A/B Testing vs. Multivariate Testing for Design Optimization

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What is A/B testing?

A/B testing (also known as split testing) is the process of comparing two versions of a web page, email, or other marketing asset and measuring the difference in performance. You do this giving one version to one group and the other version to another group. Then you can see how each variation performs. But what does it mean in terms of design? Well, it is a design optimization method that uses analytics data to determine which version of a design has the most desirable impact on user behaviour. In an A/B test, the variations being tested could be completely different from each other, and not the result of manipulating a small set of variables. For instance, you could have two pages with completely different layouts, different copy, different navigation, different visual design, and so on.  

What is multivariate testing?

In analytical aspects Multivariate testing is a technique for testing a hypothesis in which multiple variables are modified. It is also a similar design optimization method but in a multivariate test, 2 or more design elements (the variables) are tested. Each of these variables could have multiple variants (Each design version of a variable). The goal of multivariate testing is to determine which combination of variations performs the best out of all of the possible combinations.

 For example, in the below page we could test 2 variables: visual representation for a product (with 2 variants: an image or a video) and label for the main call-to-action (with 2 variants: Buy Now or Add to Cart).

These two methods have a lot in common.

They both involve splitting up live visitor traffic between different design variations in order to test their impact. They both measure which design option has the best impact on conversions. However, the key difference between these two methods comes down to what you can use them for.

 For example, with A/B testing, you might compare two different landing pages. Each landing page might have different images, content, and call-to-action text. Let’s say you split your traffic between these two versions, and you find that version B of this landing page results in more conversions than version A. That tells you that version B works better than version A, but you may not really know why. Was it the image in version B that had the most impact on users? The content? The call-to-action text? Or some combination of those variables?

 Here’s where multivariate testing can have an advantage. In multivariate testing, you can determine how various elements on a page interact with one another. You test every possible combination of the different UI elements that you are considering changing. So, let’s say you’re considering changing the image and the call-to-action text on a landing page. If you’re running a multivariate test, you’ll test every possible combination of those different variables. That way we can determine what change or combination of changes will have the greatest impact.

Learn about these testing techniques here.

Really useful, right? So why is A/B testing so much more popular than multivariate testing?

 Well, because every new combination of UI element you add is a different variation you have to test. That means rather than splitting traffic 50/50, you might have split up your traffic much more. Unless your site or app is blessed with very high traffic, that likely means you’ll have to run a multivariate test for a longer amount of time in order to reach statistical significance. And for some teams, that makes multivariate testing unusable.

If you have the option, consider reserving multivariate testing for use only when you’re trying to refine and perfect the details in an already-functional design.

 In the world of design-optimization methods, A/B testing gets all the attention. Multivariate testing is its less-understood alternative, often deemed too time-consuming to be worth the wait. While this method has its limitations, they are counterbalanced by its benefits, which cannot be easily achieved using A/B testing alone.

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