In the age of big data and sophisticated analytics, marketers have the opportunity to test various designs, content, call to actions (CTAs), and messaging options to determine what works best for their audience. But, which testing method you use depends on several factors. We’ve outlined the differences between multivariate and A/B testing below, along with some of their common uses, advantages, and limitations, so you can decide which approach works best for your brand and target consumer.

A/B Testing

A/B testing, also referred to as split testing, enables companies to better understand their audience and how to optimize their experience. Marketers use this method to compare two different design directions against each other, like two versions of your home page, email, advertisement, or another type of marketing collateral. You can also conduct A/B/C or A/B/C/D tests that run three or four different variations if desired.

For example, most people are familiar with A/B testing for email campaigns, which comes standard with many email marketing tools. Most marketers have used this to compare two different subject lines and determine which one leads to higher open rates. You create the two versions, and then traffic or views are divided evenly between the options to offer a direct side by side comparison. Companies often use this method to test small changes on landing pages, like the color of a CTA, or which mascot design should point to a sign-up button. 

Advantages of A/B Testing

  • Can be run on smaller sites without a ton of traffic
  • A powerful testing method that’s simple in concept and design
  • It’s a best-practice to test radically different design ideas, which can lead to more creativity and bolder ideas
  • User-friendly option for gaining reliable data quickly
  • Keeps analyses simple by sticking to a small number of tracked variables
  • Get meaningful results quickly, and in a way that’s easy to interpret
  • Enable a continuous flow of A/B tests on their sites — many larger companies use the A/B testing approach as a primary testing method
  • Helps introduce the idea of optimization through testing to skeptical teams because it quickly demonstrates the quantifiable impact of design changes

Limitations of A/B Testing

  • Performs best when comparing just two to four variables
  • Doesn’t provide information about interactions between variables within a page
  • Not easily scalable since more variables take longer to run

Multivariate Testing

Multivariate testing uses the same core mechanisms as A/B testing, but it compares more variables and also reveals how they interact with one another. Its design is also more subtle. Rather than comparing radically different webpage designs, for example, multivariate testing focuses on comparing different elements within a single page design.

Just like the A/B option, this approach splits views or interactions between multiple versions of the design. But, instead of having two options to test, the multivariate approach analyzes every possible combination of the variables. The results are then compared to determine which combinations of variables proved to be most successful at accomplishing the goal (e.g., getting the most conversions). 

For example, with multivariate testing, marketers can test two different lengths of signup forms, three headlines, two footers, and several graphics variations at once. The visitors or viewers would be split between all possible combinations of these elements until a decent sample size is achieved, and reliable conclusions can be drawn.

Advantages of Multivariate Testing

  • Test many more variables within a single base design
  • Have the ability to dive into tiny design details that make a difference in engagement
  • Discover how variables interact best with each other
  • Determine which combinations have the greatest positive or negative impact on visitors’ or viewers’ interactions
  • Draw conclusions about effective elements of the design that can be used on future campaigns as well

Limitations of Multivariate Testing

  • Requires substantial traffic to complete the tests
  • Better suited for advanced marketers
  • The more variables that are tested, the longer it takes to acquire meaningful data
  • It may not be worth the time to run extensive multivariate tests if you can accomplish similar results with several quality A/B tests

Which Testing Option is Right for You?

Now, you have the information necessary to make the right decision for your company and audience. Regardless of which one you choose, the value of this data is immeasurable. For a while, marketers have had to make their best guess at what works for their audience. Even when ads performed well, they weren’t sure exactly what aspect led to that result. In this modern era of data, we can test our hypotheses against actual data from our exact audience, and that’s powerful.

For tips on how to craft a compelling brand story, as well as the full A to Z on how to identify, grow, and maintain your audience through content and marketing strategies, check out AdRoll’s Ultimate Guide to Growth

Angie Tran
Author

Angie is the Senior Copywriter at AdRoll. Prior to AdRoll, she was a Content Writer at various digital marketing agencies. A writer by day and a reader by night, Angie’s other hobbies include cooking and learning useless movie trivia.