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 in order to make more accurate predictions about customer tenure, lifetime value, and so on. In this paper we reanalyze data from a leading book on data mining (Berry and Linoff 2004), who drew the dire conclusion that "parametric approaches do not work'' for such a task. 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.