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A Hidden Markov Model of Customer Relationship Dynamics

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Author Info
Netzer, Oded (Columbia U)
Lattin, James M. (Stanford U)
Srinivasan, V. "Seenu"
Abstract

This 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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Paper provided by Stanford University, Graduate School of Business in its series Research Papers with number 1904r.

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Date of creation: May 2007
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Handle: RePEc:ecl:stabus:1904r

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  1. 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. [Downloadable!] (restricted)
  2. Dekimpe, M.G. & Franses, Ph.H.B.F. & Hanssens, D.M. & Naik, P., 2006. "Time-Series Models in Marketing," Research Paper ERS-2006-049-MKT Revision, 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 Uni. [Downloadable!]
  3. Baade, Robert A. & Sundberg, Jeffrey O., 1996. "What determines alumni generosity?," Economics of Education Review, Elsevier, vol. 15(1), pages 75-81, February. [Downloadable!] (restricted)
  4. 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. [Downloadable!] (restricted)
  5. Ulf Böckenholt & Rolf Langeheine, 1996. "Latent change in recurrent choice data," Psychometrika, Springer, vol. 61(2), pages 285-301, June. [Downloadable!] (restricted)
  6. 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.
  7. 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. [Downloadable!] (restricted)
  8. Fournier, Susan, 1998. " Consumers and Their Brands: Developing Relationship Theory in Consumer Research," Journal of Consumer Research: An Interdisciplinary Quarterly, University of Chicago Press, vol. 24(4), pages 343-73, March.
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