What is Multivariate Testing and How to Use It?

Testing and optimization are the building blocks of every successful digital marketing strategy. They’re like the secret sauce that helps you really understand how people are using your website and what makes them tick. 

In this article, we’re going to dig into why you might want to do multivariate testing (which goes beyond A/B testing, allowing you to test more than one variable) and how to accomplish it. 

Keep reading to discover how multivariate testing can help you experiment, learn, and make decisions based on real data.

What is multivariate testing?

Multivariate testing, also known as A/B/n testing, is like a more complex version of regular A/B testing.

It helps you figure out how different parts of your webpage work together to convert your visitors. Instead of just comparing two versions of a webpage, you can mix and match different elements like headlines, copy, and images to discover how they interact.

In multivariate testing, you carefully mix and show all these variations to people who visit your page. Each group of visitors sees a unique mix of elements. This way, you can test ideas that involve many different elements.

For instance, you might test a few different headlines, product descriptions, and buttons. The test will tell you which combination of these turns most of your visitors into happy customers.

Run multivariate tests

What is the difference between multivariate testing and A/B testing?

A/B testing focuses on comparing two distinct versions of a webpage, making it ideal for binary changes to one particular element (like the color of your call-to-action button). 

With multivariate testing, you can test multiple elements simultaneously, which means you can run through all the possible combinations of multiple variables within a single page.

Multivariate tests require a lot of traffic in order to reach statistical significance, since all the possible combinations of elements need to be shown to many different website visitors in order to judge their effectiveness. The amount of traffic you have will affect the trustworthiness of your test results.

Therefore, if you’re trying to optimize a low-traffic page, it might be better to use traditional A/B testing instead of performing a multivariate test. It will be much easier to hit the minimum sample size for statistically significant results when you’re only showing two versions of a web page to your visitors instead of nine combinations (or more).

What are the benefits of multivariate tests?

Multivariate testing is ideal if you want to optimize your landing pages without completely redesigning them. Not only can you track how multiple page elements interact, you can also gain insights into user behavior, preferences, and pain points.

While you can learn about these factors if you conduct informal research, you’ll generate only a fraction of the meaningful results that a multivariate test can deliver. That’s because multivariate testing is so exhaustive that it helps you gather direct feedback on many different versions of a landing page.

When you’ve compared the performance of several (or more) different combinations of elements, you can be confident that the winning version is the best choice.

Multivariate testing: steps to follow

Let’s break down the steps involved in multivariate testing. Although it might seem like multivariate testing is extremely complicated, it’s not a whole lot more difficult than basic A/B testing or usability testing. You just need to follow the steps below!

1. Define your goals

The first step of multivariate testing is defining the goals and objectives of your test. Do you want to boost sales, collect more leads on a landing page, or increase your average order value?

Depending on what you want to accomplish, you should formulate hypotheses about how to better accomplish the goal that you’re optimizing for. If you’re interested in improving your lead generation numbers, think about which aspects of your offer you could change or which visual elements might lead to a higher conversion rate.

Once you’re thinking about the types of variables you want to investigate and the key performance indicators you’re interested in, you can move on to the next step.

2. Select your variables

Selecting the right variables for testing is a critical step in multivariate testing. This involves choosing which elements you’ll modify on a web page or marketing campaign as part of the test. The elements you can examine as part of a multivariate test include things like headlines, images, offers, copy, and call-to-action buttons, among others.

You should carefully consider which variables you’ll test to ensure that you’ll generate meaningful results. If a certain element’s design won’t be likely to influence the outcome you’re interested in learning about, you shouldn’t include it in the multivariate test. But if you’re pretty sure that it will have an impact, you should test it.

3. Launch the test

Next, you’ll need to set up and perform your multivariate test. The exact steps you’ll follow here will depend on your testing tool, but there are always going to be two key tasks:

  • First, you need to create variations for each page element that’s going to be involved in your test. That means you should create between 3 and 5 versions of your headline, find a few different images that might work on your page, and vary the design of any other elements you’re testing. Then, you’ll have enough variants of each element to combine with all the variants of the other elements you’re testing.
  • Next, you need to launch the test and start collecting data. You want to generate as much traffic as possible during your multivariate testing period, since this will help you get statistically significant results. The less traffic is exposed to different variations of your site, the less confident you can be in your test results.

4. Analyze the results for statistical significance

After you’ve run the test, you need to analyze the results. 

This involves comparing the performance of different variations, looking for patterns and trends in user behavior, and assessing the statistical significance of the findings. 

The insights gained during this step are essential for making informed decisions about which variations were the most effective.

5. Apply changes accordingly

Once you’ve identified the best-performing variations, it’s time to roll out the changes on a permanent basis. 

Once you make the changes, however, you should carefully monitor the relevant KPIs to ensure they continue to have a positive impact on your goals.

Examples of multivariate testing

Now that we’ve gone over the theory of multivariate testing and the steps involved, let’s see what it looks like in practice!

1. Obvi

The Obvi marketing team thought that they might achieve higher sales if they changed up their unique selling proposition. They performed online testing in order to find out for sure.

Obvi created many different versions of their USP in order to discover for sure which one would work best.

They decided to test their headline, subheadline, and products for a campaign focused on better sleep.

Here are a few different combinations of headlines and subheadlines that they included in their multivariate test.

They also tested headlines, subheadlines and products related to weight loss.

Multivariate testing was their only option, since they were testing more than one element simultaneously.

2. Varnish & Vine 

Varnish & Vine wanted to boost conversions on their product pages by optimizing multiple elements simultaneously. 

They conducted multivariate tests on elements like headlines, benefit lists, and calls-to-action by using OptiMonk’s Smart Product Page Optimizer

This AI-powered tool automatically analyzed their product pages, creating compelling headlines, subheadlines, and benefit lists tailored to their target audience to drive conversions.

They achieved a 44% boost in their online revenue, and improved their conversions by 28% after the optimization. 

A smarter way to run multivariate tests

Although multivariate tests are an excellent way to try out optimization ideas that are too complex to fit into a regular A/B test, they require a lot of effort and manual work. Luckily, there’s now an easier way to run multivariate tests without all the fuss.

OptiMonk’s Smart A/B Testing allows you to perform several types of experiments, including conventional A/B testing and multivariate testing.

Best of all, OptiMonk’s tailor-made AI makes multivariate testing fully automatic. All you need to do is set it and forget it—no expertise needed.

You simply choose which elements you want to include in your multivariate test, choose which variants you want to test, and launch it!

Ready to experiment? Try Smart A/B Testing today! 

FAQ

Are there any best practices for multivariate testing?

There are a number of best practices that can help you derive the most benefit from your multivariate tests. Three of the most important are:

  1. Setting clear objectives: Start by defining clear and specific objectives for your multivariate test.
  2. Avoiding excessive changes: While multivariate testing allows for testing multiple variables, it’s essential to avoid making too many changes at once.
  3. Ensuring statistical significance: To draw meaningful conclusions, ensure that your sample size is adequate to achieve statistical significance.

By following these best practices, you can conduct effective multivariate tests that provide valuable insights for optimizing your digital assets.

How do I decide which elements to test in a multivariate test?

Just like A/B testing, you want to identify the elements that will have the most significant impact on your key goals for multivariate testing. You can start by trying possible combinations of headlines, sub-headlines, images, and CTAs.

What tools or software can I use for multivariate testing?

Several A/B testing tools and platforms are available to help online businesses conduct multivariate tests. These tools include OptiMonk, VWO, and Optimizely.

Wrapping up

Both A/B testing and multivariate testing begin with the traditional scientific notion that you need to perform experiments to validate hypotheses. In today’s competitive ecommerce world, it’s never been more important for online businesses to use data to drive their decisions rather than gut feelings.

You can take advantage of OptiMonk’s advanced AI tools to conduct A/B and multivariate tests on your online store today!

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