Choose this use-case if you wish to develop a model that identifies customers most likely to churn out of your system. This model sends promotions, emails, offers, etc., to boost customer retention. Given a dataset of customer attributes, you can create a model that generates the likelihood of the user to churn out. We recommend incorporating as much data as possible about user attributes, such as age, location, service subscribed, etc.
Dataset and Feature Group RequirementsThis section specifies the Datasets / Feature Groups requirements to successfully train a Customer Churn Prediction model. Feature requirements include recommendations on additional datasets that might enhance model performance.
Training Models - Training Options and MetricsThis section describes all the available model training options that can be used to create a Customer Churn Prediction model. You can utilize the metric explanations to better understand how they measure the performance of the model you trained.
Evaluating PredictionsThis section contains a quick model evaluation guide that helps you understand how well your model is performing.
Prediction APIThis section discusses the prediction API method so that you could properly generate predictions from the model you deployed.