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Predicting Customer Potential Value: an application in the insurance industry

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Listed:
  • Verhoef, P.C.
  • Donkers, A.C.D.

Abstract

For effective Customer Relationship Management (CRM), it is essential to have information on the potential value of customers. Based on the interplay between potential value and realized value, managers can devise customer specific strategies. In this article we introduce a model for predicting the potential value of a current customer. Furthermore, we discuss and apply different modeling strategies for predicting this potential value.

Suggested Citation

  • Verhoef, P.C. & Donkers, A.C.D., 2001. "Predicting Customer Potential Value: an application in the insurance industry," ERIM Report Series Research in Management ERS-2001-01-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    • Handle: RePEc:ems:eureri:67
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    References listed on IDEAS

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    Cited by:

    1. Montserrat Guillén & Ana María Pérez-Marín & Montserrat Guillén, 2011. "A logistic regression approach to estimating customer profit loss due to lapses in insurance," Working Papers XREAP2011-13, Xarxa de Referència en Economia Aplicada (XREAP), revised Oct 2011.
    2. Álvaro Julio Cuadros & Victoria Eugenia Domínguez, 2014. "Customer segmentation model based on value generation for marketing strategies formulation," Estudios Gerenciales, Universidad Icesi, March.
    3. R. Ferrentino & M. T. Cuomo & C. Boniello, 2016. "On the customer lifetime value: a mathematical perspective," Computational Management Science, Springer, vol. 13(4), pages 521-539, October.
    4. Neha GUPTA, 2018. "Influence of Demographics on Employees’ Perception for Cross-Selling and Up-Selling of eBanking Services," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 1, pages 54-60.
    5. Hamidreza Koosha & Amir Albadvi, 2020. "Allocation of marketing budgets to maximize customer equity," Operational Research, Springer, vol. 20(2), pages 561-583, June.
    6. Yang Wang & Shengguo Gao & Xiaoqi Sheng, 2014. "Enterprise Customer Life-Cycle Value Model and Applied Research," Journal of Business Administration Research, Journal of Business Administration Research, Sciedu Press, vol. 3(2), pages 68-73, October.
    7. Sunčica Rogić & Ljiljana Kašćelan & Vladimir Kašćelan & Vladimir Đurišić, 2022. "Automatic customer targeting: a data mining solution to the problem of asymmetric profitability distribution," Information Technology and Management, Springer, vol. 23(4), pages 315-333, December.
    8. Peter C. Verhoef & Martin Heijnsbroek & Joost Bosma, 2017. "Developing A Service Improvement System for the National Dutch Railways," Interfaces, INFORMS, vol. 47(6), pages 489-504, December.
    9. Alex R. Zablah & Danny N. Bellenger & Detmar W. Straub & Wesley J. Johnston, 2012. "Performance Implications of CRM Technology Use: A Multilevel Field Study of Business Customers and Their Providers in the Telecommunications Industry," Information Systems Research, INFORMS, vol. 23(2), pages 418-435, June.
    10. Daniele Durante & Sally Paganin & Bruno Scarpa & David B. Dunson, 2017. "Bayesian modelling of networks in complex business intelligence problems," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 66(3), pages 555-580, April.
    11. Boucher, Jean-Philippe & Couture-Piché, Guillaume, 2015. "Modeling the number of insureds’ cars using queuing theory," Insurance: Mathematics and Economics, Elsevier, vol. 64(C), pages 67-76.
    12. 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.
    13. Lesscher, Lisan & Lobschat, Lara & Verhoef, Peter C., 2021. "Do offline and online go hand in hand? Cross-channel and synergy effects of direct mailing and display advertising," International Journal of Research in Marketing, Elsevier, vol. 38(3), pages 678-697.
    14. Enrico Baraldi & Antonella La Rocca & Andrea Perna, 2013. "Intra- and inter-organizational effects of a CRM system implementation," MERCATI & COMPETITIVIT?, FrancoAngeli Editore, vol. 2013(1), pages 13-34.
    15. Arthur J. Lin & Hai-Yen Chang & Sun-Weng Huang & Gwo-Hshiung Tzeng, 2021. "Criteria affecting Taiwan wealth management banks in serving high-net-worth individuals during COVID-19: a DEMATEL approach," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 26(4), pages 274-294, December.
    16. L C Thomas, 2010. "Consumer finance: challenges for operational research," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(1), pages 41-52, January.
    17. Xiangbin Yan & Jing Wang & Michael Chau, 2015. "Customer revisit intention to restaurants: Evidence from online reviews," Information Systems Frontiers, Springer, vol. 17(3), pages 645-657, June.
    18. Arno de Caigny & Kristof Coussement & Koen de Bock, 2020. "Leveraging fine-grained transaction data for customer life event predictions," Post-Print hal-02507998, HAL.
    19. Malcolm Smith & Chen Chang, 2010. "Improving customer outcomes through the implementation of customer relationship management," Asian Review of Accounting, Emerald Group Publishing Limited, vol. 18(3), pages 260-285, September.

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    More about this item

    Keywords

    customer potential; customer relationship management; insurance industry; marketing models;
    All these keywords.

    JEL classification:

    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • M - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing

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