How to do A/B testing to get your email marketing campaigns right

 

If you want to improve your conversions in 2017, A/B testing is your best friend. How to do  If you are systematic when guinea email list 100000 contact leads testing different variables in your campaigns, you will be able to optimize your results more and more and thus improve conversion and return on investment.

In this article we are going to see what an A/B test is in email marketing, what not to do in A/B testing and how to use them to optimize your campaigns.

 

What is A/B testing in email marketing?

A/B testing in email marketing is based on a very simple process. How to do  sending two versions of the same email, which only differ in one variable, and checking which of the versions gets more opens and clicks.

There are many options for variables to test, such as the email subject, the text, the copy of the call to action, the color of the call eople who open your emails, clicks on a certain link or conversions to sales, for example.

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What NOT to do in A/B testing

Now that we are clear about what an A/B test is , let’s see what mistakes we must avoid for this strategy to work:

Trying to test more than one variable at a time . This is one of the biggest mistakes you can make. If you want your aleart news tests to work, take your time and change only one variable in each test. The reason is very simple: if you change the subject line, the text of the email, and the color of the call-to-action button, for example, you won’t know which factor brought you the improvement in results. Even if you end up testing all possible variables, be patient and do it one at a time.
Using groups that are too different. A/B testing is based on sending the email to two groups of users to compare 3 moments when you really need to innovate the results with each other. If the two groups are too different, the results will not be comparable and therefore you will not be able to reach conclusions about what works best. To avoid this problem, you have to make sure that both groups of users are as similar as possible.

 

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