Testing plays a big role in the digital marketing world and can be a huge asset to improving business performance. Think of it as a health-check tool for your business. Testing your website, app, or email marketing platform will open doors to improving things like conversion optimization. How? By helping businesses to understand what attracts or discourages their audience from achieving their marketing goals.
Multi-variate testing is a technique used by marketers to achieve this. A multi-variate test is when you experiment with a variety of combinations of different elements (CTAs, text, visuals) and then analyze which variations or combinations worked better. It’s an effective way to identify any gaps that are preventing your customers from acting on a call on your CTAs, completing a form, reading the content on your website, or completing a transaction.
Let’s dive deeper and look at how others across the web have defined this technique.
Optimizely describes multi-variate testing as “a technique for testing a hypothesis in which multiple variables are modified. The goal of multivariate testing is to determine which combination of variations performs the best out of all of the possible combinations.”
Websites and mobile apps are made of combinations of changeable elements; a multi-variate test is formed by changing multiple elements at the same time in order to assess the performance of those combinations. For example, by changing both the headline and the image at same time, two variations of the image and three variations of the headline, this would create six versions of the content giving you multiple combinations of variables to assess the performance. These are subsequently tested in parallel to identify the best performing variation.
The total number of variations in a multi-variate test is:
[# of Variations on Element A] x [# of Variations on Element B] = [Total # of Variations]
Common examples of multivariate tests include:
- Testing text and visual elements on a webpage together
- Testing the text and color of a CTA button together
- Testing the number of form fields and CTA text together
The Main Difference Between Multi-Variate Testing & A/B Testing
Running an A/B test involves creating two different versions of a web page and dividing traffic equally between each page. You can get more granular and perform an A/B/C test that tests three different web page versions, an A/B/C/D test that tests four different web page versions.
On the other hand, running a multi-variate test is more elusive. Here you do not test a different version of a web page like you do with an A/B test, but instead you test different elements inside one web page. The goal of the multi-variate test is to highlight the elements that work for you the best on a web page. It is far more complex as it tests multiple variables and their interactions with one another, resulting in far richer data to analyze for improving site visitor experience..
As noted by HubSpot, “While an A/B test allows marketers to learn which major formatting of a site or piece of content is most engaging, multivariate allows them to zone in on which tiny details are most engaging by showing audiences variations that only have subtle differences.”
The Benefits of Multi-Variate Tests
- Avoid having to conduct several A/B tests one after the other, saving you time since we can look at a multivariate test as several A/B tests conducted simultaneously on the same page
- Determine the contribution of each variable to the measured gains
- Measure the interaction effects between several supposedly independent elements (for example, page title and visual illustration)
Optimization expert & founder of CXL, Peep Laja, summarizes the benefit of multi-variate testing: “While A/B testing doesn’t tell you anything about the interaction between variables on a single page, MVT does. This can help your redesign efforts by showing you where different page elements will have the most impact.”
Key Considerations When Performing a Multi-Variate Test
- With multivariate tests, you split your traffic into smaller segments to accommodate each variation (as opposed to splitting your traffic in half as you do with A/B split-tests). Consequently, to achieve statistically significant results, you need a larger amount of traffic. If you spread your traffic too thin, your results won’t be reliable. And many new businesses don’t yet have enough traffic to run reliable multivariate tests.
- Multivariate tests also take longer than A/B split-tests to achieve statistical significance since they involve multiple variations. Plus, they typically assess more subtle changes versus a major design overhaul, so the impact on your conversions may not be as dramatic.
Testing Best Practices
Both multi-variate and A/B tests play essential roles with conversion rate optimization. Each test type serves a purpose. Using one test type over another also makes more sense in certain situations. But they can also complement one another. You can implement both test types to dig down even further and make the ultimate best choices for your digital tools.
We at Tegrita believe in the power of multi-variate testing. We are partners with Motiva AI, leading advanced machine learning platform for marketing automation, that offers multi-variate testing under their umbrella of audience engagement tools.
If you wish to learn more on Motiva’s functionality and how it can benefit you, here are additional resources that may help you shape your decision: “Multi-Variate Testing: Harness the Power of Motiva AI” and “Motiva vs Oracle Adaptive Intelligence”.
What are your thoughts on A/B testing? Have you tried multi-variate testing before? Do you prefer one experiment type to the other? Get in touch and let us know what you think!
About the AuthorMore Content by Manar Asaya