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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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  • D. F. BENOIT
  • D. VAN DEN POEL

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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.

Suggested Citation

  • D. F. Benoit & D. Van Den Poel, 2009. "Benefits of Quantile Regression for the Analysis of Customer Lifetime Value in a Contractual Setting: An Application in Financial Services," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 09/551, Ghent University, Faculty of Economics and Business Administration.
  • Handle: RePEc:rug:rugwps:09/551
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Bas Donkers & Peter Verhoef & Martijn Jong, 2007. "Modeling CLV: A test of competing models in the insurance industry," Quantitative Marketing and Economics (QME), Springer, vol. 5(2), pages 163-190, June.
    4. 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.
    5. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731.
    6. 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.
    7. 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.
    8. 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.
    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, L.J. & Bijmolt, T.H.A. & Vermunt, J.K., 2007. "Acquisition patterns of financial products : A longitudinal investigation," Other publications TiSEM 6e60376b-b689-4e76-93b8-5, Tilburg University, School of Economics and Management.
    11. K. Coussement & D. Van den Poel, 2008. "Churn prediction in subscription services: an application of support vector machines while comparing two parameter-selection techniques," Post-Print hal-00788096, HAL.
    12. Yu, Keming & Moyeed, Rana A., 2001. "Bayesian quantile regression," Statistics & Probability Letters, Elsevier, vol. 54(4), pages 437-447, October.
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    Citations

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    Cited by:

    1. R. Ferrentino & M. T. Cuomo & C. Boniello, 2016. "On the customer lifetime value: a mathematical perspective," Computational Management Science, Springer, vol. 13(4), pages 521-539, October.
    2. Cristina Davino & Vincenzo Esposito Vinzi, 2016. "Quantile composite-based path modeling," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 10(4), pages 491-520, December.
    3. Zhang, Jie & Thomas, Lyn C., 2012. "Comparisons of linear regression and survival analysis using single and mixture distributions approaches in modelling LGD," International Journal of Forecasting, Elsevier, vol. 28(1), pages 204-215.
    4. 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.
    5. 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.
    6. Richard J. Volpe & Timothy A. Park & Fengxia Dong & Helen H. Jensen, 2016. "Somatic cell counts in dairy marketing: quantile regression for count data," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 43(2), pages 331-358.
    7. Phiri, Andrew, 2017. "Inflation persistence in BRICS countries: A quantile autoregressive (QAR) model," MPRA Paper 79956, University Library of Munich, Germany.
    8. Ekinci, Yeliz & Ülengin, Füsun & Uray, Nimet & Ülengin, Burç, 2014. "Analysis of customer lifetime value and marketing expenditure decisions through a Markovian-based model," European Journal of Operational Research, Elsevier, vol. 237(1), pages 278-288.
    9. Andrew Phiri, 2017. "Inflation persistence in BRICS countries: A quantile autoregressive (QAR) approach," Working Papers 1702, Department of Economics, Nelson Mandela University, revised Jul 2017.
    10. repec:eee:tefoso:v:130:y:2018:i:c:p:177-187 is not listed on IDEAS
    11. repec:pal:jorsoc:v:61:y:2010:i:1:d:10.1057_jors.2009.104 is not listed on IDEAS
    12. Arunraj, Nari Sivanandam & Ahrens, Diane, 2015. "A hybrid seasonal autoregressive integrated moving average and quantile regression for daily food sales forecasting," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 321-335.

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    Keywords

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

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