A/B Testing Calculator
In email marketing, you only get one shot to get things right. Once you press send, that’s it. The email goes into your subscriber’s inbox, and you can’t change it. Unlike website development, where you can make constant changes and improvements to your user’s experience, email is out of your hands once it goes live. For this reason, A/B testing is one of the best ways to ensure you’re learning from your email campaigns and making improvements for future ones.
| Emails sent: | Unique opens: | Open rate: | ||
| A | 0% | |||
| B | 0% |
Statistical significance is a way of mathematically proving that a certain statistic is reliable. When you make decisions based on the results of experiments that you’re running, you will want to make sure that a relationship actually exists. Statistical significance is important because it gives you confidence that the changes you make to your email campaigns actually have a positive impact on your conversion rate and other metrics. Your metrics and numbers can fluctuate wildly from day to day, and statistical analysis provides a sound mathematical foundation for making business decisions and eliminating false positives.
