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A Hidden Markov Model of Customer Relationship Dynamics

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Author Info

  • Oded Netzer

    ()
    (Graduate School of Business, Columbia University, New York, New York 10027)

  • James M. Lattin

    ()
    (Graduate School of Business, Stanford University, Stanford, California 94305)

  • V. Srinivasan

    ()
    (Graduate School of Business, Stanford University, Stanford, California 94305)

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    Abstract

    This research models the dynamics of customer relationships using typical transaction data. Our proposed model permits not only capturing the dynamics of customer relationships, but also incorporating the effect of the sequence of customer-firm encounters on the dynamics of customer relationships and the subsequent buying behavior. Our approach to modeling relationship dynamics is structurally different from existing approaches. Specifically, we construct and estimate a nonhomogeneous hidden Markov model to model the transitions among latent relationship states and effects on buying behavior. In the proposed model, the transitions between the states are a function of time-varying covariates such as customer-firm encounters that could have an enduring impact by shifting the customer to a different (unobservable) relationship state. The proposed model enables marketers to dynamically segment their customer base and to examine methods by which the firm can alter 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 data set. Using the proposed model, we probabilistically classify the alumni base into three relationship states and estimate the effect of alumni-university interactions, such as reunions, on the movement of alumni between these states. Additionally, we demonstrate improved prediction ability on a hold-out sample.

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    File URL: http://dx.doi.org/10.1287/mksc.1070.0294
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    Bibliographic Info

    Article provided by INFORMS in its journal Marketing Science.

    Volume (Year): 27 (2008)
    Issue (Month): 2 (03-04)
    Pages: 185-204

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    Handle: RePEc:inm:ormksc:v:27:y:2008:i:2:p:185-204

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    Related research

    Keywords: customer relationship management; hidden Markov models; dynamic choice models; segmentation; Bayesian analysis;

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    Cited by:
    1. Asim Ansari & Ricardo Montoya & Oded Netzer, 2012. "Dynamic learning in behavioral games: A hidden Markov mixture of experts approach," Quantitative Marketing and Economics, Springer, vol. 10(4), pages 475-503, December.
    2. Jason Shachat & Lijia Wei, 2012. "Procuring Commodities: First-Price Sealed-Bid or English Auctions?," Marketing Science, INFORMS, vol. 31(2), pages 317-333, March.
    3. repec:wyi:wpaper:002025 is not listed on IDEAS
    4. Da Huo, 2013. "Cluster Analysis of Market Potential in Emerging Markets: A Dynamic Research based on Markov Chain," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 218-231, December.
    5. Anthony Heyes & Sandeep Kapur, 2012. "Community Pressure for Green Behaviour," Birkbeck Working Papers in Economics and Finance 1207, Birkbeck, Department of Economics, Mathematics & Statistics.
    6. Shaohui Ma & Joachim Büschken, 2011. "Counting your customers from an “always a share” perspective," Marketing Letters, Springer, vol. 22(3), pages 243-257, September.
    7. Durango-Cohen, Elizabeth J., 2013. "Modeling contribution behavior in fundraising: Segmentation analysis for a public broadcasting station," European Journal of Operational Research, Elsevier, vol. 227(3), pages 538-551.
    8. Peter Ebbes & Rajdeep Grewal & Wayne DeSarbo, 2010. "Modeling strategic group dynamics: A hidden Markov approach," Quantitative Marketing and Economics, Springer, vol. 8(2), pages 241-274, June.

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