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Why A/B testing is better than “A then B” guessing

In recent years, data has dethroned oil and become the world’s most valuable resource. Most companies understand the importance of data, soliciting customer feedback and utilizing numerous tools for analytics. Many also appear to recognize the value of experimentation as a source for great insights. However, when it comes to executing smart business experiments, they prove that it’s easier said than done. Beyond a standalone product or website survey, A/B testing is one of the most popular ways to gather data, particularly in online settings. Many companies use A/B testing to measure the efficiency of two versions of a webpage expected to convert visitors into customers, like a landing page for a product. According to Invesp, 71 percent of companies run two or more A/B tests a month. But what exactly is an A/B test, why is it important, and what advantages does it offer over the guessing methods many people still use? What is A/B testing? A/B testing is controlled experimentation that allows you to simultaneously compare “like for like” scenarios where one version has a different element. ‘Bucket’, ‘split’, or ‘split-run’ tests are other names for this type of experiment. A/B tests involve the use of a randomly selected “control group” and a “treatment group.” The control group represents the experiment’s comparison standard. You use this set of individuals to establish the baseline measure. In the context of webpage testing, these are the visitors you send to your current version of a given page, one with a conversion […]