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Customer Attrition Analysis For Financial Services Using Proportional Hazard Models

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Author Info

  • D. VAN DEN POEL

    ()

  • B. LARIVIÈRE

    ()

Abstract

This paper studies the topic of customer attrition in the context of a European financial services company. More specifically, we investigate predictors of churn incidence as part of customer relationship management (CRM). We contribute to the existing literature: (1) by combining several different types of predictors into one comprehensive retention model including several ‘new’ types of time-varying covariates related to actual customer behaviour; (2) by analysing churn behaviour based on a truly random sample of the total population using longitudinal data from a data warehouse. Our findings suggest that: (1) demographic characteristics, environmental changes and stimulating ‘interactive and continuous’ relationships with customers are of major concern when considering retention; (2) customer behaviour predictors only have a limited impact on attrition in terms of total products owned as well as the interpurchase time.

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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 03/164.

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Length: 45 pages
Date of creation: Jan 2003
Date of revision:
Handle: RePEc:rug:rugwps:03/164

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Related research

Keywords: Banking; marketing; retention modelling; proportional hazard model; Cox regression;

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References

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  1. Viaene, Stijn & Baesens, Bart & Van den Poel, D & Vanthienen, Jan & Dedene, Guido, 2001. "Bayesian neural network learning for repeat purchase modelling in direct marketing," Open Access publications from Katholieke Universiteit Leuven urn:hdl:123456789/102012, Katholieke Universiteit Leuven.
  2. Dekimpe, M.G. & Degraeve, Z., 1997. "The Attrition of volunteers," Open Access publications from Tilburg University urn:nbn:nl:ui:12-358836, Tilburg University.
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  6. Pastor, JoseManuel & Perez, Francisco & Quesada, Javier, 1997. "Efficiency analysis in banking firms: An international comparison," European Journal of Operational Research, Elsevier, vol. 98(2), pages 395-407, April.
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  9. Athanassopoulos, Antreas D., 2000. "Customer Satisfaction Cues To Support Market Segmentation and Explain Switching Behavior," Journal of Business Research, Elsevier, vol. 47(3), pages 191-207, March.
  10. S. Baranzoni & P. Bianchi & L. Lambertini, 2000. "Market Structure," Working Papers 368, Dipartimento Scienze Economiche, Universita' di Bologna.
  11. Lorin M. Hitt & Frances X. Frei, 1999. "Do Better Customers Utilize Electronic Distribution Channels: The Case of PC Banking," Center for Financial Institutions Working Papers 99-21, Wharton School Center for Financial Institutions, University of Pennsylvania.
  12. Leeflang, P.S.H. & Wittink, Dick R., 2000. "Building models for marketing decisions: past, present and future," Research Report 00F20, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
  13. Maxham, James III, 2001. "Service recovery's influence on consumer satisfaction, positive word-of-mouth, and purchase intentions," Journal of Business Research, Elsevier, vol. 54(1), pages 11-24, October.
  14. Kumar, Dhananjay & Westberg, Ulf, 1997. "Maintenance scheduling under age replacement policy using proportional hazards model and TTT-plotting," European Journal of Operational Research, Elsevier, vol. 99(3), pages 507-515, June.
  15. Lorin M. Hitt & Frances X. Frei, 2002. "Do Better Customers Utilize Electronic Distribution Channels? The Case of PC Banking," Management Science, INFORMS, vol. 48(6), pages 732-748, June.
  16. Jones, Michael A. & Mothersbaugh, David L. & Beatty, Sharon E., 2002. "Why customers stay: measuring the underlying dimensions of services switching costs and managing their differential strategic outcomes," Journal of Business Research, Elsevier, vol. 55(6), pages 441-450, June.
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Citations

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Cited by:
  1. K. W. De Bock & D. Van Den Poel, 2012. "Reconciling Performance and Interpretability in Customer Churn Prediction using Ensemble Learning based on Generalized Additive Models," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/805, Ghent University, Faculty of Economics and Business Administration.
  2. K. Coussement & D. F. Benoit & D. Van Den Poel, 2009. "Improved Marketing Decision Making in a Customer Churn Prediction Context Using Generalized Additive Models," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 09/603, Ghent University, Faculty of Economics and Business Administration.
  3. B. Larivière & D. Van Den Poel, 2005. "Investigating the post-complaint period by means of survival analysis," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/299, Ghent University, Faculty of Economics and Business Administration.
  4. A. Prinzie & D. Van Den Poel, 2003. "Investigating Purchasing Patterns for Financial Services using Markov, MTD and MTDg Models," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 03/213, Ghent University, Faculty of Economics and Business Administration.
  5. D. Van den Poel, 2003. "Predicting Mail-Order Repeat Buying. Which Variables Matter?," Review of Business and Economics, Katholieke Universiteit Leuven, Faculteit Economie en Bedrijfswetenschappen, vol. 0(3), pages 371-404.
  6. 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.
  7. W.R Buckinx & D. Van Den Poel, 2003. "Predicting Online Purchasing Behavior," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 03/195, Ghent University, Faculty of Economics and Business Administration.
  8. B. Larivière & D. Van Den Poel, 2004. "Predicting Customer Retention and Profitability by Using Random Forests and Regression Forests Techniques," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/282, Ghent University, Faculty of Economics and Business Administration.
  9. B. Larivière & D. Van Den Poel, 2004. "Investigating the role of product features in preventing customer churn, by using survival analysis and choice modeling: The case of financial services," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/223, Ghent University, Faculty of Economics and Business Administration.
  10. Buckinx, Wouter & Van den Poel, Dirk, 2005. "Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting," European Journal of Operational Research, Elsevier, vol. 164(1), pages 252-268, July.
  11. A. Prinzie & D. Van Den Poel, 2005. "Incorporating sequential information into traditional classification models by using an element/position- sensitive SAM," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/292, Ghent University, Faculty of Economics and Business Administration.
  12. Lariviere, Bart & Van den Poel, Dirk, 2007. "Banking behaviour after the lifecycle event of "moving in together": An exploratory study of the role of marketing investments," European Journal of Operational Research, Elsevier, vol. 183(1), pages 345-369, November.
  13. K. Coussement & D. Van Den Poel, 2008. "Improving Customer Attrition Prediction by Integrating Emotions from Client/Company Interaction Emails and Evaluating Multiple Classifiers," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 08/527, Ghent University, Faculty of Economics and Business Administration.
  14. Glady, N & Baesens, Bart & Croux, Christophe, 2006. "Modeling customer loyalty using customer lifetime value," Open Access publications from Katholieke Universiteit Leuven urn:hdl:123456789/120983, Katholieke Universiteit Leuven.
  15. K. W. De Bock & D. Van Den Poel, 2011. "An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 11/717, Ghent University, Faculty of Economics and Business Administration.

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  1. Customer attrition in Wikipedia (English)

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