This campaign aims to reactivate customers who have not ordered anything for a long time. Another option is to exclude them. This is recommended if you don’t want to spend a marketing budget on users who are less likely to convert. Shopping on smartphone 3. Predict churn based on app data (SaaS business) Imagine you are running a mobile app. In this case, you want to reduce the number of users who delete your app.
Telling good stories is no longer enough
If you have a dataset of uniquely identifiable users and their in-app actions, it is possible to make predictions about the likelihood that Canadian CFO Email Lists users will delete (or not actively use) the app. For example, use a three-month dataset that also shows whether users deleted the app, and then specify the “app deleted” column in a dataset as a predictable value. Now train your model with a set of your data.
Many brands have already
Your model learns which variables increase the chance of removing the app. You may learn that users who play five games in the first month do not delete the app for the first three months. To limit the churn , you decide based on these learnings to reward users with a nice extra after playing five games. Target audience: You place users in a target group who have not played five games after three months.