Improve your email campaigns with the A/B Split Test
With our A/B Split Test you send two versions of your email campaign to two parts of your subscription group, in order to find the best subject or contents. The more successful version is sent automatically on the basis of performance-based data or manually to the rest of the group.

A/B split test
Test two different subject lines, two different contents or both.

Determination of the winner
With the slider you can easily choose the size or rather the number of recipients of group A & group B. Then you select the criteria for determining the winning version. This will then be sent to the rest of the group.

- Open rate - The version with the highest open rates wins.
- Clicks - The version with the most unique clicks wins.
- Clicks on a link - Select a certain link. The version with the most unique clicks on this link wins.
- Conversion rate - The version with the highest conversion rate wins. (This only works when conversion tracking is used)
- Determine the winner manually - When the test period ends you will be notified by e-mail. Then you can decide on a winning version and send it to the rest of the group.
- Experiment a little and study the effects of different subject lines.Is the open rate with the subject line "20% discount on all products today " or with "Today you only pay 80%" higher?
- Find out which design is more popular. Does a simple link or a colorful button work better as call to action?
- Sell more automatically! Do your recipients rather like to buy rubber boots or umbrellas at the moment?
After the end of your chosen test period the winning version will be automatically or manually sent to the rest of the group of recipients.
Analysis during the test
After the dispatch of the split test the A/B results can be examined in the reports section. Here you can also choose a winning version manually before the end of the test period. That version is sent to the rest of the group of recipients immediately.

Why A/B split tests should be used
There a a lot of reasons why A/B split tests should be used.
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