IDEAS home Printed from https://ideas.repec.org/a/eee/ijrema/v35y2018i3p415-431.html
   My bibliography  Save this article

A random coefficients mixture hidden Markov model for marketing research

Author

Listed:
  • Kappe, Eelco
  • Stadler Blank, Ashley
  • DeSarbo, Wayne S.

Abstract

The hidden Markov model (HMM) provides a framework to model the time-varying effects of marketing mix variables. When employed in a panel data context, it is important to properly account for unobserved heterogeneity across individuals. We propose a new random coefficients mixture HMM (RCMHMM) that allows for flexible patterns of unobserved heterogeneity in both the state-dependent and transition parameters. The RCMHMM nests all HMMs found in the marketing literature. Results of two simulation studies demonstrate that 1) averaging across a large number of different data generating processes, the RCMHMM outperforms all its nested versions using both in-sample and out-of-sample performance and 2) the RCMHMM is more robust than its nested versions when underlying model assumptions are violated. In addition, we apply the RCMHMM to an empirical application where we examine the effectiveness of in-game promotions in increasing the short-term demand for Major League Baseball (MLB) attendance. We find that the effectiveness of four promotional categories varies over the course of the season and across teams and that the RCMHMM performs best.

Suggested Citation

  • Kappe, Eelco & Stadler Blank, Ashley & DeSarbo, Wayne S., 2018. "A random coefficients mixture hidden Markov model for marketing research," International Journal of Research in Marketing, Elsevier, vol. 35(3), pages 415-431.
    • Handle: RePEc:eee:ijrema:v:35:y:2018:i:3:p:415-431
    DOI: 10.1016/j.ijresmar.2018.07.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167811618300314
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijresmar.2018.07.002?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Sudhir Voleti & Pulak Ghosh, 2013. "A robust approach to measure latent, time-varying equity in hierarchical branding structures," Quantitative Marketing and Economics (QME), Springer, vol. 11(3), pages 289-319, September.
    2. Xiaoquan (Michael) Zhang & Juan Feng, 2011. "Cyclical Bid Adjustments in Search-Engine Advertising," Management Science, INFORMS, vol. 57(9), pages 1703-1719, February.
    3. Jonathan Z. Zhang & Oded Netzer & Asim Ansari, 2014. "Dynamic Targeted Pricing in B2B Relationships," Marketing Science, INFORMS, vol. 33(3), pages 317-337, May.
    4. 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.
    5. Michalek, Jeremy J. & Ebbes, Peter & Adigüzel, Feray & Feinberg, Fred M. & Papalambros, Panos Y., 2011. "Enhancing marketing with engineering: Optimal product line design for heterogeneous markets," International Journal of Research in Marketing, Elsevier, vol. 28(1), pages 1-12.
    6. 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, vol. 10(4), pages 475-503, December.
    7. Jason Shachat & Lijia Wei, 2012. "Procuring Commodities: First-Price Sealed-Bid or English Auctions?," Marketing Science, INFORMS, vol. 31(2), pages 317-333, March.
    8. Dean R. Hyslop, 1999. "State Dependence, Serial Correlation and Heterogeneity in Intertemporal Labor Force Participation of Married Women," Econometrica, Econometric Society, vol. 67(6), pages 1255-1294, November.
    9. Sungho Park & Sachin Gupta, 2011. "A Regime-Switching Model of Cyclical Category Buying," Marketing Science, INFORMS, vol. 30(3), pages 469-480, 05-06.
    10. Sandeep Chandukala & Sylvia Long-Tolbert & Greg Allenby, 2011. "A threshold model for respondent heterogeneity," Marketing Letters, Springer, vol. 22(2), pages 133-146, June.
    11. Liye Ma & Baohong Sun & Sunder Kekre, 2015. "The Squeaky Wheel Gets the Grease—An Empirical Analysis of Customer Voice and Firm Intervention on Twitter," Marketing Science, INFORMS, vol. 34(5), pages 627-645, September.
    12. Jean‐Pierre Dubé & Günter J. Hitsch & Peter E. Rossi, 2010. "State dependence and alternative explanations for consumer inertia," RAND Journal of Economics, RAND Corporation, vol. 41(3), pages 417-445, September.
    13. Ricardo Montoya & Oded Netzer & Kamel Jedidi, 2010. "Dynamic Allocation of Pharmaceutical Detailing and Sampling for Long-Term Profitability," Marketing Science, INFORMS, vol. 29(5), pages 909-924, 09-10.
    14. David A. Schweidel & Eric T. Bradlow & Peter S. Fader, 2011. "Portfolio Dynamics for Customers of a Multiservice Provider," Management Science, INFORMS, vol. 57(3), pages 471-486, March.
    15. Wayne DeSarbo & Heungsun Hwang & Ashley Stadler Blank & Eelco Kappe, 2015. "Constrained Stochastic Extended Redundancy Analysis," Psychometrika, Springer;The Psychometric Society, vol. 80(2), pages 516-534, June.
    16. Peter Ebbes & John C. Liechty & Rajdeep Grewal, 2015. "Attribute-Level Heterogeneity," Management Science, INFORMS, vol. 61(4), pages 885-897, April.
    17. McDonald, Mark & Rascher, Daniel, 2000. "Does Bat Day Make Cents? The Effect of Promotions on the Demand for Major League Baseball," MPRA Paper 25739, University Library of Munich, Germany.
    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. Savannah Wei Shi & Michel Wedel & F. G. M. (Rik) Pieters, 2013. "Information Acquisition During Online Decision Making: A Model-Based Exploration Using Eye-Tracking Data," Management Science, INFORMS, vol. 59(5), pages 1009-1026, May.
    20. 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.
    21. Yan Huang & Param Vir Singh & Anindya Ghose, 2015. "A Structural Model of Employee Behavioral Dynamics in Enterprise Social Media," Management Science, INFORMS, vol. 61(12), pages 2825-2844, December.
    22. Sha Yang & Yi Zhao & Ravi Dhar, 2010. "Modeling the Underreporting Bias in Panel Survey Data," Marketing Science, INFORMS, vol. 29(3), pages 525-539, 05-06.
    23. Peter Lenk & Wayne DeSarbo, 2000. "Bayesian inference for finite mixtures of generalized linear models with random effects," Psychometrika, Springer;The Psychometric Society, vol. 65(1), pages 93-119, March.
    24. David A. Schweidel & Young-Hoon Park & Zainab Jamal, 2014. "A Multiactivity Latent Attrition Model for Customer Base Analysis," Marketing Science, INFORMS, vol. 33(2), pages 273-286, March.
    25. Tülin Erdem & Susumu Imai & Michael Keane, 2003. "Brand and Quantity Choice Dynamics Under Price Uncertainty," Quantitative Marketing and Economics (QME), Springer, vol. 1(1), pages 5-64, March.
    26. Lemmens, Aurélie & Croux, Christophe & Stremersch, Stefan, 2012. "Dynamics in the international market segmentation of new product growth," International Journal of Research in Marketing, Elsevier, vol. 29(1), pages 81-92.
    27. David A. Schweidel & George Knox, 2013. "Incorporating Direct Marketing Activity into Latent Attrition Models," Marketing Science, INFORMS, vol. 32(3), pages 471-487, May.
    28. Nitin Mehta & Xinlei (Jack) Chen & Om Narasimhan, 2010. "Examining Demand Elasticities in Hanemann's Framework: A Theoretical and Empirical Analysis," Marketing Science, INFORMS, vol. 29(3), pages 422-437, 05-06.
    29. Alan L. Montgomery & Shibo Li & Kannan Srinivasan & John C. Liechty, 2004. "Modeling Online Browsing and Path Analysis Using Clickstream Data," Marketing Science, INFORMS, vol. 23(4), pages 579-595, November.
    30. Peter E. Rossi, 2014. "Invited Paper —Even the Rich Can Make Themselves Poor: A Critical Examination of IV Methods in Marketing Applications," Marketing Science, INFORMS, vol. 33(5), pages 655-672, September.
    31. Nitin Mehta & Jian Ni & Kannan Srinivasan & Baohong Sun, 2017. "A Dynamic Model of Health Insurance Choices and Healthcare Consumption Decisions," Marketing Science, INFORMS, vol. 36(3), pages 338-360, May.
    32. Savannah Wei Shi & Jie Zhang, 2014. "Usage Experience with Decision Aids and Evolution of Online Purchase Behavior," Marketing Science, INFORMS, vol. 33(6), pages 871-882, November.
    33. 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.
    34. V. Kumar & S. Sriram & Anita Luo & Pradeep K. Chintagunta, 2011. "Assessing the Effect of Marketing Investments in a Business Marketing Context," Marketing Science, INFORMS, vol. 30(5), pages 924-940, September.
    35. Peel, David A & Thomas, Dennis A, 1988. "Outcome Uncertainty and the Demand for Football: An Analysis of Match Attendances in the English Football League," Scottish Journal of Political Economy, Scottish Economic Society, vol. 35(3), pages 242-249, August.
    36. R. A. Thietart & R. Vivas, 1984. "An Empirical Investigation of Success Strategies for Businesses Along the Product Life Cycle," Management Science, INFORMS, vol. 30(12), pages 1405-1423, December.
    37. Eric M. Schwartz & Eric T. Bradlow & Peter S. Fader, 2014. "Model Selection Using Database Characteristics: Developing a Classification Tree for Longitudinal Incidence Data," Marketing Science, INFORMS, vol. 33(2), pages 188-205, March.
    38. Holtrop, Niels & Wieringa, Jaap E. & Gijsenberg, Maarten J. & Verhoef, Peter C., 2017. "No future without the past? Predicting churn in the face of customer privacy," International Journal of Research in Marketing, Elsevier, vol. 34(1), pages 154-172.
    39. Lucas, Robert Jr, 1976. "Econometric policy evaluation: A critique," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 1(1), pages 19-46, January.
    40. Peter Stüttgen & Peter Boatwright & Robert T. Monroe, 2012. "A Satisficing Choice Model," Marketing Science, INFORMS, vol. 31(6), pages 878-899, November.
    41. Ralf van der Lans & Rik Pieters & Michel Wedel, 2008. "—Competitive Brand Salience," Marketing Science, INFORMS, vol. 27(5), pages 922-931, 09-10.
    42. Robert J. Lemke & Matthew Leonard & Kelebogile Tlhokwane, 2010. "Estimating Attendance at Major League Baseball Games for the 2007 Season," Journal of Sports Economics, , vol. 11(3), pages 316-348, June.
    43. Eva Ascarza & Bruce G. S. Hardie, 2013. "A Joint Model of Usage and Churn in Contractual Settings," Marketing Science, INFORMS, vol. 32(4), pages 570-590, July.
    44. Chib, Siddhartha, 1996. "Calculating posterior distributions and modal estimates in Markov mixture models," Journal of Econometrics, Elsevier, vol. 75(1), pages 79-97, November.
    45. Eelco Kappe & Ashley Stadler Blank & Wayne S. DeSarbo, 2014. "A General Multiple Distributed Lag Framework for Estimating the Dynamic Effects of Promotions," Management Science, INFORMS, vol. 60(6), pages 1489-1510, June.
    46. Thomas C. Boyd & Timothy C. Krehbiel, 2006. "An Analysis of the Effects of Specific Promotion Types on Attendance at Major League Baseball Games," American Journal of Business, Emerald Group Publishing, vol. 21(2), pages 21-32.
    47. 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.
    48. Sam K. Hui, 2017. "Understanding repeat playing behavior in casual games using a Bayesian data augmentation approach," Quantitative Marketing and Economics (QME), Springer, vol. 15(1), pages 29-55, March.
    49. Allenby, Greg M. & Rossi, Peter E., 1998. "Marketing models of consumer heterogeneity," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 57-78, November.
    50. Heckman, James J, 1991. "Identifying the Hand of the Past: Distinguishing State Dependence from Heterogeneity," American Economic Review, American Economic Association, vol. 81(2), pages 75-79, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jyh-How Huang & Yu-Chia Hsu, 2021. "A Multidisciplinary Perspective on Publicly Available Sports Data in the Era of Big Data: A Scoping Review of the Literature on Major League Baseball," SAGE Open, , vol. 11(4), pages 21582440211, November.
    2. Danaher, Peter J. & Sajtos, Laszlo & Danaher, Tracey S., 2020. "Tactical use of rewards to enhance loyalty program effectiveness," International Journal of Research in Marketing, Elsevier, vol. 37(3), pages 505-520.
    3. Chen, Xian & Bai, Shuotian & Wei, Yongqin & Zhao, Yanhui & Yan, Peng & Jiang, Hai, 2023. "Passenger engagement dynamics in ride-hailing services: A heterogeneous hidden Markov approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 171(C).

    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. Jonathan Z. Zhang & Chun-Wei Chang, 2021. "Consumer dynamics: theories, methods, and emerging directions," Journal of the Academy of Marketing Science, Springer, vol. 49(1), pages 166-196, January.
    2. Amirali Kani & Wayne S. DeSarbo & Duncan K. H. Fong, 2018. "A Factorial Hidden Markov Model for the Analysis of Temporal Change in Choice Models," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 5(3), pages 162-177, December.
    3. Peter Ebbes & Oded Netzer, 2022. "Using Social Network Activity Data to Identify and Target Job Seekers," Management Science, INFORMS, vol. 68(4), pages 3026-3046, April.
    4. Gui Liberali & Alina Ferecatu, 2022. "Morphing for Consumer Dynamics: Bandits Meet Hidden Markov Models," Marketing Science, INFORMS, vol. 41(4), pages 769-794, July.
    5. Eva Ascarza & Oded Netzer & Bruce G. S. Hardie, 2018. "Some Customers Would Rather Leave Without Saying Goodbye," Marketing Science, INFORMS, vol. 37(1), pages 54-77, January.
    6. Clarence Lee & Elie Ofek & Thomas J. Steenburgh, 2018. "Personal and Social Usage: The Origins of Active Customers and Ways to Keep Them Engaged," Management Science, INFORMS, vol. 64(6), pages 2473-2495, June.
    7. Park, Chang Hee & Park, Young-Hoon & Schweidel, David A., 2018. "The effects of mobile promotions on customer purchase dynamics," International Journal of Research in Marketing, Elsevier, vol. 35(3), pages 453-470.
    8. Christof Naumzik & Stefan Feuerriegel & Markus Weinmann, 2022. "I Will Survive: Predicting Business Failures from Customer Ratings," Marketing Science, INFORMS, vol. 41(1), pages 188-207, January.
    9. Guhl, Daniel & Baumgartner, Bernhard & Kneib, Thomas & Steiner, Winfried J., 2018. "Estimating time-varying parameters in brand choice models: A semiparametric approach," International Journal of Research in Marketing, Elsevier, vol. 35(3), pages 394-414.
    10. Antonello Maruotti & Jan Bulla & Tanya Mark, 2019. "Assessing the influence of marketing activities on customer behaviors: a dynamic clustering approach," METRON, Springer;Sapienza Università di Roma, vol. 77(1), pages 19-42, April.
    11. Eva Ascarza & Scott A. Neslin & Oded Netzer & Zachery Anderson & Peter S. Fader & Sunil Gupta & Bruce G. S. Hardie & Aurélie Lemmens & Barak Libai & David Neal & Foster Provost & Rom Schrift, 2018. "In Pursuit of Enhanced Customer Retention Management: Review, Key Issues, and Future Directions," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 5(1), pages 65-81, March.
    12. Valendin, Jan & Reutterer, Thomas & Platzer, Michael & Kalcher, Klaudius, 2022. "Customer base analysis with recurrent neural networks," International Journal of Research in Marketing, Elsevier, vol. 39(4), pages 988-1018.
    13. Park, Chang Hee & Yoon, Tae Jung, 2022. "The dark side of up-selling promotions: Evidence from an analysis of cross-brand purchase behavior☆," Journal of Retailing, Elsevier, vol. 98(4), pages 647-666.
    14. Yingjie Zhang & Beibei Li & Xueming Luo & Xiaoyi Wang, 2019. "Personalized Mobile Targeting with User Engagement Stages: Combining a Structural Hidden Markov Model and Field Experiment," Information Systems Research, INFORMS, vol. 30(3), pages 787-804, September.
    15. Jonathan Z. Zhang & Oded Netzer & Asim Ansari, 2014. "Dynamic Targeted Pricing in B2B Relationships," Marketing Science, INFORMS, vol. 33(3), pages 317-337, May.
    16. Arun Gopalakrishnan & Eric T. Bradlow & Peter S. Fader, 2017. "A Cross-Cohort Changepoint Model for Customer-Base Analysis," Marketing Science, INFORMS, vol. 36(2), pages 195-213, March.
    17. Chou, Ping & Chuang, Howard Hao-Chun & Chou, Yen-Chun & Liang, Ting-Peng, 2022. "Predictive analytics for customer repurchase: Interdisciplinary integration of buy till you die modeling and machine learning," European Journal of Operational Research, Elsevier, vol. 296(2), pages 635-651.
    18. Yanwen Wang & Chunhua Wu & Ting Zhu, 2019. "Mobile Hailing Technology and Taxi Driving Behaviors," Marketing Science, INFORMS, vol. 38(5), pages 734-755, September.
    19. Arun Gopalakrishnan & Zhenling Jiang & Yulia Nevskaya & Raphael Thomadsen, 2021. "Can Non-tiered Customer Loyalty Programs Be Profitable?," Marketing Science, INFORMS, vol. 40(3), pages 508-526, May.
    20. Chang, Chun-Wei & Zhang, Jonathan Z., 2016. "The Effects of Channel Experiences and Direct Marketing on Customer Retention in Multichannel Settings," Journal of Interactive Marketing, Elsevier, vol. 36(C), pages 77-90.

    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:eee:ijrema:v:35:y:2018:i:3:p:415-431. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/international-journal-of-research-in-marketing/ .

    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.