IDEAS home Printed from https://ideas.repec.org/a/kap/qmktec/v13y2015i3p173-202.html
   My bibliography  Save this article

Consumer learning and evolution of consumer brand preferences

Author

Listed:
  • Hai Che
  • Tülin Erdem
  • T. Öncü

Abstract

We develop a structural dynamic demand model that examines how brand preferences evolve when consumers are uncertain about product quality and their needs change periodically. We allow for strategic sampling behavior of consumers under quality uncertainty and allow for strategic sampling to increase periodically as consumers’ needs change periodically. We differ from previous work on forward-looking consumer Bayesian learning by allowing for 1) spill-over learning effects across different versions of products or products in different product categories that share a brand name and 2) duration-dependence in utility for a specific version of a product or product class to capture systematic periodic changes in consumer utility and migration of consumers across product versions or classes. We also assess the evolution of price elasticities in markets where there is consumer quality uncertainty that diminishes over time as consumers get more experienced. We estimate our model using scanner data for the disposable diapers category and discuss the consumer behavior and managerial implications of our estimation and policy simulation results. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Hai Che & Tülin Erdem & T. Öncü, 2015. "Consumer learning and evolution of consumer brand preferences," Quantitative Marketing and Economics (QME), Springer, vol. 13(3), pages 173-202, September.
    • Handle: RePEc:kap:qmktec:v:13:y:2015:i:3:p:173-202
    DOI: 10.1007/s11129-015-9158-x
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11129-015-9158-x
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11129-015-9158-x?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. Gautam Gowrisankaran & Marc Rysman, 2012. "Dynamics of Consumer Demand for New Durable Goods," Journal of Political Economy, University of Chicago Press, vol. 120(6), pages 1173-1219.
    2. Rust, John, 1987. "Optimal Replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher," Econometrica, Econometric Society, vol. 55(5), pages 999-1033, September.
    3. Doug J. Chung & Thomas Steenburgh & K. Sudhir, 2014. "Do Bonuses Enhance Sales Productivity? A Dynamic Structural Analysis of Bonus-Based Compensation Plans," Marketing Science, INFORMS, vol. 33(2), pages 165-187, March.
    4. Tat Chan & Chakravarthi Narasimhan & Ying Xie, 2013. "Treatment Effectiveness and Side Effects: A Model of Physician Learning," Management Science, INFORMS, vol. 59(6), pages 1309-1325, June.
    5. Ronald L. Goettler & Brett R. Gordon, 2011. "Does AMD Spur Intel to Innovate More?," Journal of Political Economy, University of Chicago Press, vol. 119(6), pages 1141-1200.
    6. Matthew Osborne, 2011. "Consumer learning, switching costs, and heterogeneity: A structural examination," Quantitative Marketing and Economics (QME), Springer, vol. 9(1), pages 25-70, March.
    7. Baohong Sun, 2005. "Promotion Effect on Endogenous Consumption," Marketing Science, INFORMS, vol. 24(3), pages 430-443, July.
    8. Tülin Erdem & Michael P. Keane & Baohong Sun, 2008. "A Dynamic Model of Brand Choice When Price and Advertising Signal Product Quality," Marketing Science, INFORMS, vol. 27(6), pages 1111-1125, 11-12.
    9. Tülin Erdem & Michael P. Keane, 1996. "Decision-Making Under Uncertainty: Capturing Dynamic Brand Choice Processes in Turbulent Consumer Goods Markets," Marketing Science, INFORMS, vol. 15(1), pages 1-20.
    10. Gregory S. Crawford & Matthew Shum, 2005. "Uncertainty and Learning in Pharmaceutical Demand," Econometrica, Econometric Society, vol. 73(4), pages 1137-1173, July.
    11. Keane, Michael P, 1992. "A Note on Identification in the Multinomial Probit Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(2), pages 193-200, April.
    12. Daniel A. Ackerberg, 2003. "Advertising, learning, and consumer choice in experience good markets: an empirical examination," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 44(3), pages 1007-1040, August.
    13. Sangwoo Shin & Sanjog Misra & Dan Horsky, 2012. "Disentangling Preferences and Learning in Brand Choice Models," Marketing Science, INFORMS, vol. 31(1), pages 115-137, January.
    14. Wesley R. Hartmann, 2010. "Demand Estimation with Social Interactions and the Implications for Targeted Marketing," Marketing Science, INFORMS, vol. 29(4), pages 585-601, 07-08.
    15. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2013. "Learning Models: An Assessment of Progress, Challenges and New Developments," Economics Papers 2013-W07, Economics Group, Nuffield College, University of Oxford.
    16. Wesley Hartmann, 2006. "Intertemporal effects of consumption and their implications for demand elasticity estimates," Quantitative Marketing and Economics (QME), Springer, vol. 4(4), pages 325-349, December.
    17. Tülin Erdem & Michael Keane & T. Öncü & Judi Strebel, 2005. "Learning About Computers: An Analysis of Information Search and Technology Choice," Quantitative Marketing and Economics (QME), Springer, vol. 3(3), pages 207-247, September.
    18. Soren T. Anderson & Ryan Kellogg & Ashley Langer & James M. Sallee, 2013. "The Intergenerational Transmission of Automobile Brand Preferences: Empirical Evidence and Implications for Firm Strategy," NBER Working Papers 19535, National Bureau of Economic Research, Inc.
    19. Coscelli, Andrea & Shum, Matthew, 2004. "An empirical model of learning and patient spillovers in new drug entry," Journal of Econometrics, Elsevier, vol. 122(2), pages 213-246, October.
    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. Jialie Chen & Vithala R. Rao, 2020. "A Dynamic Model of Rational Addiction with Stockpiling and Learning: An Empirical Examination of E-cigarettes," Management Science, INFORMS, vol. 66(12), pages 5886-5905, December.
    2. Arjen van Lin & Els Gijsbrechts, 2019. "“Hello Jumbo!” The Spatio-Temporal Rollout and Traffic to a New Grocery Chain After Acquisition," Management Science, INFORMS, vol. 67(5), pages 2388-2411, May.
    3. Chunhua Wu & Hai Che & Tat Y. Chan & Xianghua Lu, 2015. "The Economic Value of Online Reviews," Marketing Science, INFORMS, vol. 34(5), pages 739-754, September.
    4. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2017. "Empirical Models of Learning Dynamics: A Survey of Recent Developments," International Series in Operations Research & Management Science, in: Berend Wierenga & Ralf van der Lans (ed.), Handbook of Marketing Decision Models, edition 2, chapter 0, pages 223-257, Springer.
    5. Michał Kot, 2022. "An agent-based model of consumer choice. An evaluation of the strategy of pricing and advertising," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 32(1), pages 73-95.
    6. Yong Tan, 2019. "Dynamic Entry With Demand And Supply Side Spillovers," Contemporary Economic Policy, Western Economic Association International, vol. 37(1), pages 86-101, January.
    7. van Ewijk, Bernadette J. & Gijsbrechts, Els & Steenkamp, Jan-Benedict E.M., 2022. "The dark side of innovation: How new SKUs affect brand choice in the presence of consumer uncertainty and learning," International Journal of Research in Marketing, Elsevier, vol. 39(4), pages 967-987.
    8. Xu, Yan, 2017. "Essays on preference formation and home production," Other publications TiSEM b028fd7e-53ba-4ff6-97eb-4, Tilburg University, School of Economics and Management.
    9. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2020. "How much do consumers know about the quality of products? Evidence from the diaper market," The Japanese Economic Review, Springer, vol. 71(4), pages 541-569, October.

    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. Hai Che & Tülin Erdem & T. Sabri Öncü, 2015. "Consumer learning and evolution of consumer brand preferences," Quantitative Marketing and Economics (QME), Springer, vol. 13(3), pages 173-202, September.
    2. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2013. "Invited Paper ---Learning Models: An Assessment of Progress, Challenges, and New Developments," Marketing Science, INFORMS, vol. 32(6), pages 913-938, November.
    3. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2013. "Learning Models: An Assessment of Progress, Challenges and New Developments," Economics Papers 2013-W07, Economics Group, Nuffield College, University of Oxford.
    4. Andrew Ching & Susumu Imai & Masakazu Ishihara & Neelam Jain, 2012. "A practitioner’s guide to Bayesian estimation of discrete choice dynamic programming models," Quantitative Marketing and Economics (QME), Springer, vol. 10(2), pages 151-196, June.
    5. Song Lin & Juanjuan Zhang & John R. Hauser, 2015. "Learning from Experience, Simply," Marketing Science, INFORMS, vol. 34(1), pages 1-19, January.
    6. Xu, Yan, 2017. "Essays on preference formation and home production," Other publications TiSEM b028fd7e-53ba-4ff6-97eb-4, Tilburg University, School of Economics and Management.
    7. Yufeng Huang, 2019. "Learning by Doing and the Demand for Advanced Products," Marketing Science, INFORMS, vol. 38(1), pages 107-128, January.
    8. Jie Bai, 2016. "Melons as Lemons: Asymmetric Information, Consumer Learning and Seller Reputation," Natural Field Experiments 00540, The Field Experiments Website.
    9. Ching, Andrew T. & Erdem, Tülin & Keane, Michael P., 2014. "A simple method to estimate the roles of learning, inventories and category consideration in consumer choice," Journal of choice modelling, Elsevier, vol. 13(C), pages 60-72.
    10. Guofang Huang & Hong Luo & Jing Xia, 2019. "Invest in Information or Wing It? A Model of Dynamic Pricing with Seller Learning," Management Science, INFORMS, vol. 65(12), pages 5556-5583, December.
    11. Mandy Mantian Hu & Sha Yang & Daniel Yi Xu, 2019. "Understanding the Social Learning Effect in Contagious Switching Behavior," Management Science, INFORMS, vol. 65(10), pages 4771-4794, October.
    12. Praveen K. Kopalle & Yacheng Sun & Scott A. Neslin & Baohong Sun & Vanitha Swaminathan, 2012. "The Joint Sales Impact of Frequency Reward and Customer Tier Components of Loyalty Programs," Marketing Science, INFORMS, vol. 31(2), pages 216-235, March.
    13. Kohei Kawaguchi, 2021. "When Will Workers Follow an Algorithm? A Field Experiment with a Retail Business," Management Science, INFORMS, vol. 67(3), pages 1670-1695, March.
    14. Gonca P. Soysal & Lakshman Krishnamurthi, 2012. "Demand Dynamics in the Seasonal Goods Industry: An Empirical Analysis," Marketing Science, INFORMS, vol. 31(2), pages 293-316, March.
    15. Mingyu Joo & Dinesh K. Gauri & Kenneth C. Wilbur, 2020. "Temporal Distance and Price Responsiveness: Empirical Investigation of the Cruise Industry," Management Science, INFORMS, vol. 66(11), pages 5362-5388, November.
    16. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2020. "How much do consumers know about the quality of products? Evidence from the diaper market," The Japanese Economic Review, Springer, vol. 71(4), pages 541-569, October.
    17. Daniel Ackerberg, 2009. "A new use of importance sampling to reduce computational burden in simulation estimation," Quantitative Marketing and Economics (QME), Springer, vol. 7(4), pages 343-376, December.
    18. Jean-Pierre Dubé & Xueming Luo & Zheng Fang, 2017. "Self-Signaling and Prosocial Behavior: A Cause Marketing Experiment," Marketing Science, INFORMS, vol. 36(2), pages 140-156, March.
    19. van Ewijk, Bernadette J. & Gijsbrechts, Els & Steenkamp, Jan-Benedict E.M., 2022. "The dark side of innovation: How new SKUs affect brand choice in the presence of consumer uncertainty and learning," International Journal of Research in Marketing, Elsevier, vol. 39(4), pages 967-987.
    20. Zhu, Z.;, 2023. "The Value of Patients: Heterogenous Physician Learning and Generic Drug Diffusion," Health, Econometrics and Data Group (HEDG) Working Papers 23/12, HEDG, c/o Department of Economics, University of York.

    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:kap:qmktec:v:13:y:2015:i:3:p:173-202. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.