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

Kamakura, Wagner A., Neslin, Scott, Gupta, Sunil and Mason, Charlotte, Defection Detection: Measuring And Understanding the Predictive Accuracy Of Customer Churn Models, Journal of Marketing Research, Vol. XLIII (May 2006), 204–211

9 Pages Posted: 12 Feb 2014  

Scott Neslin

Tuck School of Business at Dartmouth

Sunil Gupta

Harvard Business School

Wagner A. Kamakura

Rice University

Junxiang Lu

Comercia Bank

Charlotte Mason

University of North Carolina (UNC) at Chapel Hill - Marketing Area

Date Written: 2006

Abstract

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 modelbuilding process. The authors find important differences in performance among these approaches and discuss implications for both researchers and practitioners.

Suggested Citation

Neslin, Scott and Gupta, Sunil and Kamakura, Wagner A. and Lu, Junxiang and Mason, Charlotte, Defection Detection: Measuring and Understanding the Predictive Accuracy of Customer Churn Models (2006). Kamakura, Wagner A., Neslin, Scott, Gupta, Sunil and Mason, Charlotte, Defection Detection: Measuring And Understanding the Predictive Accuracy Of Customer Churn Models, Journal of Marketing Research, Vol. XLIII (May 2006), 204–211. Available at SSRN: https://ssrn.com/abstract=2394082

Scott Neslin (Contact Author)

Tuck School of Business at Dartmouth ( email )

Hanover, NH 03755
United States

Sunil Gupta

Harvard Business School ( email )

Soldiers Field Road
Morgan 270C
Boston, MA 02163
United States

Wagner A. Kamakura

Rice University ( email )

6100 South Main Street
P.O. Box 1892
Houston, TX 77005-1892
United States
(713) 348-6307 (Phone)

Junxiang Lu

Comercia Bank ( email )

Auburn Hills, MI
United States

Charlotte Mason

University of North Carolina (UNC) at Chapel Hill - Marketing Area ( email )

Chapel Hill, NC 27599
United States

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