What you need to know about multivariate testing

Multivariate testing (MVT) is the process of testing several variables to find out which combination is the most successful. Marketing teams use this approach to figure out which website elements work best for achieving conversion goals.

While multivariate testing and A/B testing (also known as split testing) share similarities, MVT may offer insights into complex interactions between multiple elements. This is often preferable for dynamic marketing campaigns.

Understanding how MVT works can help you optimize your on-page marketing efforts.

Multivariate testing is a method used in (CRO) to analyze and improve the performance of websites, landing pages, and marketing campaigns.

In this approach, multiple variations of different elements on a webpage or within a campaign are tested simultaneously to determine the most effective combination for achieving a desired outcome, such as increased conversions, sales, or engagement.

For example, imagine you notice that the call to action (CTA) button on one of your web pages isn’t performing well. It currently has a blue background and the phrase “Buy Now.” You want to test other colors (green and red) and an alternative phrase, “Get It Now.” Accordingly, you have four variations:

1. “Buy Now” + green

2. “Buy Now” + red

3. “Get It Now” + green

4. “Get It Now” + red

If you were to add another variable, the number of options would increase to eight. Here are the variables you would have if you also tested the placement of the CTA button on the page:

1. “Buy Now” + green + top

2. “Buy Now” + green + bottom

3. “Buy Now” + red + top

4. “Buy Now” + red + bottom

5. “Get It Now” + green + top

6. “Get It Now” + green + bottom

7. “Get It Now” + red + top

8. “Get It Now” + red + bottom

You run all options simultaneously and check which achieves the highest . The more variables you add, the more complex and time-consuming MVT becomes.

While multivariate testing and A/B testing are both methods used in CRO to improve the performance of websites, landing pages, and marketing campaigns, they differ in their approach.

Below are some of the key distinctions between the two methods.

• A/B testing: involves testing two versions (A and B) of a single element, such as a headline, button color, or CTA, to determine which performs better

• Multivariate testing: tests multiple variations of different elements simultaneously, allowing you to examine how various combinations of elements impact user behavior and

• A/B testing: generally simpler and easier to set up, as it involves testing only two variations at a time

• Multivariate testing: more complex and time-consuming as it requires careful planning and analysis due to the multiple variations being tested at once

• A/B testing: provides insights into the performance of individual elements tested against each other

• Multivariate testing: provides insights into how different combinations of elements interact and influence user behavior, offering a more comprehensive understanding of website performance

• A/B testing: requires fewer resources in terms of time and traffic, as it tests only two variations

• Multivariate testing: requires more resources, including higher traffic volumes and longer testing periods, to effectively analyze multiple variations for statistical significance

You can perform MVT in several ways. The following two methods are the most common:

Full factorial testing is useful when you need highly accurate results. It involves testing all the possible combinations of the elements you want to see perform.

For the above example, full factorial testing would take each of the eight phrase–color–position combinations and test it separately. You would need to divide the traffic to the target webpage by eight so that each variant receives 12.5% of the traffic.

This method can be time-consuming and resource-intensive. However, it provides the most comprehensive data on how different elements interact with each other.

Fractional (or partial) factorial testing involves testing a subset of all possible combinations based on statistical principles. These statistical principles involve selecting a subset of combinations to capture significant interactions efficiently while minimizing the number of experiments needed.

For example, if you are testing two colors, two phrases, and two placements of the CTA button, a fractional factorial test might only test the four combinations that are most likely to provide meaningful insights. Then, you would use the results to predict the outcomes for the remaining elements.

This approach reduces the amount of time and resources required for testing while still providing valuable data on how different elements interact. This approach can be useful when the number of elements is high, but resources are limited.

Multivariate testing is an integral part of successful CRO. The key benefits of using this method include the following:

Multivariate testing allows you to test several variables simultaneously. Instead of running a separate A/B test for each variable, you can test multiple combinations at once. This can save time and resources.

By itself, an A/B test is much faster than a multivariate test with many variants. However, it would take multiple A/B tests to check the number of variants you can run with just one MVT.

This method can help you see how different variables interact with each other. Through this, you can identify synergies or conflicts that may impact . You can use the test to understand how different variants and variables influence user behavior together, even if they appear completely independent.

MVT offers you concrete data to make informed decisions. When you analyze the test results, you can identify the variations that drive conversions most effectively. With this insight, you can make data-backed changes to your marketing tactics.

Multivariate testing is an iterative process. As you gather insights from tests, you can make informed changes and continue to refine your strategies. The right tools will enable you to continuously efforts.

The approach is highly beneficial for high-traffic websites as it makes it easy to check multiple variations of elements. For example, if you have 20,000 daily visitors and 10% of them convert, the MVT process for three to four variables can take days or weeks.

Multivariate testing comes with a few significant downsides that stop some marketing teams from choosing it over A/B testing.

Unlike A/B testing, which can easily be arranged with readily-available tools, MVT requires careful planning and implementation. Analyzing the results of both full and partial factorial testing requires technical expertise and specialized software.

You’ll need a large sample size to collect statistically significant results through MVT. Gathering sufficient data may take too long if your audience size is small. This significantly reduces the benefits of MVT.

For example, if you have 1,000 visitors and 5% of them convert, the test could take months or even years to generate sufficient results.

Multivariate testing focuses on testing combinations of variables, which can make it challenging to isolate the impact of individual elements. If you want to understand the specific impact of a single variable, running separate A/B tests may yield more effective results.

Multivariate tests can create false positives or false negatives, indicating nonexistent differences between variations or overlooking real differences.

To mitigate this risk, follow these best practices:

• Employ robust statistical methods.

• Adjust significance thresholds for multiple comparisons.

• Ensure adequate sample sizes for statistical power.

• Prioritize impactful changes rather than small changes.

• Validate results through replication or validation tests.

• Avoid prolonged testing durations.

While using MVT to test a large number of variables requires less time than running several A/B tests, the process is still time-intensive. You’ll need to invest in the right tools to ensure the test is accurate and efficient, possibly placing more burden on your budget.

Successful MVT involves a substantial amount of planning and a robust approach to analytics. The goal isn’t just to find out which variant works best but also to minimize the time and money you spend on the process.

Each multivariate test has a specific conversion rate optimization goal. For example, you might want to increase sign-ups, purchases, and downloads or drive customers to take a specific action on your website.

In the above button color–phrase–placement example, the goal is to increase purchases.

You have to determine which variables should undergo testing. You can use several methods to do this, including the following:

• Conducting customer surveys and analyzing feedback to learn what customers may want to change on the website

• Using Google Analytics and other tools to find out at which point visitors leave your website

• Determining which website elements visitors interact with most

You need to choose the variables that are most likely to provide the highest return on investment (ROI).

Create different variations for each variable you want to test. To achieve accurate results, each variation has to be distinct and have a clear hypothesis behind it; for example, you might test the green color instead of the failing blue because it aligns with the product’s eco-friendly nature.

Use a multivariate testing tool to design and implement your test. This could involve creating different versions of your webpage, email template, or CTA button. Be sure to distribute the traffic evenly to each variation. At this point, you can choose which MVT method to take advantage of.

Now, you can launch the test!

Bear in mind that it has to run long enough to give you a chance to collect statistically significant data. The duration usually depends on the amount of traffic and the expected conversion rates. You likely need to run the test for at least a few weeks to capture enough data.

After the test is over, analyze the data collected from each variation. Compare the conversion rates, engagement metrics, and other relevant data to identify which variations performed better for your needs.

Consider the following best practices to maximize the process’s efficiency:

Before running the test, develop a hypothesis about how the different variables will affect your goals. This will help you interpret the test results.

MVT is complex, so you will need high-quality analytics tools, including an instrument that can analyze the results quickly and help you gain insights into your marketing tactics.

Some tools perform both A/B testing and MVT.

While it may be tempting to test many variables simultaneously, complicating the MVT process could interfere with your marketing goals. Try to limit the number of variants to two or three, as this will enable you to get faster results.

Run the test for long enough to ensure that you have a sufficient sample size and that the results are statistically significant. Don’t expect to get clear results in just a few days, especially if you have more than two variables.

Multivariate testing is a strong tactic that can provide valuable insights. However, in many cases, simple A/B testing can get the job done well. If you want meaningful results quickly, consider narrowing the variants down to two choices.

Multivariate testing is a robust instrument designed to help companies achieve their CRO goals. Unlike A/B testing, which deals with just two options, MVT can compare the combinations of several variables, offering a detailed insight into the way different elements of website design affect your marketing efforts.

While highly beneficial, MVT comes with several disadvantages that include the high complexity and the need for large sample sizes. It can be a great choice for high-traffic websites, but websites with a smaller audience may be better off using A/B testing.

Full factorial is the multivariate test type that distributes the website traffic equally among all combinations.

A univariate test examines one variable in each dataset separately, while multivariate tests evaluate the combinations of variables.