4 Observing renewal and churn
This chapter covers
- Picking a lead time in advance of churns for observation
- Picking observation dates from subscriptions or activity
- Creating an analytic dataset by flattening metric data
- Exporting a current customer list for segmentation
The essence of fighting churn with data is learning from the natural experiments that occur every time a customer chooses to stay with or churn from the service. A natural experiment in this context means a situation that tests an outcome you are interested in, but you didn’t set it up like a formal experiment. These experiments are the churns and renewals that have already occurred, and the results are waiting for you in your data warehouse. Why aren’t you learning from the ...
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