTA2).

Although some of the same principles and tools are involved, A/B tests and multivariate tests differ in a number of ways. 

Many people consider multivariate tests to be a more complex version of an A/B test because there are more possible variations. It’s also not an either/or scenario like an A/B test. There can be dozens of variable combinations to compare and contrast. You are also testing how variables interact with one another on the page, which isn’t the case with an A/B test. 

An A/B test is only going to involve two versions of a feature release, interface, or webpage (at most, they sometimes involve three or four). A multivariate test, on the other hand, can include dozens of different versions of the feature because numerous variable combinations are being tested. 

The traffic needs to be evenly split between the web pages in both A/B tests and multivariate tests. That means to get a statistically significant result with a multivariate test, which can include many page versions, you’ll need more overall traffic than an A/B test that only has two page versions. 

For example, if your landing page gets 1,000 views in a week, each page in an A/B test would get 500 views. However, if you were to do a multivariate test that had 12 page versions, each one would only get around 83 views. 

A/B tests are usually easier to decipher because there are fewer results to compare. The subtlety of the variations and the number of different pages can sometimes make multivariate test results less clear-cut.  

Both A/B and multivariate tests can give you insights into the comprehensive impact of any changes you make. To get the most out of your tests, look beyond primary KPIs and use an analytics solution that has the capability to measure downstream behavioral changes in addition to short-term impact. You want to make sure test results that appear successful in the short-term also have a positive effect in the long-term. If you improve conversions but also increase churn (for example), your experiment may look good on paper but be detrimental overall. 

In addition to tracking the performance of primary metrics, define guardrail metrics that help spot the potential unintended effects of your tests. Create metric trees to visualize how different metrics impact each other, broader company goals, and your North Star metric. 

An A/B test will provide results faster than a multivariate test since only two options are being compared. Depending on traffic levels and the number of variables, it could take months to complete a multivariate test. 

Analytics and testing technology have evolved beyond the either/or choice of siloed tools or solution suites to integrated platforms. When you use separate tools to conduct experiments and analyze their impact, understanding the short-term results of A/B and multivariate tests might be simple, but it can be harder to see what the effects are in the long term.

Integrated analytics and experiment platforms like Mixpanel connect behavioral analytics with experiments, so you can reuse the same trusted metrics for different tests and iterate more quickly. Teams using digital continuous innovation frameworks like the OADA Loop use integrated platforms to help them run tests and make decisions more efficiently. 

Conducting A/B tests and multivariate tests requires similar tools and approaches. For both types of tests, make sure to set up proper event tracking before you launch your experiment. 

Phased or gradual rollouts allow you to introduce a test to your users in stages instead of all at once. This helps you spot issues (if something is broken or if a test is performing much worse than expected, for example) without impacting your entire user base.

With an A/B split test, you’ll serve up two versions of a feature, interface, or web page (or email, ad, etc.) and split the traffic between them 50/50 to determine which performs better. 

For example:

Given the outcomes of all three A/B split tests, it’s determined that the landing page should be the Page A version. 

A/B/n testing is an alternative way to conduct an A/B test. Instead of running multiple tests with just two pages, you simply compare numerous versions of the page at the same time and split the traffic evenly. 

For example:

If you’re using an integrated analytics and experimentation solution, you can use cohorts to split your user groups and see which ones perform best. For example, you might find that paid users respond better to Page A, whereas freemium users prefer Page B. You can use this information to create personalized experiences and target your users more effectively. 

Before running a multivariate test, you’ll need to identify your key performance metrics (KPIs) and the page elements that are more likely to affect them. You then need to decide what variations of the elements you want to test. A multivariate test involves many more versions than an A/B test because each possible variant combination needs to be compared. 

For Example:

3 Headlines X 2 CTAs X 2 Images = 12 Combinations/Page Versions

Like the A/B split test, traffic should be divided evenly for each of the pages. The scenario above is a good example of why a multivariate test requires much more traffic. Many multivariate tests involve 8-25 combinations that must be tested. 

Both A/B and multivariate testing help you test hypotheses and understand which layout or component performs better against an alternative. In other words, they are experimentation methods that help you choose between different options. 

One of the methods that can help conduct more advanced tests is a gradual rollout. In gradual rollouts, teams release a feature to a small percentage of users and monitor performance. If a release has a negative impact on key metrics or if users report a problem, PMs can rollback the feature and assess the results without impacting the broader user base. Gradual rollouts maintain momentum while also mitigating risk, so teams can move quickly without becoming reckless. 

Here’s an example of what that looks like:

To run experiments like A/B testing and perform gradual rollouts, you need solutions that make it easier to do so without having to spend hours writing and rewriting code. Feature flags are an elegant, simple solution that allows teams to turn features on or off for different subsections of users during runtime, without deploying any new code. Think of them a bit like an on/off light switch for product code.

Feature flags make it easier to perform both experiments and gradual rollouts with minimal developer intervention, which gives non-technical teams more control over processes they are responsible for. 

Modern platforms will often combine feature flagging and analytics, so that teams can manage this entire workflow in a single place rather than switching between tools. 

A/B tests tend to be the default starting point for testing. They are simpler, require less traffic, and are usually less time-intensive. 

Use an A/B test when: 

Multivariate tests aren’t used as much as A/B tests, but they provide insight that can’t be gained through split testing. 

Use a multivariate test when:

A multivariate test is particularly useful when an element is used across various pages, such as a universal CTA, navigation, or footer. Testing on one page will apply to all the other pages without the need for additional tests.

In some cases, the best option is to use both A/B tests and multivariate tests. The A/B test can be used first to determine which option converts the best. Once the landing page or feature update is getting a good amount of traffic, multivariate tests can then be used to fine-tune it and gain insights for future development. 

Using a combination of the two tests can help you get the best conversion rate possible. 

Today, product and growth teams use a complete integrated tech stack to run tests, measure impact, and understand why things happen the way they do. An integrated experimentation tech stack will include: 

Using an integrated solution like Mixpanel, rather than several siloed or clunkily suited tools, helps avoid problems like data drift (which happens with separate systems) and frictions in workflow that slow down learning. It also saves time, since you don’t have to rebuild metrics across platforms.
Explore Mixpanel’s experimentation capabilities to learn more.