Actionable tips, community conversations, and marketing inspiration.

Getting Started With Multivariate Testing in Marketing

Wilson Lau

Sr. SEO Marketing Manager @ AdRoll

One of the biggest benefits of digital marketing is the ability to test almost every aspect of your marketing mix to see what works best. From email subject lines and headlines to landing page images and CTA colors, the ability to test and compare results gives you an edge over traditional media.

In many cases, you might rely on A/B testing to select and test one campaign element at a time. But in others, you may want to gauge the effectiveness of changing several different elements at once. You might also want to uncover the relationships between different elements. In these situations, multivariate testing is just what you need.

What is Multivariate Testing? 

Multivariate testing (MVT) is a testing method that involves testing multiple elements or combinations of variables at a time. Compared with A/B testing, where you test one element at a time, you can use MVT processes to discover which combination of variables performs the best in terms of achieving your desired outcome. It gives you valuable insights into how different elements can impact user behavior.

Multivariate testing is a resource-intensive process that requires a large sample size. If you’re interested in using it, the following guide can help you determine if it’s a good fit.

Multivariate Testing Examples

What does multivariate testing look like in real life? 

Consider the following examples for an ecommerce retailer:

  • You want to switch up the color of a CTA button as well as the language to see which drives more clicks. You decide to use MVT to determine which one works best. In this situation, you’d create four different versions of the page and test each one.

  • You want to know if changing the headline and adding more images to your customer welcome email drives clicks. You create several versions with different headlines and images and let the campaign run its course.

  • You need to bump conversions on your landing page, so you decide to test different combinations of headlines, images, text content, and form fields to see which combination leads to the highest conversion rate.

  • You see a higher bounce rate and fewer clicks to product pages from your website’s home page, so you test banners, navigation menus, and CTA button placement to optimize the user experience and move users further into the sales funnel.

Remember, the goal of multivariate testing isn’t just to find the best-working elements, but to understand how those elements interact with each other.

Multivariate Testing vs. A/B Testing 

What’s the difference between multivariate testing and A/B testing? Both tests involve making a hypothesis about what might happen if you change things on the page. As we’ve established, in the former you’re testing multiple elements or variables, often at the same time. A/B testing focuses exclusively on one variable at a time.

So how do you know which one makes more sense for your needs?

Let’s say you want to increase conversions on a landing page from a paid search ad, but you’re not sure which approach you want to take. While A/B testing is a simpler and faster method that’s ideal for changing a single change, multivariate testing offers a more comprehensive approach. Depending on how much time and resources you have, one might work better than the other.

Ultimately, your choice of testing model comes down to your time, traffic, and the complexity of what you want to test.

When to Use Multivariate Testing

Multivariate testing is an excellent tool for website optimization, but it’s not always the best choice. If you’re faced with the following conditions or needs, it might be a good fit for you:

  • You have a substantial amount of traffic—likely thousands of visitors. This number increases based on the number of variations.The required sample size should be less than your level of current traffic.

  • You need to optimize key conversion points on your website, including landing pages or checkout processes.

  • You want to understand how different elements on a page interact and impact user behavior.

  • You need to refine established designs for performance optimization.

With so many good use cases for multivariate testing, when does it not make sense?

  • You have low traffic.

  • You are in the early stages of your design.

  • You want to test single variables.

In these situations, A/B testing might be the better choice. Before you commit to a test process, consider your specific circumstances and goals.

Types of Multivariate Testing

When you commit to multivariate testing, you also need to determine which method best suits your approach. The number of factors that you need to test can influence how long your testing process takes and how much it costs. 

Multivariate testing can usually take one of two forms: full factorial testing or partial testing. Depending on your resources and desired outcomes, one may be better suited than the other.

Full factorial testing

Full factorial testing includes testing all possible combinations of content with equal probability. In a full factorial test, you’d test every single combination. Let’s say you have a landing page with two images and four CTAs. That means you’d test eight different versions:

  • Version 1: Image 1, CTA 1

  • Version 2: Image 1, CTA 2

  • Version 3: Image 1, CTA 3

  • Version 4: Image 1, CTA 4

  • Version 5: Image 2, CTA 1

  • Version 6: Image 2, CTA 2

  • Version 7: Image 2, CTA 3

  • Version 8: Image 2, CTA 4

Full factorial testing does offer comprehensive data, but it’s got some downsides. For one, it’s resource intensive. As you can see, you need a high number of experimental runs for every variable you change, and that number only goes up if you add factors. Analyzing those results is also fairly complex. Because of the large sample size requirements, achieving statistical significance can be difficult, too. That impacts scalability.

In many cases, you might opt to perform a partial or fractional factorial test, which can reduce the resources you need but still provide valuable insights.

Partial testing

Partial (or fractional) testing is a simpler process that tests smaller subsets of available options. Let’s say you decide to test even more complex variable combinations for your landing page and double the number of variables to 16. In a full factorial test, you’d split the traffic equally between all variations.

In a fractional factorial test, you would divide that traffic between eight variations. The conversion rate of the remaining variations comes from statistical deduction based on the ones you’ve already tested.

Why would you implement this testing?  You usually require less traffic with these tests but you don’t get the granular data. What it does give you, however, is a general sense of whether variations are better or worse than others.

Why would you select partial testing, especially if it doesn’t offer the comprehensive data that a full factorial test does?

Partial multivariate tests allow you to focus on the most promising or relevant combinations of variables. This is important when you have limited resources, like time, traffic, or computational power. It also cuts down on the complexity of the tests, especially as variables increase. You can even reduce the number of comparisons you’re making, which leads to a smaller risk of false positives (results that don’t actually provide benefits).

Ultimately, partial testing can work for you if you want to make your tests as efficient as possible. Just know there’s a trade-off: because you’re not testing every possible combination, you might risk missing potentially significant factor interactions.

How to Conduct a Multivariate Test

Your multivariate test will differ depending on the number of variables you include and the type of test you do, but the basic steps are as follows:

  1. Identify your goals: Define what you want to achieve with the test. Example goals include increasing conversions, reducing bounce rate, improved user engagement, etc.

  2. Select your variables: When you know your goals, you can identify the elements of the site to test. These include headlines, images, colors, buttons, CTAs, and more.

  3. Design the variations: For each variable, create a different version. If you’re testing headlines, create different versions to see which one performs best.

  4. Set up your test: Find the right multivariate tool to set up your test. This tool will randomly serve the different variations to your users and track the results.

  5. Run the test: Allow your test to run for a sufficient amount of time to collect the data. Your test’s duration depends on website traffic and the number of variations you need to test.

  6. Analyze the results: Once your test is complete, analyze the results. Compare how each variation performed in relation to your goal.

  7. Implement changes: Based on your analysis, implement the most successful variation(s) on your site or app.

  8. Repeat the tests as necessary: Multivariate testing isn’t a “one and done” operation. Once you’ve completed one test, identify new variables and begin the process again.

Remember: multivariate testing isn’t just about identifying the best performing variation. It’s also important to understand how different elements interact and influence user behavior.

How to Find Variables to Test

Knowing which elements to test in a multivariate test is a skill. It’s also vital to ensuring the test is successful. Variables in a multivariate test can include things like headlines, images, videos, CTA buttons, product descriptions, layout, colors, and more. The keys to a successful multivariate test include choosing variables that significantly impact user behavior and your ultimate goal(s). 

To help uncover which variables you should include in your testing, consider taking the following steps:

Understand your goals

Before you decide on the variables, ask what your goals are. Do you want to increase conversions? Is the plan to improve user engagement or reduce bounce rates? These goals guide the variables you will focus on.

Analyze your existing data

Use your current analytics tools to analyze your existing data and identify trends or yellow or red flags. If certain pages have high bounce rates, you may want to test variables on them to reduce that metric.

Conduct user testing and seek feedback

User testing is a powerful tool to understand how real people use your website or product. Conducting surveys and requesting feedback can help you identify potential pain points for your customers.

Check out the competition

Look at what your competitors are doing. What elements on their site are helping them succeed? These might be variables you can test on your own landing pages or website.

Use heatmaps to your advantage

Heatmaps can help you see where your users are clicking, scrolling, and spending time on your site. Are users lingering in specific sections? How are they reading or engaging with your content? Heat maps can show you areas of interest and uncover elements you can test in the future.

Advantages and Disadvantages of Multivariate Testing

Like all testing processes, multivariate testing isn’t without its unique advantages and disadvantages. While it’s definitely a powerful tool to optimize website performance, it may not be the best fit for everyone. Consider the following pros and cons before you jump into testing.


  • MVT is an efficient way to test interactions between page elements.

  • It requires fewer successive tests because you’re testing multiple variables at once.

  • It allows you to create a highly optimized experience for your users.

The other major benefit of multivariate testing is that it enables you to create a highly optimized user experience to maximize conversions. Most of the elements or variables you test like page speed, visuals, and even CTA types can impact the user’s experience. When you optimize these elements for conversion, you often have the added benefit of improving how people interact with your site. 


  • MVT is a complex process that involves several combinations of variables.

  • It can be time-consuming, especially compared with simple A/B tests.

  • It requires large volumes of site traffic, making statistical significance harder to achieve for smaller sites

  • There is a risk of insignificant changes that don’t make a difference in user behavior.

Best Multivariate Testing Tools

There’s no shortage of powerful A/B testing and multivariate testing tools on the market. Pricing can vary by platform and needs. Some of the most popular testing platforms include:

  • Optimizely: Some of the biggest names in digital marketing, sales, and ecommerce rely on Optimizely’s Experiment platform to run experiments to get insights on their customers’ behavior. With the Accelerate and Scale options, you can perform MVT on several pages at once. Like many other providers, you’ll need to request a price quote.

  • AB Tasty: AB Tasty doesn’t offer upfront pricing, instead relying on custom quotes based on the information you provide. It does, however, offer plenty of powerful tools for testing (including code and WYSIWYG editing capabilities).  They list several reputable brands on their client roster, which could give you the social proof you need to check them out.

  • VWO: VWO offers a powerful suite of testing tools, especially in their free version. What you’ll pay for the plan per month varies based on your needs and the traffic you need to track each month. But for marketers with less than 50,000 monthly visitors, their growth plan costs $822 per month and offers comprehensive multivariate testing tools.

  • Convert: Convert is another MVT tool that says you can “set up every kind of experiment possible.” It offers A/B, split, and multivariate testing. Pricing for Convert varies based on the number of users you need to test per month, although their Expert plan is $13,432 per year for 12 million users. You may need longer than 15 days to run a test, but they do offer a trial period.

The Importance of Multivariate Testing in Cross-Channel Campaigns

Multivariate testing can be a complex process, but don’t underestimate how important it is to your cross-channel strategy. Understanding the relationships between elements on a web page or within an email campaign goes a long way toward improving your customers’ experience as they move through the journey. Being able to analyze and speak to the data behind what works best for your digital marketing efforts is essential.

You’ll need powerful tools to help put that data in one convenient place, which is where our digital advertising platform comes in. We can help you analyze the information from your marketing campaigns and determine which paid efforts are more successful. When you use the right digital marketing performance dashboard, you’ll reduce the time spent looking at data and free up the energy to test your campaigns.


What is the difference between multivariate testing and A/B testing?

A/B testing typically only tests one variable at a time and shows you which was the more effective one at achieving your goal. Multivariate testing tests many combinations of variables to see how they interact.

What are the benefits of multivariate testing?

Multivariate testing allows you to see how several variations interact with one another on a landing page, website, or other marketing asset. It helps identify the best combination of elements that work together to achieve your goals, like an increased conversion rate. 

What is the downside of multivariate testing?

Multivariate testing is a time-intensive process due to the number of variables you need to include. It can also be expensive. Because multivariate testing involves so many variables, it typically requires a higher volume of traffic to yield statistically significant results.  

What is an example of a multivariate test?

You’re an ecommerce retailer who wants to test how different elements on your landing page interact with one another and drive conversions. In a multivariate test, you create combinations of these elements (headlines, color, CTA button copy, page content, and images) and randomly assign them to different audiences. At the conclusion of the test, you analyze the combination that saw the best results for your specific goal, in this case the conversion rate.

When should I use multivariate testing?

Use multivariate testing when you have a high level of traffic, need to optimize key conversion points, and want to understand the interactions between different elements on your page(s).

Explore Next