As you’re testing your ads, landing pages and web pages, you may have considered whether to A/B or multivariate test your content to learn about your audience and which message better converts. Each testing mode offers different advantages and opportunities, and each testing mode also has different requirements. So, which testing mode is best for you?
What is A/B vs. multivariate testing?
To answer the questions posed above, it’s imperative to know the difference between the two testing forms.
A/B testing, also called split testing, is a testing method whereby the conversion rates of two different version of a sales page, email, etc., are tested against each other using live visitors as the testers. The ‘A’ and ‘B’ of the test could be just about anything: a call-to-action (CTA), button color, headline, or something else altogether. Depending on which A/B converts better in terms of clicks, buys, etc., the marketer selects the winner and uses that conversion winner in her eventual sales campaign.
A/B testing is very simple, which is why it’s popular. In this testing method, only one element is being tested, whether it be a button, a message, or a CTA. Practically speaking, this involves dividing the audience and having a small number of individuals view the proposed element, and then using their yes/no response to the element as the determinant for what the remainder of the audience sees.
One of the main advantages of A/B testing is its ability to work with a small audience. Because the response being tracked is very straightforward, it doesn’t take thousands or even hundreds of viewers to make a decent determination of which element works best. A blogger with 200 daily visitors can easily set up a relevant A/B test.
A/B testing is also easily quantifiable- in other words, it can be used to prove that one element is better than another, especially if there is a skeptical sales team or co-blogger involved who requires some kind of data.
The biggest disadvantage of A/B testing is that it’s time-consuming to set up and test single elements. A typical sales page, for example, might have 20 different button styles and/or colors to choose from, and that’s not even bringing in the possibilities of different messages, fonts, titles, images, etc.
A/B testing’s other major limitation is that it only reveals information about how the audience reacts with individual page elements. There is very little opportunity to learn how the elements affect and interact with each other.
Multivariate testing, also called full factorial testing, tests several elements simultaneously instead of two. Like A/B testing, multivariate testing involves splitting the audience and having different groups view different versions of a sales page, ad, etc., with the ‘winning’ group defined as the one that achieves the highest conversion rate. The version used with the highest converting group is then used with the entire audience.
Multivariate testing is more complicated than A/B testing and can also take a longer time to run. Data collected from such testing is more complex but can also help a marketer brainstorm new ideas, and those ideas that go beyond just which color or shape to use on a CTA.
With multivariate testing, the main advantage is the wealth of information that can be extracted from the testing. Knowing about different element versions, as well as how they play together, helps a marketer determine which set(s) of elements to place together. A set of campaigns can be built from the information derived from even one in-depth multivariate test, and future campaigns can also take advantage of that information.
A large audience is the basic requirement of multivariate testing because many different element versions will require many testing groups. This can be a limitation for affiliate and other marketers who only have an audience of several hundred or even thousand.
Many marketers work around this limitation by running just 10 combination tests, then letting A/B tests provide the remainder of the information. For example, three different sale page versions might be initially tested, and the page having the best conversion rate would then be subjected to different A/B tests for button color, message, etc.
The Bottom Line with A/B vs multivariate testing
In summary, A/B testing provides quick answers to simple testing questions like which message best converts, or which button color, or font. Multivariate testing takes a more global approach to conversion and examines how individual elements work together.
Rather than viewing them as opposites, you can use A/B and multivariate testing as complementary means of learning about your audience and finding out their preferences. Even if you don’t have a large audience, you can still use a few multivariate tests to get going on a landing page, for example. Afterwards, you can fine tune individual elements by using A/B tests.
There are many third party paid platforms for running either testing method; however, there is also a free one available through Google in its Webmasters Tools area.
Google goes a long way towards defining and explaining how you can perform ‘Content Experiments‘ via its analytics platform. It’s certainly worth a look if you already have a website and want to try new campaigns in the oncoming year (or even this one).