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Investigating the role of product features in preventing customer churn, by using survival analysis and choice modeling: The case of financial services

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  • B. LARIVIÈRE
  • D. VAN DEN POEL

Abstract

The enhancement of existing relationships is of pivotal importance to companies, since attracting new customers is known to be more expensive. Therefore, as part of their customer relationship management (CRM) strategy, many researchers have been analyzing “why” customers decide to switch. However, despite its practical relevance, few studies have investigated how companies can react to defection prone customers by offering the right set of products. Additionally, within the current customer attention “hype”, one tends to overlook the nature of different products when investigating customer defection. In this research, we study the defection of the savings and investment (SI) customers of a large Belgian financial service provider. We created different SI churn behavior categories by introducing two dimensions: (i) duration of the products (fixed term versus infinity) and (ii) capital/revenue risks involved. Considering these product features, we first gain explorative insight in the timing of the churn event by means of Kaplan-Meier estimates. Secondly, we elaborate on the most alarming group of customers that emerged from the former explorative analysis. A hazard model is built to detect the most convenient product categories to cross-sell in order to reduce their churn likelihood. Complementary, a multinomial probit model is estimated to explore the customers’ preferences with respect to the product features involved and to test whether these correspond with the findings of the survival analysis. The results of our study indicate that customer retention cannot be understood by solely relying on customer characteristics. In sum, it might be true that “not all customers are created equal”, but neither are all products.

Suggested Citation

  • 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.
  • Handle: RePEc:rug:rugwps:04/223
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    References listed on IDEAS

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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. Asamoah, Kwadwo, 2016. "On the credibility of insurance claim frequency: Generalized count models and parametric estimators," Insurance: Mathematics and Economics, Elsevier, vol. 70(C), pages 339-353.
    3. E Lima & C Mues & B Baesens, 2009. "Domain knowledge integration in data mining using decision tables: case studies in churn prediction," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(8), pages 1096-1106, August.
    4. Ali Dehghan & Theodore Trafalis, 2012. "Examining Churn and Loyalty Using Support Vector Machine," Business and Management Research, Business and Management Research, Sciedu Press, vol. 1(4), pages 153-161, December.
    5. Gattermann-Itschert, Theresa & Thonemann, Ulrich W., 2021. "How training on multiple time slices improves performance in churn prediction," European Journal of Operational Research, Elsevier, vol. 295(2), pages 664-674.
    6. Slãvescu Ecaterina Oana & Panait Iulian, 2012. "Improving Customer Churn Models as one of Customer Relationship Management Business Solutions for the Telecommunication Industry," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, vol. 0(1), pages 1156-1160, May.
    7. J. Burez & D. Van Den Poel, 2008. "Handling class imbalance in customer churn prediction," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 08/517, Ghent University, Faculty of Economics and Business Administration.
    8. Vera Miguéis & Dirk Poel & Ana Camanho & João Falcão e Cunha, 2012. "Predicting partial customer churn using Markov for discrimination for modeling first purchase sequences," 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. 6(4), pages 337-353, December.
    9. 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.
    10. J. Burez & D. Van Den Poel, 2005. "CRM at a Pay-TV Company: Using Analytical Models to Reduce Customer Attrition by Targeted Marketing for Subscription Services," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/348, Ghent University, Faculty of Economics and Business Administration.
    11. Alvaro Arroyo & Alvaro Cartea & Fernando Moreno-Pino & Stefan Zohren, 2023. "Deep Attentive Survival Analysis in Limit Order Books: Estimating Fill Probabilities with Convolutional-Transformers," Papers 2306.05479, arXiv.org.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. Atakan Yalcin & Lerzan Aksoy & Timothy L. Keiningham & Bart Larivière & Sunil Mithas & Forrest V. Morgeson III, 2012. "The Satisfaction, Repurchase Intention and Shareholder Value Linkage: A Longitudinal Examination of Fixed and Firm Specific Effects," EcoMod2012 4543, EcoMod.
    17. Liu, Meijun & Hu, Xiao & Wang, Yuandi & Shi, Dongbo, 2018. "Survive or perish: Investigating the life cycle of academic journals from 1950 to 2013 using survival analysis methods," Journal of Informetrics, Elsevier, vol. 12(1), pages 344-364.

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