The first step to building a churn model is to clearly identify a churn event, the metric that indicates a customer has churned. How this is defined depends on how your business works: For instance, if you know that most customers don’t return if they haven’t made a purchase in 60 days, you can use unique user IDs and timestamps to pinpoint which customers have churned according to the churn event you’ve defined.
Actually defining the churn event, however, has its challenges. Sometimes, customer status might already be explicitly defined in your data schema; in other cases, a customer might cancel and reactive service as a way to save money or delay a delivery. It’s not always clear if a customer churned or not. So, if you’re presented with a database of timestamps of cancellations, activations, and renewals, how can you properly define a churn event? In this piece, we walk through the process of defining the churn target variable.