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Defection Detection: Measuring and Understanding the Predictive Accuracy of Customer Churn Models

Scott A. Neslin¹; Sunil Gupta²; Wagner Kamakura³; Junxiang Lu⁴; Charlotte H. Mason⁵

¹ Tuck School of Business, Dartmouth College · ² Graduate School of Business, Columbia University · ³ Fuqua School of Business, Duke University , · ⁴ Comerica Bank · ⁵ Kenan-Flagler Business School, University of North Carolina ,

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.

DOI

10.1509/jmkr.43.2.204

Volume

43 (2)

Pages

204-211

Language

en

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BibTeX

Sources

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