Marketing Letters

, Volume 16, Issue 3–4, pp 279–291 | Cite as

Choice Models and Customer Relationship Management

  • Wagner KamakuraEmail author
  • Carl F. Mela
  • Asim Ansari
  • Anand Bodapati
  • Pete Fader
  • Raghuram Iyengar
  • Prasad Naik
  • Scott Neslin
  • Baohong Sun
  • Peter C. 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


customer relationship management direct marketing 


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  1. Adiguzel, Feray and Michel Wedel. (2004). “The Design of Split Questionnaires,” Working Paper, University of Michigan.Google Scholar
  2. Anderson, Eric T. and Duncan Simester. (2004). “Long-Run Effects of Promotion Depth on New Versus Established Customers: Three Field Studies,” Marketing Science 23(1), 4–20.CrossRefGoogle Scholar
  3. Ansari, A., S. Essegaier, and R. Kohli. (2000). “Internet Recommendation Systems,” Journal of Marketing Research, 37, 363–375.CrossRefGoogle Scholar
  4. Ansari, Asim and Carl F. Mela. (2003). “E-Customization,” Journal of Marketing Research 40(2), 131–145.CrossRefGoogle Scholar
  5. Ansari, Asim, Carl Mela, and Scott A. Neslin. (2004). “Customer Channel Migration,” Working Paper, Columbia University School of Business, NY.Google Scholar
  6. Balasubramanian, S., S. Gupta, W. Kamakura, and M. Wedel. (1998). “Modeling Large Data Sets in Marketing,” Statistica Neerlandica 52, 303–323.CrossRefGoogle Scholar
  7. Bitran, Gabriel R. and Susana V. Mondschein. (1996). “Mailing Decisions in the Catalog Industry,” Management Science 42(9), 1364–1381.Google Scholar
  8. Biyalogorsky, Eyal and Prasad Naik. (2003). “Clicks and Mortar: The Effect of Online Activities on Offline Sales,” Marketing Letters 14(1), 21–32.CrossRefGoogle Scholar
  9. Blattberg, Robert C. and John Deighton. (1991). “Interactive Marketing: Exploiting the Age of Addressability,” Sloan Management Review 33(Fall), 5–14.Google Scholar
  10. Blattberg, Robert C., Gary Getz, and Jacquelyn S. Thomas. (2001). Customer Equity: Building and Managing Relationships as Valuable Assets. Boston, MA: Harvard Business School Press.Google Scholar
  11. Blattberg, Robert C., Kim and Scott A. Neslin. (2004). Database Marketing (forthcoming).Google Scholar
  12. Bodapati, A. V. (2004). “Recommendation Systems,” Working Paper, Anderson School of Management, UCLA.Google Scholar
  13. Bolton, Ruth, P. K. Kannan, and Matthew Bramlett. (2000). “Implications of Loyalty Program Membership and Service Experiences for Customer Retention and Value,” Journal of the Academy of Marketing Science 28(1), 95–108.Google Scholar
  14. Bolton, Ruth N., Katherine N. Lemon, and Peter C. Verhoef. (2004). “The Theoretical Underpinnings of Customer Asset Management: A Framework and Propositions for Future Research,” Journal of the Academy of Marketing Science 32(3), 271–292.CrossRefGoogle Scholar
  15. Breese J. S., D. Heckerman, and C. Kadie. (1998). “Empirical Analysis of Predictive Algorithms for Collaborative Filtering,” In G. F. Cooper and S. Moral (eds.), Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, pp. 43–52, Morgan Kaufmann.Google Scholar
  16. Bas Donkers, Philip Hans Franses, and Peter C. Verhoef. (2003). “Selective Sampling for Binary Choice Models,” Journal of Marketing Research 40(4), 492–497.Google Scholar
  17. Dowling, Grahame R. and Mark Uncles. (1997). “Do Customer Loyalty Programs Really Work?” Sloan Management Review 38(4), 71–82.Google Scholar
  18. Du, Rex and Wagner A. Kamakura. (2005). “Household Lifecycles and Life Styles in America,” Journal of Marketing Research, (forthcoming).Google Scholar
  19. Du, Rex, Wagner A. Kamakura, and Carl F. Mela. (2005). “Customers' Share of Category Requirements,” Working Paper, Duke University Marketing Department.Google Scholar
  20. Fader, P. S., B. G. S. Hardie, and K. L. Lee. (2004). “Counting Your Customers' the Easy Way: An Alternative to the Pareto/NBD Model,” Working Paper, Wharton Marketing Department.Google Scholar
  21. Franses, Philip, H. (2005). “On the Use of Econometric Models for Policy Simulation in Marketing,” Journal of Marketing Research (42), 4–14.Google Scholar
  22. Gilula, Zvi, Robert E. McCulloch, and Peter E. Rossi. (2004). “A Direct Approach to Data Fusion,” Working Paper, University of Chicago.Google Scholar
  23. Gupta, Sunil, Donald R. Lehmann, and Jennifer Ames Stuart. (2004). “Valuing Customers,” Journal of Marketing Research 41(1), 7–18.CrossRefGoogle Scholar
  24. Hastie, T., R. Tibshirani, and J. Friedman. (2001). The Elements of Statistical Learning: Data Mining, Inference and Prediction. New York: Springer-Verlag.Google Scholar
  25. Iacobucci, D., P. Arabie, and A. V. Bodapati. (2000). “Recommendation Agents on the Internet,” Journal of Interactive Marketing 14(3).Google Scholar
  26. Jain, D. and S. S. Singh. (2002). “Customer Lifetime Value Research in Marketing: A Review and Future Directions,” Journal of Interactive Marketing 16(Spring), 34–46.Google Scholar
  27. Kamakura, Wagner A., S. Ramaswami, and R. Srivastava. (1991). “Applying Latent Trait Analysis in the Evaluation of Prospects for Cross-Selling of Financial Services,” International Journal of Research in Marketing 8, 329–349.CrossRefGoogle Scholar
  28. Kamakura, Wagner A. and Michel Wedel. (1997). “Statistical Datafusion for Cross-Tabulation,” Journal of Marketing Research 34(November), 485–498.Google Scholar
  29. Kamakura, Wagner A. and Michel Wedel. (2000). “Factor Analysis and Missing Data,” Journal of Marketing Research 37, 490–498.CrossRefGoogle Scholar
  30. Kamakura, Wagner A. and Michel Wedel. (2003). “List Augmentation with Model Based Multiple Imputation: A Case Study Using a Mixed-Outcome Factor Model,” Statistica Neerlandica 57(1), 46–57.CrossRefGoogle Scholar
  31. Kamakura, Wagner A, Michel Wedel, Fernando de Rosa, and Jose A. Mazzon. (2003). “Cross-Selling Through Database Marketing: A Mixed Data Factor Analyzer for Data Augmentation and Prediction,” International Journal of Research in Marketing 20, 45–65.CrossRefGoogle Scholar
  32. King, Gary and Langche Zeng. (2001). “Logistic Regression in Rare Events Data,” Political Analysis 9(2), 137–163.Google Scholar
  33. Kivetz, Ran. (2003). “The Effects of Effort and Intrinsic Motivation on Risky Choice,” Marketing Science 22, 477–502.CrossRefGoogle Scholar
  34. Kivetz, R. and I. Simonson. (2002). “Earning the Right to Indulge: Effort as a Determinant of Customer Preferences Toward Frequency Program Rewards,” Journal of Marketing Research 39(2), 155–170.CrossRefGoogle Scholar
  35. Kivetz, Ran, Oleg Urminsky, and Yuhuang Zheng. (2004). “Goal-Motivated Purchase Acceleration:Evidence and Consequences in Reward Programs,” Working Paper Columbia University.Google Scholar
  36. Knott, Aaron, Andrew Hayes, and Scott A. Neslin. (2002). “Nest-Product-to-Buy Models for Cross-Selling Applications,” Journal of Interactive Marketing 16(3), 59–75.CrossRefGoogle Scholar
  37. Kopalle and Neslin. (2003). “The Economic Viability of Frequency Reward Programs in a Strategic Competitive Environment,” Review of Marketing Science Vol. 1, Article 1.Google Scholar
  38. Lal, Rajiv andDavid E. Bell. (2003). “The Impact of Frequent Shopper Programs in Grocery Retailing,” Quantitative Marketing and Economics 1(2), 179–202.CrossRefGoogle Scholar
  39. Leenheer, Jorna, Tammo H. A. Bijmolt, Harald J. van Heerde, and Ale Smidts. (2004). “Do Loyalty Programs Enhance Behavioral Loyalty? A Market-Wide Analysis Accounting for Endogeneity,” Working Paper, Tilburg University.Google Scholar
  40. Lemmens, Aurélie and Christophe Croux. (2003). “Bagging and Boosting Classification Trees to Predict Churn,” Working Paper, Teradata center.Google Scholar
  41. Lewis, Michael. (2003). “Customer Lifetime Value as a Function of Acquisition Discount: An Analysis of Promotionally and Regularly Acquired Customers,” Working Paper, University of Florida.Google Scholar
  42. Li, K. C. (1991). “Sliced Inverse Regression for Dimension Reduction, With Discussions,” Journal of the American Statistical Association 86, 316–342.Google Scholar
  43. Li, Shibo, Baohong Sun, and Rong Zhou. (2004). “Learning and Optimal Matching of Customers and ServiceProviders,” Working Paper, Carnegie Mellon University.Google Scholar
  44. Li, Shibo, Baohong Sun, and Ron Wilcox. (2004). “Cross-selling Naturally Ordered Products: An Application to Consumer Banking Services,” Working Paper, U. Virginia.Google Scholar
  45. Mittal, Vikas and Wagner A. Kamakura. (2001). “Satisfaction, Repurchase Intent and Repurchase Behavior: Investigating the Moderating Effect of Customer Characteristics,” Journal of Marketing Research 38(February), 131–142.Google Scholar
  46. Naik, Prasad A. and Chih-Ling Tsai. (2001). “Single-Index Model Selections,” Biometrika 88(3), 821–832.Google Scholar
  47. Naik, Prasad A. and Chih-Ling Tsai. (2004). “Residual Information Criterion for Single-Index Model Selections,” Journal of Nonparametric Statistics 16(1–2), 187–197.Google Scholar
  48. Naik, Prasad A. and Chih-Ling Tsai. (2004). “Isotonic Single-Index Model for Database Marketing,” Computational Statistics and Data Analysis, 47, 775–790.Google Scholar
  49. Naik, Prasad A. and Chih-Ling Tsai. (2005). “Constrained Inverse Regression for Incorporating Prior Information,” Journal of the American Statistical Association 100(469), 204–211.Google Scholar
  50. Neslin, S, S. Gupta, W. Kamakura, J. Lu, and C. Mason. (2004). “Defection Detection: Improving Predictive Accuracy of Customer Churn Models,” Working Paper, Teradata Center at Duke University.Google Scholar
  51. Rassler, Suzanne. (2002). Statistical Matching. New York: Springer.Google Scholar
  52. Reinartz, W. J. and V. Kumar. (2003). “Customer Lifetime Duration: An Empirical Framework for Measurement and Explanation,” Journal of Marketing 67(January), 77–99.Google Scholar
  53. Rust, Roland and Anthony Zahorik. (1993). “Customer Satisfaction, Customer Retention, and Market Share,” Journal of Retailing 69(2).Google Scholar
  54. Schmittlein,David, Donald Morrison, and Richard Colombo. (1987). “Counting Your Customers: Who Are They and What Will They Do Next?” Management Science 33(1).Google Scholar
  55. Sharp Byron and Anne Sharp. (1997). “Loyalty Programs and Their Impact on Repeat-Purchase Loyalty patterns,” International Journal of Research in Marketing 14, 473–86.CrossRefGoogle Scholar
  56. Shi, Mengze and Fusun Gonul. (1998). “Optimal Mailing of Catalogs: A New Methodology Using Estimable Structural Dynamic Programming Models,” Management Science 44(9), 1249–1262.Google Scholar
  57. Simester, Duncan, Peng Sun, and John N. Tsitsiklis. (2004). “Dynamic Catalog Mailing Policies,” Working Paper, MIT.Google Scholar
  58. Steenburg, Thomas J., Andrew Ainsle, and Peder Hans Engbretson. (2003). “Massively Categorical Variables, Revealing the Information in ZIP-Codes,” Marketing Science 22(1), 40–57.Google Scholar
  59. Thomas, J. S. (2001). “A Methodology for Linking Customer Acquisition to Customer Retention,” Journal of Marketing Research 38, 262–268.CrossRefGoogle Scholar
  60. Verhoef, Peter C. (2003). “Understanding the Effect of CRM Efforts on Customer Retention and Customer Share Development,” Journal of Marketing 67(4), 30–45CrossRefGoogle Scholar
  61. Venkatesan, R. and V. Kumar. (2003). “Using Customer Lifetime Value in Customer Selection and Resource Allocation,” MSI Report 03–112.Google Scholar
  62. Verhoef, Peter C. and Bas Donkers. (2005). “The Effect of Acquisition Channels on Customer Retention and Cross-Buying,” Journal of Interactive Marketing (forthcoming).Google Scholar
  63. Ying, Yuanping, Fred Feinberg, and Michel Wedel. (2004). “Improving Online Product Recommendations by Including Nonrated Items,” Working Paper, University of Michigan Business School.Google Scholar
  64. Zhang, Jie and Michel Wedel. (2004). “The Effectiveness of Customized Promotions in Online and Offline Stores,” Working Paper, University of Michigan Business School.Google Scholar

Copyright information

© Springer Science + Business Media, Inc. 2005

Authors and Affiliations

  • Wagner Kamakura
    • 1
    Email author
  • Carl F. Mela
    • 1
  • Asim Ansari
    • 2
  • Anand Bodapati
    • 3
  • Pete Fader
    • 4
  • Raghuram Iyengar
    • 2
  • Prasad Naik
    • 5
  • Scott Neslin
    • 6
  • Baohong Sun
    • 7
  • Peter C. Verhoef
    • 8
  • Michel Wedel
    • 9
  • Ron Wilcox
    • 10
  1. 1.Duke UniversityUSA
  2. 2.Columbia UniversityColumbia
  3. 3.University of California
  4. 4.University of Pennsylvania
  5. 5.University of CaliforniaDavis
  6. 6.Dartmouth CollegeDartmouth
  7. 7.Carnegie Mellon UniversityUSA
  8. 8.Erasmus University RotterdamRotterdam
  9. 9.University of Michigan
  10. 10.University of Virginia

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