How to segment in CheetahMail. Part II
As we indicated in the first post in the seriesWe present the segmentation functionalities of CheetahMail because it is one of the most established platforms in our market; and we believe that many of you who follow the blog will be familiar with the tool.
In the first post dedicated to this topic, we dealt with demographic segmentation within the standard type. Now we are going to look at two types of segmentation within the section that in CheetahMail is called System

Insights
Insights is the name given to the possibility of segmenting on the basis of variables from Engagement and/or Frequency Cap. Let's look at the first one.
In Insights select Engagement and drag the icon (text) to the right. Remember that in any type of segmentation we carry out, it is a necessary condition to have selected the subscription list(s) in which the users are found.

A window will appear with a box in which we can define the engagement conditions. For example, we may want to include users subscribed to a list who have opened n emails in a given period of time. We can see in figure 3 that there are several options depending on whether we combine active/inactive by open, click or transaction in different time periods.

Once the segment has been created, if we click on Group:EngagementIf you have a group, you can save that group so that you can use it in later segmentations.

The second segmentation option within Insights, corresponds to Frequency Cap. This is to segment a list based on the frequency with which it has been impacted by an emailing in the past. As in the previous case, drag the icon (text) to the right area and select the option you want. In this case, we can also save the group for later use in other segments.

Subscription
In this section we can segment the users of a list(s) according to whether they are subscribed to an affiliate. What is interesting here is the option to select the date or range of dates on which the user subscribed to the affiliate. These types of selections are very useful when trying to find relationships between the age of registration and behaviour.

We thank Experian Marketing Services their collaboration.