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A Hidden Markov Model of Customer Relationship Dynamics

Author

Listed:
  • 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)

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.

Suggested Citation

  • Oded Netzer & James M. Lattin & V. Srinivasan, 2008. "A Hidden Markov Model of Customer Relationship Dynamics," Marketing Science, INFORMS, vol. 27(2), pages 185-204, 03-04.
  • Handle: RePEc:inm:ormksc:v:27:y:2008:i:2:p:185-204
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    File URL: http://dx.doi.org/10.1287/mksc.1070.0294
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    References listed on IDEAS

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    Cited by:

    1. John R. Hauser & Glen L. Urban & Guilherme Liberali & Michael Braun, 2009. "Website Morphing," Marketing Science, INFORMS, pages 202-223.
    2. Ricardo Montoya & Oded Netzer & Kamel Jedidi, 2010. "Dynamic Allocation of Pharmaceutical Detailing and Sampling for Long-Term Profitability," Marketing Science, INFORMS, pages 909-924.
    3. 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, pages 218-231.
    4. Peter S. Fader & Bruce G. S. Hardie & Jen Shang, 2010. "Customer-Base Analysis in a Discrete-Time Noncontractual Setting," Marketing Science, INFORMS, pages 1086-1108.
    5. Bolano, Danilo & Berchtold, André, 2016. "General framework and model building in the class of Hidden Mixture Transition Distribution models," Computational Statistics & Data Analysis, Elsevier, pages 131-145.
    6. James Agarwal & Wayne DeSarbo & Naresh K. Malhotra & Vithala Rao, 2015. "An Interdisciplinary Review of Research in Conjoint Analysis: Recent Developments and Directions for Future Research," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), pages 19-40.
    7. Asim Ansari & Ricardo Montoya & Oded Netzer, 2012. "Dynamic learning in behavioral games: A hidden Markov mixture of experts approach," Quantitative Marketing and Economics (QME), Springer, pages 475-503.
    8. Clemente-Císcar, M. & San Matías, S. & Giner-Bosch, V., 2014. "A methodology based on profitability criteria for defining the partial defection of customers in non-contractual settings," European Journal of Operational Research, Elsevier, vol. 239(1), pages 276-285.
    9. 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.
    10. Sungho Park & Sachin Gupta, 2011. "A Regime-Switching Model of Cyclical Category Buying," Marketing Science, INFORMS, pages 469-480.
    11. Shaohui Ma & Joachim Büschken, 2011. "Counting your customers from an “always a share” perspective," Marketing Letters, Springer, vol. 22(3), pages 243-257, September.
    12. Chenfeng Xiong & Xiqun Chen & Xiang He & Wei Guo & Lei Zhang, 2015. "The analysis of dynamic travel mode choice: a heterogeneous hidden Markov approach," Transportation, Springer, pages 985-1002.
    13. Jason Shachat & Lijia Wei, 2012. "Procuring Commodities: First-Price Sealed-Bid or English Auctions?," Marketing Science, INFORMS, pages 317-333.
    14. Jason Shachat & Lijia Wei, 2012. "Procuring Commodities: First-Price Sealed-Bid or English Auctions?," Marketing Science, INFORMS, pages 317-333.
    15. Peter Ebbes & Rajdeep Grewal & Wayne DeSarbo, 2010. "Modeling strategic group dynamics: A hidden Markov approach," Quantitative Marketing and Economics (QME), Springer, pages 241-274.
    16. Heyes, Anthony & Kapur, Sandeep, 2012. "Community pressure for green behavior," Journal of Environmental Economics and Management, Elsevier, pages 427-441.
    17. repec:eee:reensy:v:164:y:2017:i:c:p:84-97 is not listed on IDEAS
    18. Lemmens, A. & Croux, C. & Stremersch, S., 2012. "Dynamics in international market segmentation of new product growth," Other publications TiSEM 306086bd-670f-48d2-97d1-3, Tilburg University, School of Economics and Management.
    19. Yun-Ling Wu & Cheng-Huang Tung & Chun-Chang Lee, 2017. "The Power of a Leading Indicators Fluctuation Trend for Forecasting Taiwans Real Estate Business Cycle: An Application of a Hidden Markov Model," Asian Economic and Financial Review, Asian Economic and Social Society, pages 81-98.
    20. David A. Schweidel & Eric T. Bradlow & Peter S. Fader, 2011. "Portfolio Dynamics for Customers of a Multiservice Provider," Management Science, INFORMS, pages 471-486.
    21. Sha Yang & Yi Zhao & Ravi Dhar, 2010. "Modeling the Underreporting Bias in Panel Survey Data," Marketing Science, INFORMS, pages 525-539.
    22. V. Kumar & S. Sriram & Anita Luo & Pradeep K. Chintagunta, 2011. "Assessing the Effect of Marketing Investments in a Business Marketing Context," Marketing Science, INFORMS, pages 924-940.

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