What is Split Testing in Digital Marketing?
Split testing is also known as A/B testing. It enables marketers to make comparisons between two different versions of a web page — a control (the original) version and the variation — to figure out which version performs better, with the goal of boosting conversions. You would ideally have just a single difference between these pages so that you can understand the reason behind the change in performance.
You’re essentially running an experiment to split your audience to test a number of variations of a campaign and determine which performs better. You’re showing version A of a piece of marketing content to one half of your audience, and version B to another.
The whole purpose of a split test is to improve website metrics (such as clicks or conversions) or improve the performance of another marketing asset.
Split tests are generally carried out on landing pages or product pages (in the case of eCommerce companies), but it is possible for you to conduct a split test for any page on your website.
After the test has reached a statistically significant sample size, your design and optimization team will look into the differences in behavior and pick a winner. However, there could also be an inconclusive test result if no measurable differences can be found.
Why is Split Testing important?
Split testing is extremely important because it helps ensure that decisions aren’t being made purely on the basis of gut feel or guesswork. In the absence of split testing, you might see decisions being made on the basis of best practices (a word that conversion rate optimization professionals sorely detest) or in line with the Highest Paid Person’s Opinion (HiPPO).
Why isn’t it a good idea to just follow best practices or the highest paid person’s opinion? Because best practices are based on what worked in the past for others (there is no guarantee that something that worked elsewhere will work for your company) and it’s very possible that the highest-paid person’s opinion can be just as flawed as anyone else’s.
Even marketers, designers, and writers with years (and yep, decades too) of experience under their belts could go wrong when they’re trying to determine what would strike a chord with their users and get them to take action. When you engage in split testing, you’re putting the ball in the user’s court and letting them take the call rather than forcing your conversion rate optimization (CRO) team to run down a dead-end because of a decision taken without looking at the data.
Split testing is also great for websites that see low levels of traffic. Such websites can’t really rely too heavily on other testing methods such as multivariate testing because those tests require a higher quantity of daily traffic. It’s also easier to conduct a split test and pick a winner than it is to conduct multivariate tests, analyze the data, and see what works the best.
Now for the most obvious reason to opt-in for split testing - the increase in conversions that you see due to split testing will increase your sales and your average order value, which is bound to have a fantastic effect on your bottom line.
What’s the difference between a Split Test and an A/B Test?
Yes, I know we said that split tests are also known as A/B tests. The fact is that the terms ‘split test’ and ‘A/B test’ are used interchangeably, but there are nuances that differentiate between these tests. It’s more of a difference in emphasis.
A/B testing involves two web pages or website variations that are competing against each other. Split refers to the fact that the traffic is equally split between the existing variations of a webpage or other marketing content.
Similar to A/B testing, split testing can be used to evaluate minor changes to a single website element (like a different image, header, call to action, button color, signup form, etc.), it can even be run to compare two entirely different styles of design and web content.
All the users will be unknowingly split into groups. A control group will see the original version and the other group(s) will see the new version(s) - the variation(s).
How do you set up a Split Test?
The process of setting up and conducting a split test can be broken down into 6 steps. These steps are -
Step 1: Carry out informal research
See what customers say about your brand and your products. Take a look at customer reviews, speak with your product designers, sales, and support staff. Study the metrics for your marketing campaigns. Find the common themes in these varying sources of feedback. You’d even want to conduct social listening (preferably along with a sentiment analysis tool if there is a very large amount of social media users talking about your brand).
Step 2: Check where your users exit your website
Make use of tools like Google Analytics to see which of your web pages have the highest exit rates, figure out what’s confusing them or triggering them to exit your website from your page. This will help you narrow down on the pages that you should be conducting split tests on.
Step 3: See which page elements your users interact with
Make use of heat maps to see how large groups of your users scroll through your webpage, which elements they click on, and where they tend to hover their mouse pointers.
Step 4: Collect customer feedback
Make use of customer polls, on-page surveys, chatbots, and other feedback collection tools to acquire direct feedback from your website visitors and customers.
Step 5: Watch session recordings
Study recordings of individual sessions, see how users navigate through your website and interact with it. Watch these sessions while keeping in mind the feedback that you receive from your customers.
Step 6: Conduct usability testing
Usability testing will help you watch real people use your website, allowing you to create a better, frictionless experience.
At this point, you can have a strong split test hypothesis.
Step 7: Test your hypothesis
Create a variation and test it against the original webpage. Use your analytics tools to track the performance and see which version brings you more conversions.