A/B testing
A/B testing, sometimes known as split testing, is a randomized process of presenting users with two different versions of a website—an A or a B version to observe which one performs better. Key metrics are then measured to see if variation 'A' or 'B' is statistically better at increasing business KPIs. Determining and implementing the winning variation can boost conversions and help with continuous improvement in customer experience.
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Contact SalesWhy is A/B testing valuable?
A/B testing clarifies which version of your product aligns with your audience, which can settle any internal debates. Depending on the number of conversion rates, you’ll know which changes to your design are helping boost sales and conversions. For example, if you change your call-to-action (CTA), and one CTA has more conversions than the other, it’s evident that the former CTA is more effective. And, when you enhance your A/B testing with qualitative data, you'll better understand your target customer and how to create experiences that resonate with them.
The most significant value of A/B testing is that it challenges assumptions and helps businesses make decisions based on data rather than on gut feelings. In particular, A/B testing is a valuable methodology as it can be applied to almost anything, whether email subject lines, color preferences, website information architecture, or even new processes. Tests can be conducted on something as small as a single copy change or as large as a website redesign.
When should A/B testing be used?
A/B testing helps explain quantitative data and is best used alongside usability testing to complement one another. As always, the earlier you test, the better.
Remember, A/B testing shouldn't be a one-time test. If you received interesting findings from one study, we recommend repeating the study multiple times to see if the results are consistent. Conducting repeat tests reduces risk, ensures any results weren't coincidental, and proves that you're pursuing the best possible solution for your product.
What are the drawbacks of A/B testing?
Due to the quantitative nature of A/B testing, designers receive few qualitative insights to explain the reasoning behind users' choices. Although these experiments are driven by hypotheses—often with controlled variations between the designs—there may be alternate reasons behind the success of one variation over another. All you know is that one design change resulted in more conversions than the other. However, it’s difficult to determine whether further improvements can yield the same or better results unless you conduct more testing—which requires more time.
