The authors express their gratitude to Sanyin Siang (Managing Director, Teradata Center for Customer Relationship Management at the Fuqua School of Business, Duke University); research assistants Sarwat Husain, Michael Kurima, and Emilio del Rio; and an anonymous wireless telephone carrier that provided the data for this study. The authors also thank participants in the Tuck School of Business, Dart-mouth College, Marketing Workshop, for comments and the two anony-mous JMR reviewers for their constructive suggestions. Finally, the authors express their appreciation to former editor Dick Wittink (posthumously) for his invaluable insights and guidance. This article provides a descriptive analysis of how methodological factors contribute to the accuracy of customer churn predictive models. The study is based on a tournament in which both academics and practitioners downloaded data from a publicly available Web site, estimated a model, and made predictions on two validation databases. The results suggest several important findings. First, methods do matter. The differences observed in predictive accuracy across submissions could change the profitability of a churn management campaign by hundreds of thousands of dollars. Second, models have staying power. They suffer very little decrease in performance if they are used to predict churn for a database compiled three months after the calibration data. Third, researchers use a variety of modeling "approaches," characterized by variables such as estimation technique, variable selection procedure, number of variables included, and time allocated to steps in the model-building process. The authors find important differences in performance among these approaches and discuss implications for both researchers and practitioners
