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Improving Customer Retention In Financial Services Using Kinship Network Information

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  • D. F. BENOIT

    ()

  • D. VAN DEN POEL

    ()

Abstract

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.

Suggested Citation

  • 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.
  • Handle: RePEc:rug:rugwps:12/786
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    File URL: http://wps-feb.ugent.be/Papers/wp_12_786.pdf
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    References listed on IDEAS

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    3. Coussement, Kristof & Benoit, Dries Frederik & Van den Poel, Dirk, 2009. "Improved Marketing Decision Making in a Customer Churn Prediction Context Using Generalized Additive Models," Working Papers 2009/18, Hogeschool-Universiteit Brussel, Faculteit Economie en Management.
    4. Charles F. Manski, 2000. "Economic Analysis of Social Interactions," Journal of Economic Perspectives, American Economic Association, vol. 14(3), pages 115-136, Summer.
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    6. 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.
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    Cited by:

    1. M. Ballings & D. Van Den Poel & E. Verhagen, 2013. "Evaluating the Added Value of Pictorial Data for Customer Churn Prediction," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/869, Ghent University, Faculty of Economics and Business Administration.
    2. Ballings, Michel & Van den Poel, Dirk, 2015. "CRM in social media: Predicting increases in Facebook usage frequency," European Journal of Operational Research, Elsevier, vol. 244(1), pages 248-260.
    3. Gaurav Gupta & Himanshu Aggarwal, 2016. "Analysing customer responses to migrate strategies in making retailing and CRM effective," International Journal of Indian Culture and Business Management, Inderscience Enterprises Ltd, vol. 12(1), pages 92-127.
    4. repec:eee:ejores:v:267:y:2018:i:3:p:1141-1155 is not listed on IDEAS

    More about this item

    Keywords

    network based marketing; CRM; predictive analystics; social network analysis (SNA); kinship network; financial services; random forests;

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