Every website, app, and landing page contains various parts. Headlines, calls to action, links and buttons, content blocks, images—the list goes on.
Multivariate testing enables you to experiment with different combinations of these elements to determine which are most likely to influence user behavior. It's a powerful tool that makes refining campaigns and driving higher conversions easier and faster.
Although multivariate testing can be incredibly valuable, it’s not the right fit for every scenario. Sometimes, a simple A/B test is the best choice. However, if you want to test several variants, multivariate tests are a great way to pinpoint which winning combinations drive click-through rates (CTR), conversions, and revenue.
- Multivariate tests help product teams strategically test how different combinations of variables impact conversion rates.
- A/B testing is a good choice if you have low traffic or only want to test two variations of a single element.
- The more variables you want to test, the more complex your multivariate test becomes. Consider this when designing tests, and try testing fewer variables at the same time.
What is multivariate testing?
Multivariate testing is the process of strategically modifying multiple digital variables to determine which have the most significant impact on outcomes, including conversions, user engagement, and website performance. You can run a multivariate analysis on your website, app, or any other digital asset you want to optimize.
Multivariate tests treat different elements as distinct variables that you can test and tweak. You can analyze combinations of headlines, calls to action (CTAs), buttons, text blocks, design choices, and more on a website or landing page. At the end of the experiment, you’ll have objective data that shows which potential combinations and variations are most effective.
Multivariate testing use cases
With multivariate testing, you can:
- Optimize form fills by testing field placements, label wording, and button colors.
- Refine your product pages by comparing image sizes, product descriptions, and pricing displays.
- Improve sign-up rates on your landing pages by modifying and combining variables like headline copy, trust badges, and button text.
- Determine which colors, CTAs, and pricing options will most likely influence website visitors to click your “Buy Now” button.
Note that each example use case includes several distinct variables and ways to combine them.
How is multivariate testing different from A/B testing?
Multivariate tests examine the impact of changing multiple variables simultaneously, while A/B tests compare version “A” of something against version “B” of the same thing.
Let’s say you want to determine whether “Get Started” or “Try Now” is a more effective CTA for your product landing page. You could run an A/B test to directly compare the two options and determine the winner.
However, a multivariate test would be a better choice if you wanted to test various CTA button colors, text, and placements. Doing so would enable you to discover distinct combinations of elements most likely to maximize user engagement and conversions.
An example of an A/B Test Dashboard in Amplitude Experiment showing a summary of variant performance. Try this chart yourself in our self-service demo.
The biggest challenge when executing multivariate tests is the substantial traffic required for meaningful results. Because these experiments involve multiple variables and their combinations, each variation receives a small percentage of overall traffic.
If your traffic volume is too low to justify a multivariate test, consider opting for an A/B test instead.
How does multivariate testing help product teams?
Although single-variable tests are helpful, multivariate tests provide more comprehensive and holistic insights into how elements interact with each other and influence user behavior. Run enough multivariate tests, and you’ll likely discover synergistic effects between different variables that weren’t initially obvious.
Multivariate testing is also more efficient. Experimenting with various combinations in a single test saves you time and resources compared to running multiple individual tests. Similarly, you can run tests to validate hypotheses before investing in them at scale. Sharing the results of your multivariate tests across teams can help guide product marketing and design choices.
What variables should you test?
The goal is to run tests that generate meaningful results, so be strategic about the variables you test. First, identify a problem, like low conversions on a pricing page. Then, think about which page elements might have the most significant and direct impact on user behavior and conversions.
For example, if you want to optimize your pricing strategy, you can test combinations of the following variables on your pricing page:
- Pricing grid displays
- Pricing tiers and options
- Subscription plan structures
- Discount and promotion visibility
- Free trial duration
- Money-back guarantee terms
- Feature and benefit content
- CTA text
- CTA design
- CTA placement
- Inclusion of customer testimonials
- Callout boxes
These are far from the only variables you can test.
If you want to improve your user onboarding experience to help prevent customer churn, potential variables to experiment with include:
- Onboarding flow and steps
- User profile setup process
- Sequence and timing of welcome emails
- Feature tour and introduction
- In-app tooltips and guidance
- Personalization of the user journey
However, just because you can test many variables doesn’t mean you should. Every additional variable increases the complexity of your experiment. When building multivariate tests, consider the possible variations and remember that traffic is split among each option.
The basic formula for the total number of variations in a multivariate test is:
Total Variations = (# of Variations for Element A) x (# of Variations for Element B) x (# of Variations for Element C)
So, if you're testing three headlines, two images, and two buttons, the total number of potential variations you can test would be calculated as follows:
Total Variations = 3 (headlines) × 2 (images) × 2 (buttons) = 12
What are some best practices for running multivariate tests?
The following best practices will help you ensure the best results when conducting multivariate analyses:
Start with a plan. Establish a hypothesis to test and confirm that testing your theories will positively impact important metrics.
For example, if you want to increase free-trial-to-paid-conversion rates, consider testing variations in pricing page design, product feature access, and free trial length.
Only test some of the possible variations. Since traffic is split among each variation, we recommend only testing variables you suspect will have a measurable impact on your goals.
This is an especially helpful consideration when building multivariate tests that compare how design elements influence user behavior. It’s tempting to test an array of colors, fonts, and other design elements, but comparing too many variations will dilute the value of your results.
Remove low-performing variations. Sometimes it's quickly obvious that certain variations won’t help you meet your goals. If a variation isn’t performing, there’s no harm in removing it before the test is complete so that traffic can flow to more promising variations.
Don’t skip data analysis. At the end of your multivariate experiment, assess the performance of each variation. Check on things throughout the campaign, too—that way, you can remove those non-winning variations as needed.
The right analytics tool can make data analysis easier and more scalable. With a platform like Amplitude, you can analyze all your product data in a single system, providing unparalleled insights into user behavior.
Use Amplitude to conduct meaningful multivariate tests
Experimentation drives product growth, and multivariate testing helps you test the performance of different variables more efficiently and at a greater scale than single-variable experiments.
Amplitude helps you collect, organize, and analyze product-related data, including insights from various experiments like multivariate and A/B tests. With Amplitude Experiment, you can easily:
- Test multiple hypotheses in a single experiment.
- Segment users into cohorts and determine which are most likely to convert.
- Learn what drives user behavior to increase conversions and drive revenue growth.
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