Create two identical welcome emails. But send one at the time you typically send your welcome emails and one at the time reflect in your hypothesis. Following the hypothesis example above: if you typically send your welcome emails two days after the user joins. Send your control email at this time. Your test group email could be sent 10 minutes after the new user joins to test the effectiveness against your baseline results from your control group. The only thing different between the two emails should be the time you sent them. If you were to test more than one element. It is call multivariate testing. For example. A multivariate test would be if you were testing both the time the email is sent and different subject line. You should only use multivariate testing when you are testing combinations of different elements. And it’s best to implement multivariate testing only after testing each individual element.
Measure the combin impact
For example. After you test and find the most effective time to send your email. You can then combine it with winning subject lines to measure the combin impact. If you attempt to test all aspects of an email at the same time. It can be difficult to determine which is contributing positively or negatively to the overall outcome.Run your test on a platform that can measure resultsNow it’s finally time to hit play on your test. Make sure you send your email from an esp that has a strong analytics dashboard so you can easily measure and assess the results. Remember to isolate all variables except the one you’re testing. So if you’re testing send times. Don’t write different subject lines and send on different days of the week or different times of day. Include the same subject lines in both emails. And just change the time sent.
The outcomes and determine
Analyze the dataOnce you’ve run your test. It’s time to assess the outcomes and determine if your hypothesis was correct or not. When C phone number testing the hypothesis above. For example. Look at open rates for each email segment to measure the impact of send time. Whichever group had the highest open rate would be the “winner.”If you’re using an esp that has built-in a/b testing. The platform should do most of the hard work for you. For example. In campaign monitor’s a/b test analytics dashboard. You can view graphs of your results and conversion values all at the same time.