50 will receive issue A and the other 50 will

shiyabulsa@gmai

Able to test several times until you find the best result, and sometimes use different variables on the same action. This is what is known as AB testing. Here, we are going to explain what they are about and we will also debunk some myths about AB testing within the different marketing strategies. What is an AB test? It is the application of different variables within the same action with the objective of comparing their performance, and thus being able to choose the best one for later use. To make it more practical, imagine the following, you are going to send an email campaign with two different subjects : Subject A : Learn about our offers this

month ADVERTISEMENT Issue B : Are you going to lose the Christmas offers? That Jiangsu Mobile Phone Number List campaign will be sent to 100 people, that means that 50 will receive issue A and the other 50 will receive issue B. In the end, you will know which variable performed better. This test is usually applied to landing pages, emails, pop-ups and other channels that request conversion data or lead the visitor to perform an action. What are the myths of AB testing and how true are they? Like any practice within digital marketing , the AB test has myths about its use. We are going to reveal some so that you keep them in mind when you meet them. The tests can be done at a different time and not simultaneously When test A and B are used at different times or times, they may not have the best performance, and the data they bring us may correspond to





a partial truth. When doing an AB test, it is important to have tools that allow you to do it at the same time, for example: send an email to a certain database with two different subjects, to a similar audience segment and at the same time, since emails, as well as other people's consumption habits, are subject to schedules; A landing page that contains two different layouts and you want to analyze, for example, which image or text is generating the most conversions. When analyzing the different versions, it is important that you look at the moment in which the content was published. If you do not see results the next day, it is better to stop doing the test. Y