Have you heard of multivariate testing? It’s an innovative technique that allows you to optimize your campaigns and achieve success.
Testing different aspects of your website, email campaigns and marketing efforts helps you improve your stats. Sometimes, you only need to know if a red or green button works best. Other times, you’re testing the preference of “my” over “your.”
What happens when you need to make multiple changes? Testing each hypothesis would take ages.
That’s where multivariate testing comes in. With this technique, you can test more than one variable at a time. As a result, you can optimize content and entice customers. Since more than 60% of shoppers ordering something online every 30 days, it’s crucial to tap into the digital market.
A Closer Look at Multivariate Testing
If you’re a numbers geek, you’ll adore multivariate testing. It’s like a supercharged science experiment with multiple tests that finish at the same time. Still, how does it work?
Let’s say you have a simple website with four different sections, including the:
- Feature Image
- Body Text
You aren’t quite sure which versions of your site work best. For example, you may have several ideas for how to word the heading. Maybe you want to pose a question or share an inspirational quote?
For the sake of this example, let’s say you have two variations of each section. You can run a test that tries different combinations of and identify which your customers prefer.
With multivariate testing, you might try combinations such as:
- Headline A + Image A + Body Text A + CTA A
- Headline A + Image A + Body Text A + CTA B
- Headline A + Image A + Body Text B + CTA A
- Headline A + Image B + Body Text A + CTA A
Continue new variations until you find a solution. Keep on eye on key performance indicators like website traffic, impressions, click-through rate, unsubscribes and more.
How Does It Differ From A/B Testing?
If you want to find success as a marketer, you must test your campaigns and understand your typical buyer. The more information you have, the better you can optimize your attempts.
With an A/B test — also called a split test — you compare two versions of a page, ad or email. You split your typical traffic down the middle and send half to each version. This test offers a narrow analysis of the page. You can, of course, compare several pages and have an A/B/C/D test.
With multivariate testing, you achieve a more advanced examination. Instead of looking at two versions of a page, you look at different elements and how they perform together. You can dig into subtle wording choices and color changes.
When to Use Multivariate Testing
Marketers use A/B testing when they make significant changes to their work. For example, a website redesign is an excellent time to test two different versions, old and new.
Use multivariate testing when you make small changes and want to see how customers interact with them.
Some examples of when to use multivariate testing include:
- You have enough time to run a wide range of tests
- You want to choose between two very similar designs
- You want to determine which elements people interact with
- You have enough subjects to easily split tests into segments
Multivariate testing shows which design elements positively and negatively impact your audience. To get an edge over the competition, make minor adjustments and retest. Your conversion rate will slowly creep up while theirs remains stagnant.
The Pros and Cons of Multivariate Testing
You’ll discover a number of key benefits to multivariate testing. You can avoid repeating split tests to determine small changes, which is helpful in the end stages of design.
Other benefits include:
- You identify how elements work together
- You save time, as tests occur simultaneously
- You determine the impression of each component
As with anything, multivariate testing comes with pros and cons. For example, splitting your customer list repeatedly may water down results. You need a large amount of visitor traffic to acquire meaningful insights.
Due to the fully factorial nature of these tests, the number of variations can add up quickly. The more variations, the less traffic allocated to each. Unlike A/B tests, where you split traffic into two segments, multivariate tests cut your audience into quarters, sixths or smaller.
Before you run a multivariate test, determine the sample size you’ll need to achieve significant results. If your page traffic is low, consider using an A/B test instead.
Another challenge appears when you test an element that doesn’t have a measurable effect on the conversion goal. For example, variations of images on the landing page may not have an effect. Yet modifications to a CTA or headline will. Therefore, you may have more success if you run an A/B test.
Types of Multivariate Tests
Before you begin, research the two types of multivariate tests, full factorial and fractional factorial.
With this method, you test all possible combinations of variables over equal traffic segments. For example, say you test two variations of one element and two of another. Each of the four combinations will receive 25% of your traffic.
Fractional factorial testing only looks at a fraction of the possible combinations on your traffic segments. This method requires less traffic, but it’s less precise than full factorial testing.
How to Use Multivariate Testing to Boost Your Strategy
Don’t think of A/B and multivariate testing as two separate things. You’ll find a time and place for each.
Sometimes, you’ll run both tests at the same time to collect more data. For example, you may want stats on how well a design performs vs. another. At the same time, you might want to seek information on specific elements. The different methods are meant to complement each other.
Whether you run an A/B or multivariate test, remember to let it run its course. You can watch the results in real-time, but you shouldn’t enact permanent changes until the test is complete. Remember to zero-in on a couple of key metrics, instead of analyzing dozens at once.
Ready to start testing? First, determine your goal. Then, predict how you can achieve it based on proposed changes. Once you know where you focus, you’re ready to test and optimize.