Variant B : The second version of the variable. are Latest Mailing Database subject lines, respectively. Two equal audiences : Your goal is to ensure that variables A and B are tested in relatively equal populations. Otherwise, your experimental results will be skewed. In the case of our emails, all you need to do is split one part of your mailing list (this will be your focus group) in half. However, when testing a website page, you have to deal with an "unlimited" audience Latest Mailing Database you don't know how many visitors will come to it.
In this case, it makes sense to split your traffic by 50. So Latest Mailing Database of your visitors will see version A and the other half will see version B. But make sure you only show one variation to each group at a time. Most A/B testing Latest Mailing Database tools can ensure this. Assumptions : Depending on what you are testing, you need to make assumptions about the results of your experiments. For example, " Email A is more likely to be opened than E-mail B because.
This will allow you to articulate your goals up front and determine which metrics you must use to confirm or disprove hypotheses. Test metrics : You'll rely Latest Mailing Database metrics that determine which variant A or B performs better. According to our assumptions, in our case it is the email open rate. If variant A has more opens than variant B at the end of the Latest Mailing Database period, then it performs better and your hypothesis is confirmed. If variant B beats variant A, it makes sense to send an email with subject line B to the rest of the list, assuming it's dismissed.