- The killer of A/B tests is the lack of a clear hypothesis.
Seriously, how are you going to get meaningful results if you don’t understand what exactly you want to test? It’s like going to the store without a shopping list - at best, you’ll buy something you don’t need, at worst, you’ll go home empty-handed. The first step is to formulate a hypothesis that you want to confirm or deny. For example, “Using emoji in the subject line will increase open rates by 15%.”
- Perhaps the most common mistake is changing the testing parameters.
You’ve launched an A/B test comparing two versions of the email subject line. But then you decide to add another experimental group with a test for the sending time. Bam! Your results have just turned into a pumpkin. It’s now almost impossible to objectively compare and draw conclusions. Conclusion: choose the metrics and testing parameters in advance and strictly follow them.
- The next mistake is incorrectly calculating the sample size.
Imagine you decided to compare two versions of an email, but only selected 50 people for the test group. Yes, the results may be interesting, but will they be statistically significant? Unlikely. So before launching a test, analyze the size of the base, determine the minimum threshold for representativeness, and distribute the audience accordingly.
- Another common mistake that cannot be ignored is testing several elements at once.
You want reliable results, not a mess from an axe, right? If you change the subject line, sender, CTA, and design at the same time, how will you understand what exactly influenced the final result? It is better to focus on one element at a time. Then you will be able to understand what worked and what did not, and continue optimizing.