Your recipients know best, which newsletters/mailings they like. A/B test have proven useful for this purpose. We will show you how A/B tests work and what you need to bear in mind.
There is a multitude of factors, which need to be optimised in a newsletter or mailing, e.g. the subject line, the header graphics, the design of call-to-action elements and last but not least, the copy. It should be the goal of each email sender to design each element in such a way that it is attractive to the target group. In order to achieve an optimal newsletter/mailing, there are different possibilities: Checklists, recipient surveys and trials.

Compare Two Email Variants

A proven test procedure is the so-called A/B test. In an A/B test, two variant (A and B) of an email are sent, which differ in one factor (e.g. the subject line). By comparing the performance figures (when testing the subject line, e.g. the open rate), you can now analyse, which variant works better. It is important to bear in mind that you can only text one factor of the email in an A/B test. If you test the subject line, for example, other factors, such as the send time, must be the same in both emails. This will guarantee that the difference in the success figures is actually caused by the tested factor and not otherwise distorted. Tests, where several factors are tested simultaneously, are called multivariate tests and they are more complex to carry out.
In principle, there are two possibilities to run an A/B test. Firstly, it is possible to test during the running operation. This only makes sense with periodical sendings, such as newsletters. An edition of a newsletter is sent in two variants and the following edition will be based on the version with the better performance figures. Secondly, it is possible to run a trial sending prior to the sending of the actual mailing. In this case, you choose two approval groups from the mailing list, which will each receive a different version of the email. The more successful variant will the be sent to the remainder of the mailing list. This can also be automated.

Guarantee Representativeness

A/B do not need to be limited to two newsletter variants. In theory, you can test an unlimited number of variants against each other. The pre-condition is that the mailing list is large enough to create approval groups with enough recipients for a representative view. The groups should have the same size. It is also recommended to randomly choose the recipients in order to guarantee an even distribution of, for example, socio-demographic properties.

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