The role of pay-per-click in email monetization

[Rakibul200]

Ignoring statistical significance Sometimes one of the variants wins, but that doesn't mean it will yield a boost if you repeat the test. Why? Because the results are not statistically significant, because the audience sample is too small, or the difference between the results they produce is too small. How to do it Use an A/B testing calculator, such as the one provided by CXL, to ensure your tests produce statistically significant results.

If your audience is too small to linkedin data perform such a test all at once, consider running the test multiple times over a longer period of time and then analyze the aggregated data. Best Practices for Running Successful Email A/B Tests Once you know what to avoid, it’s time to master the best practices: Start Simple Making things too complicated right from the start can lead to chaos. It’s a good idea to start testing something simple but meaningful – like a subject line.

Test one element at a time You want to determine which variable contributes to higher open and click-through rates. So be sure to compare apples to apples—subject line to subject line, CTA text to CTA text. Select the same time and date Sending emails on different days and times of the week and comparing them will cause you trouble with analysis. If you decide to go on Tuesday morning, stick with it.