A/B testing or split testing is a widely available feature offered with most email service providers (ESPs) and marketing automation platforms. If you want to run tests on your marketing site or app, tools like VWO or Optimizely also offer solutions. When it comes to selecting your audience, consider how many people you need in your audience to be part of your overall test to establish statistical significance, or the likelihood that the difference in conversion rate between the group A and group B is not due to chance. You will want to carve out some of your overall audience to split 50/50 into these groups if you have a large enough audience. At Litmus, we have come across various tools over the years to help you. One of our favorites is Neil Patel's A/B Testing Calculator. Once you've determined how many people should be in your test audience, half of your test audience should have no changes applied to their experience. This group will be your control group. As much as possible, their experience should closely resemble what you consider your baseline or typical experience.
The other half of your audience will be your variation cohort. For users in this group, apply the test treatment. A/B testing is typically analyzed at the cohort level. Significance: We assess whether or not the cohort that received the treatment experience converted significantly different from the control cohort. It is essential that the placement of a member of the public in a given cohort is random and that each receives only one treatment. If we were to consider the Image Masking Service composition of each of the cohorts (test and control), we want to ensure that we are not introducing any bias towards any demographic, firmographic or any other user characteristic for a single cohort. Randomizing your cohorts and having fewer variations better ensures that your cohorts represent a random selection of your audience. Wrap A/B testing doesn't have to be difficult, but if you don't set it up correctly,
the insights you get from it won't mean much. Understanding the fundamentals we've explored here will set you up for success, so you can apply your learnings to your entire marketing strategy. Remember to take a step back to think about each item and you'll be on the right track. Stay tuned for our blog on A/B testing your email marketing, where we'll dive deeper into testing our favorite channel. More data. More ideas. Get more email data, more insights, when you go beyond standard email metrics. Access read rate, transfer rate and more with the power of Litmus Email Analytics, integrated directly into Litmus Plus.