Abstract
Customer attrition refers to the phenomenon whereby a customer leaves a service provider. As competition intensifies, preventing customers from leaving is a major challenge to many businesses such as telecom service providers. Research has shown that retaining existing customers is more profitable than acquiring new customers due primarily to savings on acquisition costs, the higher volume of service consumption, and customer referrals. For a large enterprise, its customer base consists of tens of millions service subscribers, more often the events, such as switching to competitors or canceling services are large in absolute number, but rare in percentage, far less than 5%. Based on a simple random sample, popular statistical procedures, such as logistic regression, tree-based method and neural network, can sharply underestimate the probability of rare events, and often result a null model (no significant predictors). To improve efficiency and accuracy for event probability estimation, a case-based data collection technique is then considered. A case-based sample is formed by taking all available events and a small, but representative fraction of nonevents from a dataset of interest. In this article we showed a consistent prior correction method for events probability estimation and demonstrated the performance of the above data collection techniques in predicting customer attrition with actual telecommunications data.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
King, G., Zeng, L.: Logistic Regression in Rare Events Data. Society for Political Methodology, 137–163 (February 2001)
Prentice, R.L.: A Case-cohort Design for Epidemiologic Cohort Studies and Disease Prevention Trials. Biometrika 73, 1–11 (1986)
Jacob, R.: Why Some Customers Are More Equal Than Others. Fortune, 200–201 (September 19, 1994)
Walker, O.C., Boyd, H.W., Larreche, J.C.: Marketing Strategy: Planning and Implementation, 3rd edn., Irwin, Boston (1999)
Li, S.: Applications of Demographic Techniques in Modeling Customer Retention. In: Rao, K.V., Wicks, J.W. (eds.) Applied Demography, pp. 183–197. Bowling Green State University, Bowling Green (1994)
Li, S.: Survival Analysis. Marketing Research, 17–23 (Fall, 1995)
Breslow, N.E.: Statistics in Epidemiology: The case-Control Study. Journal of the American Statistical Association 91, 14–28 (1996)
Hanley, J.A., McNeil, B.J.: The Meaning and Use of the Area under a ROC Curve. Radiology 143, 29–36 (1982)
Ma, G., Hall, W.J.: Confidence Bands for ROC Curves. Medical Decision Making 13, 191–197 (1993)
Au, T., Li, S., Ma, G.: Applications Applying and Evaluating Models to Predict Customer Attrition Using Data Mining Techniques. J. of Cmparative International Management 6, 10–22 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Au, T., Chin, ML.I., Ma, G. (2009). Mining Rare Events Data for Assessing Customer Attrition Risk. In: Prasad, S.K., Routray, S., Khurana, R., Sahni, S. (eds) Information Systems, Technology and Management. ICISTM 2009. Communications in Computer and Information Science, vol 31. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00405-6_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-00405-6_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-00404-9
Online ISBN: 978-3-642-00405-6
eBook Packages: Computer ScienceComputer Science (R0)