What Are The Pros And Cons Of Split-Testing Compared To Multivariate Testing?

Split-testing, also know as A/B testing, is the best-known type of optimization experiment, pairing different versions of your website against one another to see which performs better according to specific metrics.
For example, in version A, you might design your webpage so that your call-to-action is above the fold. And in version B, you might create a new version that places your call-to-action below the fold instead. Then, after conducting your split-test, you can compare to see which placement got more visitors to take action.
Split-testing helps you evaluate your site’s design and all its components by revealing which version of your site is likely to yield the best results. Split-testing is an important part of effective conversion rate optimization. And most businesses benefit from its practice.
If, however, you plan to perform split-tests or hire an outside agency to assist, you should know that split-testing does have limitations. And depending on your testing goals and the type of redesign you want to do, you may want to perform a different type of experiment, such as a multivariate test, instead.
So what are the benefits and what are the limitations of split-testing as compared to multivariate testing? 

Here Are The Advantages Of Split-Testing

Split-tests are the simplest way to evaluate your webpages’ design. Since they work with a limited number of tracked variables or versions of your webpage, split-tests are quick to deliver reliable data. Plus, their results are easier to interpret.
advantages of split-testing

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Split-tests also do not need large amounts of traffic the way that multivariate tests do.
With split-tests, you need to split your traffic into no more than 2-4 different segments. But with multivariate tests, you usually need to split your traffic into a greater number of segments. And you don’t want to spread your traffic too thin because that yields unreliable results.
This makes split-testing the more workable option for more business, especially those just starting out or those that don’t generate much traffic yet.

These Are The Biggest Limitations Of Split-Testing

One of the biggest benefits of split-testing (its simplicity) is also one of its biggest limitations. As you can guess from the split-testing’s other name, A/B testing, split-testing measures the effectiveness of 2-4 different versions of your webpage. (When you test three versions, it’s an A/B/C test and when you test four, it’s an A/B/C/D test.)
But if you want to test multiple variables and more subtle changes, then a multivariate test may be the better choice.
For instance, say you want to test two different headlines and two different images on your webpage. A multivariate test lets you test all the possible page variations at the same time.
multivariate testing

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You might want to complicate the scenario even further by testing two different placements for each image on the page. Multivariate testing lets you test more complex variations of your webpage, like this example, more quickly and easily than split-testing.
Moreover, the other notable limitation of split-testing is that assesses the performance of each version of your webpage only as a whole. It doesn’t evaluate how individual page elements interact with one another to gauge which are most responsible for the test’s results. Multivariate testing does do this, on the other hand. (You can learn more about multivariate testing here.)

The Bottom Line…

Each experiment type has its own advantages and limitations. And when it comes to split-testing, you should choose to split-test under the following circumstances:

  1. You don’t have a lot of traffic.
  2. The design ideas you want to test are not subtle; they are significant redesigns.
  3. There are only a small number of variables in question that you want to test.

For more complex testing scenarios, you may want to consider multivariate testing.
What do you think about split-testing or A/B testing? Have you tried multivariate testing before? Do you prefer one experiment type to the other? Use our contact page to share your thoughts!