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Benefits of Quantile Regression for the Analysis of Customer Lifetime Value in a Contractual Setting: An Application in Financial Services

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Author Info

  • D. F. BENOIT
  • D. VAN DEN POEL

    ()

Abstract

The move towards a customer-centred approach to marketing, coupled with the increasing availability of customer transaction data, has led to an interest in understanding and estimating customer lifetime value (CLV). Several authors point out that, when evaluating customer profitability, profitable customers are rare compared to the unprofitable ones. In spite of this, most authors fail to recognize the implications of these skewed distributions on the performance of models they use. In this study, we propose analyzing CLV by means of Quantile Regression. In a financial services application, we show that this technique provides management more in-depth insights into the effects of the covariates that are missed with Linear Regression. Moreover, we show that in the common situation where interest is in a top-customer segment, Quantile Regression outperforms Linear Regression. The method also has the ability of constructing prediction intervals. Combining the CLV point estimate with the prediction intervals leads to a new segmentation scheme that is the first to account for uncertainty in the predictions. This segmentation is ideally suited for managing the portfolio of customers.

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Bibliographic Info

Paper provided by Ghent University, Faculty of Economics and Business Administration in its series Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium with number 09/551.

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Length: 34 pages
Date of creation: Jan 2009
Date of revision:
Handle: RePEc:rug:rugwps:09/551

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Related research

Keywords: customer relationship management (CRM); database marketing; customer segmentation; quantile regression; prediction interval; customer lifetime value;

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References

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  1. Paas, L.J. & Bijmolt, T.H.A. & Vermunt, J.K., 2007. "Acquisition patterns of financial products: A longitudinal investigation," Open Access publications from Tilburg University urn:nbn:nl:ui:12-210569, Tilburg University.
  2. Roger Koenker & Kevin F. Hallock, 2001. "Quantile Regression," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 143-156, Fall.
  3. Peter E. Rossi & Robert E. McCulloch & Greg M. Allenby, 1996. "The Value of Purchase History Data in Target Marketing," Marketing Science, INFORMS, vol. 15(4), pages 321-340.
  4. Brent A. Gloy & Jay T. Akridge & Paul V. Preckel, 1997. "Customer Lifetime Value: An application in the rural petroleum market," Agribusiness, John Wiley & Sons, Ltd., vol. 13(3), pages 335-347.
  5. A. Prinzie & D. Van Den Poel, 2007. "Predicting home-appliance acquisition sequences: Markov/Markov for Discrimination and survival analysis for modeling sequential information in NPTB models," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 07/442, Ghent University, Faculty of Economics and Business Administration.
  6. Moshe Buchinsky, 1998. "Recent Advances in Quantile Regression Models: A Practical Guideline for Empirical Research," Journal of Human Resources, University of Wisconsin Press, vol. 33(1), pages 88-126.
  7. Yu, Keming & Moyeed, Rana A., 2001. "Bayesian quantile regression," Statistics & Probability Letters, Elsevier, vol. 54(4), pages 437-447, October.
  8. K. Coussement & D. Van Den Poel, 2006. "Churn Prediction in Subscription Services: an Application of Support Vector Machines While Comparing Two Parameter-Selection Techniques," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 06/412, Ghent University, Faculty of Economics and Business Administration.
  9. David C. Schmittlein & Donald G. Morrison & Richard Colombo, 1987. "Counting Your Customers: Who-Are They and What Will They Do Next?," Management Science, INFORMS, vol. 33(1), pages 1-24, January.
  10. Paas, Leonard J. & Bijmolt, Tammo H.A. & Vermunt, Jeroen K., 2007. "Acquisition patterns of financial products: A longitudinal investigation," Journal of Economic Psychology, Elsevier, vol. 28(2), pages 229-241, April.
  11. Bas Donkers & Peter Verhoef & Martijn Jong, 2007. "Modeling CLV: A test of competing models in the insurance industry," Quantitative Marketing and Economics, Springer, vol. 5(2), pages 163-190, June.
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Cited by:
  1. D. F. Benoit & D. Van Den Poel, 2012. "Improving Customer Retention In Financial Services Using Kinship Network Information," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/786, Ghent University, Faculty of Economics and Business Administration.
  2. Volpe III, Richard & Park, Timothy & Hennessy, David & Jensen, Helen H., 2013. "Somatic Cell Counts in Dairy Marketing: Quantile Regression for Count Data," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 151425, Agricultural and Applied Economics Association.
  3. Chen, Zhen-Yu & Fan, Zhi-Ping & Sun, Minghe, 2012. "A hierarchical multiple kernel support vector machine for customer churn prediction using longitudinal behavioral data," European Journal of Operational Research, Elsevier, vol. 223(2), pages 461-472.

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