Churn Risk Is a Conditional Rate: A Simpler Empirical Bayes Model
My old churn model measured how surprising a user’s silence was against their own history. That’s a real question with a real answer, but it isn’t churn risk: it flagged regulars who took a week off and ignored light users who’d quietly stopped. It passed every backtest for two years; a confused user in a focus group is what caught it. The replacement is the table banks use for late accounts – bin users by how long they’ve been gone and how much they normally use the product, count who came back – with the same empirical Bayes shrinkage as before, pointed at the right quantity this time.