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Choice Models and Customer Relationship Management


  • Wagner Kamakura


  • Carl Mela
  • Asim Ansari
  • Anand Bodapati
  • Pete Fader
  • Raghuram Iyengar
  • Prasad Naik
  • Scott Neslin
  • Baohong Sun
  • Peter Verhoef
  • Michel Wedel
  • Ron Wilcox


Customer relationship management (CRM) typically involves tracking individual customer behavior over time, and using this knowledge to configure solutions precisely tailored to the customers' and vendors' needs. In the context of choice, this implies designing longitudinal models of choice over the breadth of the firm's products and using them prescriptively to increase the revenues from customers over their lifecycle. Several factors have recently contributed to the rise in the use of CRM in the marketplace A shift in focus in many organizations, towards increasing the share of requirements among their current customers rather than fighting for new customers. An explosion in data acquired about customers, through the integration of internal databases and acquisition of external syndicated data. Computing power is increasing exponentially. Software and tools are being developed to exploit these data and computers, bringing the analytical tools to the decision maker, rather than restricting their access to analysts. In spite of this growth in marketing practice, CRM research in academia remains nascent. This paper provides a framework for CRM research and describes recent advances as well as key research opportunities. See for a more complete version of this paper Copyright Springer Science + Business Media, Inc. 2005

Suggested Citation

  • Wagner Kamakura & Carl Mela & Asim Ansari & Anand Bodapati & Pete Fader & Raghuram Iyengar & Prasad Naik & Scott Neslin & Baohong Sun & Peter Verhoef & Michel Wedel & Ron Wilcox, 2005. "Choice Models and Customer Relationship Management," Marketing Letters, Springer, vol. 16(3), pages 279-291, December.
  • Handle: RePEc:kap:mktlet:v:16:y:2005:i:3:p:279-291
    DOI: 10.1007/s11002-005-5892-2

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    1. Mercedes Esteban-Bravo & Jose M. Vidal-Sanz & Gökhan Yildirim, 2014. "Valuing Customer Portfolios with Endogenous Mass and Direct Marketing Interventions Using a Stochastic Dynamic Programming Decomposition," Marketing Science, INFORMS, vol. 33(5), pages 621-640, September.
    2. J. D’Haen & D. Van Den Poel, 2013. "Model-supported business-to-business prospect prediction based on an iterative customer acquisition framework," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/863, Ghent University, Faculty of Economics and Business Administration.
    3. Vidal-Sanz, Jose M. & Mela, Carl F. & Gupta, Sunil, 2009. "The value of a "free" customer," DEE - Working Papers. Business Economics. WB wb092903, Universidad Carlos III de Madrid. Departamento de Economía de la Empresa.
    4. Bas Donkers & Peter Verhoef & Martijn Jong, 2007. "Modeling CLV: A test of competing models in the insurance industry," Quantitative Marketing and Economics (QME), Springer, vol. 5(2), pages 163-190, June.
    5. Sander Triest & Maurice Bun & Erik Raaij & Maarten Vernooij, 2009. "The impact of customer-specific marketing expenses on customer retention and customer profitability," Marketing Letters, Springer, vol. 20(2), pages 125-138, June.
    6. Mizuno, Makoto & Saji, Akira & Sumita, Ushio & Suzuki, Hideo, 2008. "Optimal threshold analysis of segmentation methods for identifying target customers," European Journal of Operational Research, Elsevier, vol. 186(1), pages 358-379, April.
    7. Yi Qian & Hui Xie, 2014. "Which Brand Purchasers Are Lost to Counterfeiters? An Application of New Data Fusion Approaches," Marketing Science, INFORMS, vol. 33(3), pages 437-448, May.
    8. Andrés Musalem & Yogesh V. Joshi, 2009. "—How Much Should You Invest in Each Customer Relationship? A Competitive Strategic Approach," Marketing Science, INFORMS, vol. 28(3), pages 555-565, 05-06.
    9. P. Baecke & D. Van Den Poel, 2012. "Including Spatial Interdependence in Customer Acquisition Models: a Cross-Category Comparison," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/788, Ghent University, Faculty of Economics and Business Administration.
    10. P. Baecke & D. Van Den Poel, 2010. "Improving purchasing behavior predictions by data augmentation with situational variables," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 10/658, Ghent University, Faculty of Economics and Business Administration.
    11. V Kumar & Amalesh Sharma & Shaphali Gupta, 2017. "Accessing the influence of strategic marketing research on generating impact: moderating roles of models, journals, and estimation approaches," Journal of the Academy of Marketing Science, Springer, vol. 45(2), pages 164-185, March.
    12. Evanschitzky, Heiner & Malhotra, Neeru & Wangenheim, Florian v. & Lemon, Katherine N., 2017. "Antecedents of peripheral services cross-buying behavior," Journal of Retailing and Consumer Services, Elsevier, vol. 36(C), pages 218-224.
    13. Tarek Ben Rhouma & Georges Zaccour, 2018. "Optimal Marketing Strategies for the Acquisition and Retention of Service Subscriber," Management Science, INFORMS, vol. 64(6), pages 2609-2627, June.
    14. Song Yao & Carl F. Mela, 2008. "Online Auction Demand," Marketing Science, INFORMS, vol. 27(5), pages 861-885, 09-10.
    15. P. Baecke & D. Van Den Poel, 2012. "Improving Customer Acquisition Models by Incorporating Spatial Autocorrelation at Different Levels of Granularity," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 12/819, Ghent University, Faculty of Economics and Business Administration.


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