At the heart of any contractual or subscription-oriented business model is the notion of the retention rate. An important managerial task is to take a series of past retention numbers for a given group of customers and project them into the future to make more accurate predictions about customer tenure, lifetime value, and so on. As an alternative to common “curve-fitting” regression models, we develop and demonstrate a probability model with a well-grounded “story” for the churn process. We show that our basic model (known as a “shifted-beta-geometric”) can be implemented in a simple Microsoft Excel spreadsheet and provides remarkably accurate forecasts and other useful diagnostics about customer retention. We provide a detailed appendix covering the implementation details and offer additional pointers to other related models.