Once you have segmented lists, india whatsapp number data it’s time to form a hypothesis, or “educated guess,” just like you would in a scientific test. To develop your hypothesis, first pick a segment of your list to focus on, then pick a single element to test that’s key for that group.
For example, you may make an educated guess about what the outcome would be of changing the time you send welcome emails. Similar to setting a goal, your hypothesis should be S.M.A.R.T. (Specific, Measurable, Achievable, Relevant, and Timebound). In this case, your hypothesis could be “sending welcome emails within 10 minutes of a user joining will increase email open rates by 6% over the next three months with the new user segment.”
Split each segment into an “A” and “B” test group
Now that you’ve formed your while the amount of time and resources hypothesis, split the subscriber segment in two: an “A” group for your control group and a “B” group for your test group.
Split the segment equally at random to ensure the results aren’t skewed one way or the other. The easiest way to achieve random group selection is to use an email service provider (ESP) that has built-in A/B testing.
Assess if each group is large enough to provide statistically significant results to ensure the most accurate data. If the groups are too small or not varied enough, the test will be prone to just reflect the results of randomness. Whereas a larger group will increase the accuracy of results by reducing the probability of randomness.
Create “A” and “B” test assets
To test a specific aspect phone number list of your email, create two variations of the same email with just that single element changed to reflect your hypothesis.
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 combined 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.
Once you’ve run your test, it’s time to assess the outcomes and determine if your hypothesis was correct or not. When 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.”