Multivariate Testing vs. A/B Testing: A Digital Marketer’s Guide


September 14, 2023

Create marketing experiments that’ll help you hit your brand’s goals when you learn the differences between multivariate testing vs. A/B testing this year.

Your success online hinges on your capability to innovate your digital marketing. This can be done through marketing experimentation. Whether you’re running experiments to improve your paid advertising, email marketing, or overall user experience (UX), you need to know the basics of experimentation to be able to optimize campaigns and drive digital wins for your business today.

A/B analysis and multivariate testing are some of the most basic, yet most essential methods of marketing experimentation in the biz. In order to use these testing methods in effective ways, you need to understand their definitions, use cases, and differences, so that you can decide which one will work best for the goals you have in mind for your business’ success.

Excited to utilize these two methods to improve your marketing and drive digital wins for your brand? Then keep reading this guide by Propelrr to discover how to use multivariate testing vs. A/B testing in email marketing, paid advertising, UX, and more today.

A/B testing explained

At its core, A/B testing is a type of test that compares two versions of an ad, landing page, website, or email, to see which version performs the best. Also known as split testing, this technique allows you to optimize your digital marketing executions and improve overall performance online.

The pros to this method of experimentation include:

  • Simplicity. The best and most effective A/B tests compare just two variants of a single variable in a marketing campaign. This lends a sense of simplicity that multivariate analyses do not necessarily have.
  • Clarity in results. As long as your research design and methodology are sound, then you’ll get results that’ll clearly identify a winning variant from your marketing experiment.
  • Iterative nature. Given the iterative nature of this type of experimentation, you’ll get to develop and improve upon your executions in a highly focused and gradual manner.
  • Platform integration. Since this is one of the most basic forms of comparative analysis, you’ll find A/B experiment integrations on tons of social media platforms, like on Facebook’s ad optimization.

The cons of this method, on the other hand, include:

  • Limited insights. You can only gain so many insights when you’re comparing just two variants against one another, after all.
  • Potential for false positives. Unless you set a false positive rate at the start of your analysis, you run the risk of falsely concluding a statistically significant difference between your variants – when there isn’t one at all in your small-scale test.
  • Can be time-consuming. Since you can only test two variants of a single variable at a time, you’ll need tons more time to analyze every single variable that you want to improve in something complex, like a landing page or a website.
  • Can use up resources. Given the amount of time this form of experimentation takes, it only makes sense that you’ll use up resources throughout this series of variant analyses as well.

Given these pros and cons, you might be interested to know the specific cases where it’s best to use this type of experimentation for your marketing needs. Below you’ll find some scenarios and examples of when to utilize A/B analysis to improve your chosen campaigns.

Here are some use case scenarios for using split testing in your optimization journey:

  • Comparing Google AdWord elements. You can optimize your ad’s copy by switching between two different Google AdWords and seeing which one gets clicks more effectively.
  • Testing out colors of a hyperlink. What has a better clickthrough rate, a green hyperlink or a blue hyperlink? You can compare these two colors to see which one works best on a page.
  • Limited changes for a paid ad visual. You’d probably want your pay-per-click (PPC) ad to garner conversions – so make sure to optimize its visuals by comparing hero images and seeing which one is more effective than the other.
  • Basic variations on a CTA button. Whether this refers to the color, placement, copy, or shape of your website’s call-to-action (CTA) button, you can test basic variations of it to see which version garners a higher clickthrough or conversion rate.

Here are two successful case studies that showcase the appropriate use of this testing method:

  • Århus Teater. This theater company in Denmark simply revised their website’s CTA button from “Køb Billet” (“Buy Ticket”) to “Køb Billetter” (“Buy Tickets”). They ended up with a 20% increase in ticket sales due to clearer instructions from the second version of the CTA.
  • SWISSGEAR. In a variation of their on-sale products’ information pages, SWISSGEAR used red to highlight only their “special price” and “add to cart” sections. This made it easier for customers to see what was on sale, leading to a 52% increase in conversions for the brand.

Think you have a handle on A/B testing when it comes to your digital marketing campaigns? Then it’s time for you to learn more about multivariate testing, to see if it’s the right fit for your experimentation today.

Multivariate testing unveiled

Multivariate testing (MVT) is a method that lets you analyze multiple variants of an ad, landing page, website, UX, or other marketing execution, to see what combination of variables works best for said execution. Since you can test more versions simultaneously with this type, you get results that are more complex than what you’d get from a traditional A/B analysis.

Given that definition, the subsequent pros of this method of experimentation include:

  • Efficient optimization. With MVT, you can optimize your ad, website, UX, or landing page more efficiently since you can test more elements in a shorter amount of time.
  • Comprehensive insights. Since you collect more data points from this experiment type, you can get more comprehensive insights that allow you to extrapolate results as well.
  • Eliminates the need for multiple A/B tests. MVT is essentially a bunch of A/B tests layered on top of one another, so by running this experimentation method you can eliminate the need to run several sequential A/B tests in a row.
  • Statistically significant results. This test type requires a considerable amount of website traffic to run properly; this means you can ensure statistically significant results with this larger audience pool.

The cons of MVT, on the other hand, include:

  • Complex methodology. Given how this type tests multiple variables from multiple variants, you can expect a more complex methodology that requires a deep analysis into the interactions of said variables with one another.
  • Less iterative in nature. This method can test everything you need for an ad in one go, but if you’re looking to pursue a more iterative approach to your conversion optimization, then this method isn’t the one for you.
  • Requires more website traffic to actually run. Since you need enough user data to test all combinations of your variables, MVT necessitates a significant amount of website traffic to run properly. If you’re a new or small business, you might not even have this site traffic yet – which means that you can’t actually run a successful experiment for your brand.
  • Requires more expertise than split tests. Since this type compares more variables and their interactions with one another, this type is best suited for advanced digital marketers with more experience in experimentation.

There are some unique use case implications for MVT, in light of the pros and cons listed above. Discover the situations and scenarios in which you can use this form of analysis by checking out the lists below.

Here are examples of scenarios where you can use MVT to optimize your marketing execution:

  • Multiple changes on a sign-up form. With this method, you can experiment with the placement, length, and languaging of a sign-up form to see which version garners the most number of successful sign-ups.
  • Intricate variations on a paid ad. You can also switch out the headline, copy, and visual of an ad to discover which version gets the most conversions. This is crucial, especially for paid ads that use up critical resources.
  • Revising the layout of a whole landing page. What text placement best guides your users through your landing page? By optimizing with MVT, you can move headlines and body text around to see what arrangement best guides users to the page’s CTA.
  • Complex variations on a CTA ad. Whereas before you could only test one element at a time, here you can change up the color, placement, copy, and/or the shape of your website’s CTA button to see which version garners a higher clickthrough rate.

Meanwhile, here are examples of case studies where MVT was utilized and executed correctly:

  • Ashley Furniture. By removing an entirely irrelevant section from their checkout page, Ashley Furniture was able to improve their UX, reduce their bounce rate by 4%, and increase sales conversions by 15%.
  • Discovery. By optimizing both video engagement and ad viewability on their content pages, Discovery was able to drive a 6% increase in click-throughs for the videos they offer on their online network of shows.

Given all these excellent definitions and examples of the A/B and MVT methods, you’re now better equipped to understand which method might work best for your digital marketing needs today. Let’s keep bolstering your knowledge of these two types by comparing them against one another in the next section.

Key differences between A/B testing vs. multivariate testing

Each test is useful in its own way, and one may not be a great substitute for the other due to some key differences. As you compare each test type for your optimization goals, remember to factor in the following unique differences between the two methods too:

A/B Testing: Multivariate Testing:
Methodology and research design Compares two variations on a single variable for an ad, landing page, UX, or other marketing execution Compares multiple variables in multiple variations for an ad, landing page, website, UX, or other marketing execution
Statistical significance and data interpretation Smaller audience pool may imply higher risk of false positives, leading to the necessity of more A/B tests to collect more data Necessity for a larger audience pool results in more data points collected, implying lower risk of false positives
Resource and time requirements Longer amount of time for sequential experiments, fewer resources like budget and manpower due to simpler execution Shorter amount of time due to multiple comparisons in one run, more resources like automated tools, website traffic, and analytics needed


The best method of choice will inevitably depend on the optimization needs of your selected marketing campaign. But aside from the test’s suitability for your needs, you should also see what tools you have at your disposal to run these experiments overall.

Follow us into the next section to discover four essential tools and platforms for running an A/B analysis or MVT this year.

Technical considerations to implement multivariate vs. A/B testing

The decision-making process for picking between these two types should also include tools, platforms, and technologies available to you when running your experiment. If you don’t have the tools you need to run a multivariate analysis, for example, then you might need to restrategize and do an A/B comparison instead.

Here are some examples of essential testing tools and platforms for setting up your experiments, tracking their progress, and collecting data for your expert interpretation:

  • AB Tasty. Utilized by massive global companies like Fenty and Lush, A/B Tasty provides you both split analysis and MVT capabilities at competitive prices – even for small to medium-sized businesses.
  • Convert. Trusted by Unicef and Sony, Convert offers a unique 15-day free trial for its customers so that they can test out the platform’s A/B and multivariate capabilities.
  • Evolv AI. The AI-driven solutions at Evolv AI allow companies like yours to efficiently optimize campaigns through its adaptive A/B and MVT experimentation platforms.
  • Optimizely. Used by Pizza Hut, eBay, Yamaha, and Microsoft, Optimizely allows brands to access A/B, MVT, and multi-page capabilities from its wide range of services.

With this short list of heavy-hitting software for marketing experimentation and optimization, you can set up a solid starting point for the improvement of your campaigns and content from this point forward.

Empowering data-driven innovation

No matter which method you choose, the important thing to remember is that you should always experiment with your content. Testing out your campaigns is key in hitting business goals; without it, you won’t be able to innovate your executions in successful and data-driven ways.

Testing and experimentation empower data-driven innovation in digital marketing. With them, you can address critical pain points, discover data-backed solutions, and drive campaigns that return real results for your brand in the long run.

Key takeaways

Drive innovation with the right types of testing today. Here are a few final reminders to take with you as you embark on your digital marketing journey today:

  • Identify your why. Why are you conducting this experiment in the first place? By establishing your context and reason for this comparison, you’ll be able to determine which method works best for your given goals.
  • Drive your decisions with data. Once you establish your reasons for experimenting, you should use all the data at your disposal to determine whether to use A/B analysis or MVT for your execution.
  • Consult with experimentation experts. Not too confident in your analytical skills for bigger marketing campaigns? Don’t be afraid to avail Propelrr’s services to get additional advice and guidance today.

If you have any other questions, send us a message via our Facebook, X, and LinkedIn accounts. Let’s chat!

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