How to Achieve Increased Conversions with Email A/B Testing
How to start an email A/B testing campaign?
Start email A/B testing by creating a hypothesis that sets the foundation for a successful testing strategy. If your hypothesis is incorrect, the statistical significance of the test result will be impacted.
One reason A/B testing suffers is that many marketers are hesitant while developing hypotheses. What if it’s the wrong one? What if the test fails?
Kath makes a great point by saying that it’s okay if your hypothesis didn’t work out or you didn’t get the expected results.’ Because you learned something. You can use that learning to develop more targeted hypotheses for future email split testing.
The importance of right sample size for email split testing
The correct sample size while conducting email ab testing makes all the difference. A wrong sample size might get skewed results even though you had the right hypothesis.
But, sometimes, it’s not easy to get a large sample size because you might be starting out and have a limited number of users. In that case, Kath shares an easy trick to ensure statistical significance from your email testing campaigns: Run multiple tests over a period of time. It will help you build results, giving you a clearer picture of your test. The idea is you aggregate the results from each test and analyze it.
Use our Statistical Significance Calculator
3 best practices to run a successful email A/B testing campaign
1. Start with a hypothesis
Look at your existing data and analyze it. You’ll find gaps, loopholes, and anomalies in that data. This is how you develop a hypothesis or variable to test in your email campaigns.
2. Be clear about your success metrics
Jot down how you will measure the success of your email A/B test. You might see a rise in open rates but no increase in average order value. In that scenario, you can’t declare it a successful test as your AOV, which is a better metric, is lower. So, you must run the test again and see if anything changes.
3. Record and document your tests and results
Documentation helps you think about what worked in the past and mistakes you made, and based on this information, you can develop future email ab tests. You also get to explore your learnings from the previous tests.
One common email A/B testing mistake that marketers should avoid
One common ab-testing mistake many marketers make is stopping testing if their hypothesis fails.
This ab testing mistake often keeps marketers from running a test again, so they never get to see the real results of ab testing.
Kath is encouraging by saying it’s okay if your hypothesis doesn’t work out. You still learned something, and you can use this learning to develop better and more effective hypotheses to run your email ab testing campaigns. The hypothesis must be based on previous data and align with your success metric.
▶️ Key moments
00:00 – Introduction
04:26 – Why does email A/B testing matter?
7:13 – Important factor to run a successful email ab testing campaign
13:46 – Important components to test to analyze email conversions
18:58 – Examples of real-life A/B testing campaigns
20:58 – How to segment the audience for email A/B testing
25:18 – Email A/B testing best practices
31:56 – Mistakes to avoid while running an email A/B test