Improving Customer Retention In Financial Services Using Kinship Network Information
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.
|Date of creation:||May 2012|
|Date of revision:|
|Contact details of provider:|| Postal: Hoveniersberg 4, B-9000 Gent|
Phone: ++ 32 (0) 9 264 34 61
Fax: ++ 32 (0) 9 264 35 92
Web page: http://www.ugent.be/eb
More information through EDIRC
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- Coussement, Kristof & Benoit, Dries Frederik & Van den Poel, Dirk, 2009.
"Improved Marketing Decision Making in a Customer Churn Prediction Context Using Generalized Additive Models,"
2009/18, Hogeschool-Universiteit Brussel, Faculteit Economie en Management.
- 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.
- 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.
- 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.
- K. Coussement & D.F. Benoît & D. Van den Poel, 2010. "Improved marketing decision making in a customer churn prediction context using generalized additive models," Post-Print halshs-00581701, HAL.
- 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.
- Charles F. Manski, 2000.
"Economic Analysis of Social Interactions,"
Journal of Economic Perspectives,
American Economic Association, vol. 14(3), pages 115-136, Summer.
- D. Van Den Poel & B. Larivière, 2003.
"Customer Attrition Analysis For Financial Services Using Proportional Hazard Models,"
Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium
03/164, Ghent University, Faculty of Economics and Business Administration.
- 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.
- 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.
- Childers, Terry L & Rao, Akshay R, 1992. " The Influence of Familial and Peer-Based Reference Groups on Consumer Decisions," Journal of Consumer Research, Oxford University Press, vol. 19(2), pages 198-211, September.
- 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.
- 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.
- Peppard, Joe, 2000. "Customer Relationship Management (CRM) in financial services," European Management Journal, Elsevier, vol. 18(3), pages 312-327, June.
When requesting a correction, please mention this item's handle: RePEc:rug:rugwps:12/786. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Nathalie Verhaeghe)
If references are entirely missing, you can add them using this form.