Split Testing vs. A/B Testing
Both of these options garner data-backed insights to help you optimize your website and/or marketing campaigns. It is important to understand when to use which approach.
What’s the difference between the two?
Let’s visualize the product page of your website for an example. A split test would involve creating two unique web pages and feeding one version to a group of potential customers, and the second version to a different group of potential customers. The two versions of these pages could have entirely different layouts, headings, copy text, images, call-to-actions, etc.
The next step is to monitor consumer behavior and review the analytics. Which page is performing better? Key data to compare is conversion rates, average value per conversion, time spent on the page, and what visitors are clicking on.
Let’s use the same example for A/B testing. This would work in a similar manner except, instead of testing two different pages, we would test a single element on two otherwise identical pages. For instance, you might just change the call to action, or just the color of the call to action button, or perhaps the placement of the call to action button (top versus bottom, for example).
Next, just like with the split test, you would review the analytics to see which version of the page is performing better. Even small changes can make a big difference. One study revealed a 22% increase in conversions when a company used a red call-to-action button versus a green one (The Button Color A/B Test: Red Beats Green (hubspot.com)
Which should I use?
Many marketers favor A/B testing, since it allows you to pinpoint the exact change that made the difference. When you change more than one element, it makes it difficult to determine which changes made a positive impact. However, the answer to the question still depends on your individual situation.
While the majority of the time A/B testing will make the most sense, there are situations where the split test should be the preferred method. The split test works best when you are comparing two widely different strategies. For example, let’s visualize your product page again. Suppose you wanted to compare the performance of a more “hard sell” approach versus a “soft sell” approach. In this situation, changing a single element would not work, as the entire page would need to be adapted to fit the specific sales strategy. This could mean altering the title, the text, the call-to-action, and possibly even images and videos.
Once you have analyzed the results to see which page performs best, it is still advisable to run additional A/B tests in order to tweak the strategy further. For instance, suppose the soft approach worked better. Now you can test different “soft” titles, copy, calls-to-action, etc., to see which individual changes make an impact.
How does this apply to marketing campaigns?
While the example we did above was for a web page, the aims and goals would be the same for a marketing campaign. Rather you are running google ads, social media ads, or an email campaign, conducting a split test or A/B test is a best practice. You can try out different headlines, copy, images, and calls-to-action, and then check the analytics to see the differences in performance.
Split tests and A/B tests are a crucial part of optimizing your marketing campaigns. Unlike traditional advertising, digital advertising provides very specific and fully trackable consumer insights. Leverage this data fully through spit testing and A/B testing to ensure you get the most value out of your marketing dollars.
Ciniva is well versed in split campaigns and A/B campaigns. Let us do the work while you revel in the results. Contact our team to learn more.