In this case, let’s say your metric is the number of visitors who click on the button.
Then you need to know how you want to evaluate its performance. Fung gives a simple example: the size of the subscribe button on your website.
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You start an A/B test by deciding what it is you want to test. “It’s the same core concepts, but now you’re doing it online, in a real-time environment, and on a different scale in terms of number of participants and number of experiments.” How Does A/B Testing Work? Fung says that throughout the past century the math behind the tests hasn’t changed. In the 1960s and 1970s the concept was adapted by marketers to evaluate direct response campaigns (e.g., would a postcard or a letter to target customers result in more sales?).Ī/B testing, in its current form, came into existence in the 1990s. “He wasn’t the first to run an experiment like this, but he was the first to figure out the basic principles and mathematics and make them a science,” Fung says.įisher ran agricultural experiments, asking questions such as, What happens if I put more fertilizer on this land? The principles persisted and in the early 1950s scientists started running clinical trials in medicine. In the 1920s statistician and biologist Ronald Fisher discovered the most important principles behind A/B testing and randomized controlled experiments in general. While it’s most often associated with websites and apps, Fung says the method is almost 100 years old. What Is A/B Testing?Ī/B testing, at its most basic, is a way to compare two versions of something to figure out which performs better.
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His latest book is Number Sense: How to Use Big Data to Your Advantage. To better understand what A/B testing is, where it originated, and how to use it, I spoke with Kaiser Fung, who founded the applied analytics program at Columbia University and is author of Junk Charts, a blog devoted to the critical examination of data and graphics in the mass media. One of the most common methods, particularly in online settings, is A/B testing. That’s a good thing, of course, and fortunately there are lots of ways to get information without having to rely on one’s instincts. Leaders don’t want to make decisions unless they have evidence.
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While it’s an often-used method, there are several mistakes that managers make when doing A/B testing: reacting to early data without letting the test run its full course looking at too many metrics instead of focusing on the ones they most care about and not doing enough retesting to be sure they didn’t get false positive results. The test works by showing two sets of users (assigned at random when they visit the site) different versions of a product or site and then determining which influenced your success metric the most. It’s now used to evaluate everything from website design to online offers to headlines to product descriptions. This testing method has risen in popularity over the last couple of decades as companies have realized that the online environment is well-suited to help managers, especially marketers, answer questions like, “What is most likely to make people click? Or buy our product? Or register with our site?”. While it’s most often associated with websites and apps, the method is almost 100 years old and it’s one of the simplest forms of a randomized controlled experiment. A/B testing is a way to compare two versions of something to figure out which performs better.
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