In 2010, digital marketing was new to us all. With new platforms came new tactics that could optimize campaigns more efficiently than ever before. While many stayed away, the organizations who capitalized on these technologies disrupted their markets.
Platforms like Google and Facebook were the first to give advertisers the chance to A/B test for the first time, but since those early days of digital advertising a lot has changed.
While we’ve read multiple great A/B testing guides online, we realized that there needed to be a guide that will help a new generation of advertisers who want to leverage AI and the new platforms of today. Just like the first to use Facebook, the companies who leverage these new platforms first will also disrupt their markets.
As advertisers, we’re always looking for new ways to improve the performance of our campaigns. A/B testing is a common method of optimizing because it allows you to test different versions of ads and see which ones do better.
In this guide, we’ll explain the fundamentals of A/B testing, but also demonstrate how you can use cutting-edge AI technology to test faster and more effectively than your competitors.
First of all, what is an A/B test? Simply put, It’s a method of comparing two versions of an ad. For example, if you’re trying to increase the number of people who sign up for your newsletter, you might create two versions of the ad.
Version A could be your current ad, while version B could be a slightly modified ad with a different headline or call-to-action button. You would then run both ads and see which one converts more clicks into newsletter subscribers.
More details in the next sections of our guide!
A/B testing is more than for advertising, it can be used to improve just about any metric on your website or app, from click-through rates and conversion rates to time on site and bounce rate. This means that the knowledge you gain in this area will be transferable to many other domains in the future.
If there’s something you want to optimize in an app, site or ad, chances are there’s an A/B test that can help you do it. And even if you’re not sure what needs optimizing, learning how to run tests can give you valuable insights into how users interact with your product and what changes might lead to improvements.
You might have experience running experiments and find that sometimes they don’t work out the way we expect them to! There’s a number of reasons this might happen which include lack of budget, too short a testing period or too few differences in variations.
This guide will help you make sure you don’t run into these problems any more. A well designed experiment will usually provide statistically significant results that we can act upon with confidence. In other words, as long as tests are properly set up, there’s a lot of value to be gained.
This is a fair question and we wanted to answer at the start of our guide. The main reason we might not recommend A/B testing is if you’ve just begun running your ad campaigns. With small budgets, your test might be too small, which runs the risk of making decisions on insufficient data.
Another time to avoid using an A/B test is if you’re testing something that could have a major negative impact on your business (like a redesign). It’s best to avoid an AB test altogether and go with another method like focus groups or surveys.
Throughout this A/B testing guide we’ll explore the art and science of this interesting process and help you master it.
Enjoy the guide!