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For growth marketers, there’s nothing more important than data. Wait, scratch that — there’s nothing more important than analyzing data properly. If you fail to do so, it’s easy to come to the wrong conclusions. Fortunately, there’s a way to measure data without any room for guesswork: A/B testing.
A/B testing, also known as split testing or bucket testing, is one of the most powerful tools in a marketer’s kit. It’s a method of comparing two different user experiences against each other to determine which one drives better results.
First, you’ll randomly divide your target audience into a test versus control group and show a different experience to each group across the same time period. Below is an illustrative view of how A/B testing works:
If at the end of the experiment period, you have observed a conversion rate of 23% across the control group and 35% across the test group, with the only significant difference being the designed experience, then you can conclude that your change in experience caused the improvement.
The elements that typically change for test experiences include:
Imagine that you make a change to your website and your signup rate goes up around the same time. You may be tempted to attribute the results to the change that you made, but without the benefit of a control group, you have no way to tell whether your change caused the result or if they merely happened around the same time. The world that we live in is constantly changing — your signup rate improvement could’ve been caused by a change in your audience makeup, seasonal shifts, an unexpected press hit, or even random chance. Without a control group, you can’t confidently conclude whether your actions had the desired effect.
While A/B testing sounds simple in theory, setting up a proper A/B test can be quite challenging and is something that many people get wrong. If your tests aren’t executed properly, your results will be invalid and you will be relying on misleading data.
In a perfect data world, there would be no uncertainty. However, even A/B tests have limitations — after all, you’re measuring a sample of the infinitely many future visitors to your site, and then predicting how those visitors would behave. Any time we try to glean knowledge about a whole population or predict future behavior, there’s always going to be an element of uncertainty there.
That’s where statistical significance comes in. In the context of A/B testing, the concept of statistical significance is to “quantify uncertainty.” In other words, statistical significance is “how likely it is that the difference between your experiment’s control version and test version isn’t due to error or random chance.” It’s an integral component of A/B testing and plays an essential part in conversion optimization and user testing.
A/B testing is an essential part of marketing, and, ultimately, growing your business. That’s why you shouldn’t just run one or two tests and call it a day; A/B testing must be a continuous part of your marketing strategy. And although you’ll likely run into many mediocre results before finding success, it’s all apart of the process — with careful planning and patience, you’ll get there.
Now that you’ve touched on A/B testing and statistical significance, it’s time to go over other types of testing, including multi-page funnel testing and multivariate testing. Read our post on implementation and testing for D2C brands for more.
Originally published on March 9th, 2020, last updated on June 22nd, 2022.