A Hidden Markov Model of Customer Relationship Dynamics
AbstractThis research models the dynamics of customer relationships using typical transaction data. It permits the evaluation of the effectiveness of customer-brand encounters on the dynamics of customer relationships and the subsequent buying behavior. Our approach to modeling relationship dynamics is structurally different from existing approaches. In the proposed model, customer-brand encounters may have an enduring impact by shifting the customer to a different (unobservable) relationship state. We constructed and estimated a hidden Markov model (HMM) to model the transitions among latent relationship states and effects on buying behavior. This model enables to dynamically segment the firm's customer base, and to examine methods by which the firm can alter the long-term buying behavior. We use a hierarchical Bayes approach to capture the unobserved heterogeneity across customers. We calibrate the model in the context of alumni relations using a longitudinal gift-giving dataset. Using the proposed model, we are able to probabilistically classify the alumni base into three relationship states, and estimate the marginal impact of alumni-university interactions on moving the alumni between these states. The application of the model for marketing decisions is illustrated using a "what-if" analysis of a reunion marketing campaign. Additionally, we demonstrate improved prediction ability on a validation sample.
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Bibliographic InfoPaper provided by Stanford University, Graduate School of Business in its series Research Papers with number 1904r.
Date of creation: May 2007
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- Baade, Robert A. & Sundberg, Jeffrey O., 1996. "What determines alumni generosity?," Economics of Education Review, Elsevier, vol. 15(1), pages 75-81, February.
- Sunil Gupta & Valarie Zeithaml, 2006. "Customer Metrics and Their Impact on Financial Performance," Marketing Science, INFORMS, vol. 25(6), pages 718-739, 11-12.
- Keane, Michael P, 1997. "Modeling Heterogeneity and State Dependence in Consumer Choice Behavior," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(3), pages 310-27, July.
- Neil A. Morgan & Lopo Leotte Rego, 2006. "The Value of Different Customer Satisfaction and Loyalty Metrics in Predicting Business Performance," Marketing Science, INFORMS, vol. 25(5), pages 426-439, September.
- Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-84, March.
- Peter M. Guadagni & John D. C. Little, 1983. "A Logit Model of Brand Choice Calibrated on Scanner Data," Marketing Science, INFORMS, vol. 2(3), pages 203-238.
- Jan Roelf Bult & Tom Wansbeek, 1995. "Optimal Selection for Direct Mail," Marketing Science, INFORMS, vol. 14(4), pages 378-394.
- Koen Pauwels & Imran Currim & Marnik Dekimpe & Dominique Hanssens & Natalie Mizik & Eric Ghysels & Prasad Naik, 2004. "Modeling Marketing Dynamics by Time Series Econometrics," Marketing Letters, Springer, vol. 15(4), pages 167-183, December.
- Randolph E. Bucklin & James M. Lattin, 1991. "A Two-State Model of Purchase Incidence and Brand Choice," Marketing Science, INFORMS, vol. 10(1), pages 24-39.
- Ruth N. Bolton, 1998. "A Dynamic Model of the Duration of the Customer's Relationship with a Continuous Service Provider: The Role of Satisfaction," Marketing Science, INFORMS, vol. 17(1), pages 45-65.
- Scott S. L., 2002. "Bayesian Methods for Hidden Markov Models: Recursive Computing in the 21st Century," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 337-351, March.
- Peter S. Fader & James M. Lattin, 1993. "Accounting for Heterogeneity and Nonstationarity in a Cross-Sectional Model of Consumer Purchase Behavior," Marketing Science, INFORMS, vol. 12(3), pages 304-317.
- Prasad A. Naik & Murali K. Mantrala & Alan G. Sawyer, 1998. "Planning Media Schedules in the Presence of Dynamic Advertising Quality," Marketing Science, INFORMS, vol. 17(3), pages 214-235.
- Fournier, Susan, 1998. " Consumers and Their Brands: Developing Relationship Theory in Consumer Research," Journal of Consumer Research, University of Chicago Press, vol. 24(4), pages 343-73, March.
- David C. Schmittlein & Robert A. Peterson, 1994. "Customer Base Analysis: An Industrial Purchase Process Application," Marketing Science, INFORMS, vol. 13(1), pages 41-67.
- Ulf Böckenholt & Rolf Langeheine, 1996. "Latent change in recurrent choice data," Psychometrika, Springer, vol. 61(2), pages 285-301, June.
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