If you’re a marketer, you’re probably familiar with this conversation (or one much like it):
“Hey, our homepage is getting pretty boring. Let’s make some changes. I think we need a new image for our hero. And what about updating the color of that button? I think green would look better.”
Another colleague: “Yeah, I think we should add a customer testimonial at the bottom, too. That will really help sales.”
We all have a lot of ideas about how to make our websites better. But in this age of amazing technology, there’s no reason we should be driven by personal opinions, i.e. HIPPOs. Website testing is the answer. No guesswork, just statistical accuracy showing what works best. But the question becomes: What type of web test will provide the answers I need?
Understanding the differences between A/B and multivariate testing helps marketers select the proper method for reaching optimization goals and improving customer experience. Read our white paper, Website Testing 101: Understand the Differences Between A/B and Multivariate, to get all the details. Here are the basics:
What is A/B Testing?
A/B testing is a comparison of one design or group of elements or copy to another distinct design or group. It is the most common starting point for optimizing a website. The winner of the A/B test is the page that provides the highest conversion rate, or the key performance indicator that is most useful to the marketing campaign or organization.
What is Multivariate Testing?
Multivariate testing is the simultaneous analysis of multiple complex elements as opposed to an A/B test with just two variations. While multivariate testing is less commonly used, it can often produce better and more informative results than A/B tests. If used correctly, multivariate testing can identify the optimal layout, colors, offers, message, creative and format to improve conversions or increase revenue in a single test.
If you’ve yet to begin a web testing program, just start small. Form a hypothesis about a single element and run an A/B test. If you’re experienced at running A/B tests, but you’re ready to experiment with more complex subjects, it’s a great time to look into multivariate testing. Remember, even a “failed” test is successful because you learned that the hypothesized result from the change was incorrect. These are insights into visitor behaviors and will help inform your next hypothesis and test.
