IDEAS home Printed from https://ideas.repec.org/p/ecl/stabus/1904r.html
   My bibliography  Save this paper

A Hidden Markov Model of Customer Relationship Dynamics

Author

Listed:
  • 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.

Suggested Citation

  • Netzer, Oded & Lattin, James M. & Srinivasan, V. Seenu, 2007. "A Hidden Markov Model of Customer Relationship Dynamics," Research Papers 1904r, Stanford University, Graduate School of Business.
    • Handle: RePEc:ecl:stabus:1904r
    as

    Download full text from publisher

    File URL: http://gsbapps.stanford.edu/researchpapers/library/RP1904R.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Abel P. Jeuland & Frank M. Bass & Gordon P. Wright, 1980. "A Multibrand Stochastic Model Compounding Heterogeneous Erlang Timing and Multinomial Choice Processes," Operations Research, INFORMS, vol. 28(2), pages 255-277, April.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. David C. Schmittlein & Robert A. Peterson, 1994. "Customer Base Analysis: An Industrial Purchase Process Application," Marketing Science, INFORMS, vol. 13(1), pages 41-67.
    7. Peter E. Rossi & Greg M. Allenby, 2003. "Bayesian Statistics and Marketing," Marketing Science, INFORMS, vol. 22(3), pages 304-328, July.
    8. Ulf Böckenholt & Rolf Langeheine, 1996. "Latent change in recurrent choice data," Psychometrika, Springer;The Psychometric Society, vol. 61(2), pages 285-301, June.
    9. Peter S. Fader & Bruce G. S. Hardie & Chun-Yao Huang, 2004. "A Dynamic Changepoint Model for New Product Sales Forecasting," Marketing Science, INFORMS, vol. 23(1), pages 50-65, October.
    10. 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.
    11. Baade, Robert A. & Sundberg, Jeffrey O., 1996. "What determines alumni generosity?," Economics of Education Review, Elsevier, vol. 15(1), pages 75-81, February.
    12. Chang-Jin Kim & Charles R. Nelson, 1999. "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262112388, December.
    13. 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.
    14. 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 310-327, July.
    15. Sunil Gupta & Valarie Zeithaml, 2006. "Customer Metrics and Their Impact on Financial Performance," Marketing Science, INFORMS, vol. 25(6), pages 718-739, 11-12.
    16. Davis, Ken & Whittlesey, Norman K. & Huffaker, Ray G. & Butcher, Walter R. & Smith, Eldon D. & Francl, Terry & Smith, Katherine R. & Ravnholt, Eiler C. & Gantz, Eugene & Baquet, Alan E. & Skees, Jerry, 1994. "Letters," Choices: The Magazine of Food, Farm, and Resource Issues, Agricultural and Applied Economics Association, vol. 9(3), pages 1-5.
    17. 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 357-384, March.
    18. 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.
    19. Roland T. Rust & Tuck Siong Chung, 2006. "Marketing Models of Service and Relationships," Marketing Science, INFORMS, vol. 25(6), pages 560-580, 11-12.
    20. Jan Roelf Bult & Tom Wansbeek, 1995. "Optimal Selection for Direct Mail," Marketing Science, INFORMS, vol. 14(4), pages 378-394.
    21. 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.
    22. James J. Heckman, 1981. "Heterogeneity and State Dependence," NBER Chapters, in: Studies in Labor Markets, pages 91-140, National Bureau of Economic Research, Inc.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Marshall Freimer & Dan Horsky, 2008. "Try It, You Will Like It—Does Consumer Learning Lead to Competitive Price Promotions?," Marketing Science, INFORMS, vol. 27(5), pages 796-810, 09-10.
    3. Roland T. Rust & Peter C. Verhoef, 2005. "Optimizing the Marketing Interventions Mix in Intermediate-Term CRM," Marketing Science, INFORMS, vol. 24(3), pages 477-489, December.
    4. Andrés Musalem & Yogesh V. Joshi, 2009. "—How Much Should You Invest in Each Customer Relationship? A Competitive Strategic Approach," Marketing Science, INFORMS, vol. 28(3), pages 555-565, 05-06.
    5. J. Andrew Petersen & V. Kumar & Yolanda Polo & F. Javier Sese, 2018. "Unlocking the power of marketing: understanding the links between customer mindset metrics, behavior, and profitability," Journal of the Academy of Marketing Science, Springer, vol. 46(5), pages 813-836, September.
    6. Polo, Yolanda & Sese, F. Javier & Verhoef, Peter C., 2011. "The Effect of Pricing and Advertising on Customer Retention in a Liberalizing Market," Journal of Interactive Marketing, Elsevier, vol. 25(4), pages 201-214.
    7. Mercedes Esteban-Bravo & Jose M. Vidal-Sanz & Gökhan Yildirim, 2014. "Valuing Customer Portfolios with Endogenous Mass and Direct Marketing Interventions Using a Stochastic Dynamic Programming Decomposition," Marketing Science, INFORMS, vol. 33(5), pages 621-640, September.
    8. Roland T. Rust & Tuck Siong Chung, 2006. "Marketing Models of Service and Relationships," Marketing Science, INFORMS, vol. 25(6), pages 560-580, 11-12.
    9. Wolter, Jeremy S. & Bock, Dora & Smith, Jeffery S. & Cronin, J. Joseph, 2017. "Creating Ultimate Customer Loyalty Through Loyalty Conviction and Customer-Company Identification," Journal of Retailing, Elsevier, vol. 93(4), pages 458-476.
    10. Füsun F. Gönül & Frenkel Ter Hofstede, 2006. "How to Compute Optimal Catalog Mailing Decisions," Marketing Science, INFORMS, vol. 25(1), pages 65-74, 01-02.
    11. Michael Platzer & Thomas Reutterer, 2016. "Ticking Away the Moments: Timing Regularity Helps to Better Predict Customer Activity," Marketing Science, INFORMS, vol. 35(5), pages 779-799, September.
    12. Baltas, George & Doyle, Peter, 2001. "Random utility models in marketing research: a survey," Journal of Business Research, Elsevier, vol. 51(2), pages 115-125, February.
    13. Smith, Aaron & Naik, Prasad A. & Tsai, Chih-Ling, 2006. "Markov-switching model selection using Kullback-Leibler divergence," Journal of Econometrics, Elsevier, vol. 134(2), pages 553-577, October.
    14. J. Miguel Villas-Boas, 2004. "Consumer Learning, Brand Loyalty, and Competition," Marketing Science, INFORMS, vol. 23(1), pages 134-145, December.
    15. Guenther, Miriam & Guenther, Peter, 2021. "The complex firm financial effects of customer satisfaction improvements," International Journal of Research in Marketing, Elsevier, vol. 38(3), pages 639-662.
    16. Jorge Silva-Risso & Irina Ionova, 2008. "—A Nested Logit Model of Product and Transaction-Type Choice for Planning Automakers' Pricing and Promotions," Marketing Science, INFORMS, vol. 27(4), pages 545-566, 07-08.
    17. Peter Ebbes & Rajdeep Grewal & Wayne DeSarbo, 2010. "Modeling strategic group dynamics: A hidden Markov approach," Quantitative Marketing and Economics (QME), Springer, vol. 8(2), pages 241-274, June.
    18. Staus, Alexander, 2011. "Which household attitudes determine the store type choice for meat?," Journal of Retailing and Consumer Services, Elsevier, vol. 18(3), pages 224-234.
    19. George, Morris & Kumar, V. & Grewal, Dhruv, 2013. "Maximizing Profits for a Multi-Category Catalog Retailer," Journal of Retailing, Elsevier, vol. 89(4), pages 374-396.
    20. Tarek Ben Rhouma & Georges Zaccour, 2018. "Optimal Marketing Strategies for the Acquisition and Retention of Service Subscriber," Management Science, INFORMS, vol. 64(6), pages 2609-2627, June.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ecl:stabus:1904r. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/gsstaus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.