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Improving Customer Retention In Financial Services Using Kinship Network Information

  • D. F. BENOIT

    ()

  • D. VAN DEN POEL

    ()

This study investigates the advantage of social network mining in a customer retention context. A company that is able to identify likely churners in an early stage can take appropriate steps to prevent these potential churners from actually churning and subsequently increase profit. Academics and practitioners are constantly trying to optimize their predictive-analytics models by searching for better predictors. The aim of this study is to investigate if, in addition to the conventional sets of variables (socio-demographics, purchase history, etc.), kinship network based variables improve the predictive power of customer retention models. Results show that the predictive power of the churn model can indeed be improved by adding the social network (SNA-) based variables. Including network structure measures (i.e. degree, betweenness centrality and density) increase predictive accuracy, but contextual network based variables turn out to have the highest impact on discriminating churners from non-churners. For the majority of the latter type of network variables, the importance in the model is even higher than the individual level counterpart variable.

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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 12/786.

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Length: 28 pages
Date of creation: May 2012
Date of revision:
Handle: RePEc:rug:rugwps:12/786
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Web page: http://www.ugent.be/eb

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  1. Lemmens, A. & Croux, C., 2006. "Bagging and Boosting Classification Trees to Predict Churn," Other publications TiSEM d5cb664d-5859-44db-a621-e, Tilburg University, School of Economics and Management.
  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. Childers, Terry L & Rao, Akshay R, 1992. " The Influence of Familial and Peer-Based Reference Groups on Consumer Decisions," Journal of Consumer Research, University of Chicago Press, vol. 19(2), pages 198-211, September.
  4. Peppard, Joe, 2000. "Customer Relationship Management (CRM) in financial services," European Management Journal, Elsevier, vol. 18(3), pages 312-327, June.
  5. Nair, Harikesh S. & Manchanda, Puneet & Bhatia, Tulikaa, 2006. "Asymmetric Peer Effects in Physician Prescription Behavior: The Role of Opinion Leaders," Research Papers 1970, Stanford University, Graduate School of Business.
  6. Charles F. Manski, 2000. "Economic Analysis of Social Interactions," NBER Working Papers 7580, National Bureau of Economic Research, Inc.
  7. Van den Poel, Dirk & Lariviere, Bart, 2004. "Customer attrition analysis for financial services using proportional hazard models," European Journal of Operational Research, Elsevier, vol. 157(1), pages 196-217, August.
  8. 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.
  9. Prinzie, Anita & Van den Poel, Dirk, 2006. "Investigating purchasing-sequence patterns for financial services using Markov, MTD and MTDg models," European Journal of Operational Research, Elsevier, vol. 170(3), pages 710-734, May.
  10. 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.
  11. Puneet Manchanda & Ying Xie & Nara Youn, 2008. "The Role of Targeted Communication and Contagion in Product Adoption," Marketing Science, INFORMS, vol. 27(6), pages 961-976, 11-12.
  12. Baesens, Bart & Viaene, Stijn & Van den Poel, Dirk & Vanthienen, Jan & Dedene, Guido, 2002. "Bayesian neural network learning for repeat purchase modelling in direct marketing," European Journal of Operational Research, Elsevier, vol. 138(1), pages 191-211, April.
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