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Online Demand Under Limited Consumer Search

Author

Listed:
  • Jun B. Kim

    () (College of Management, Georgia Institute of Technology, Atlanta, Georgia 30308)

  • Paulo Albuquerque

    () (Simon Graduate School of Business, University of Rochester, Rochester, New York 14627)

  • Bart J. Bronnenberg

    () (CentER, Tilburg School of Economics and Management, University of Tilburg, 5037 AB Tilburg, The Netherlands)

Abstract

Using aggregate product search data from Amazon.com, we jointly estimate consumer information search and online demand for consumer durable goods. To estimate the demand and search primitives, we introduce an optimal sequential search process into a model of choice and treat the observed market-level product search data as aggregations of individual-level optimal search sequences. The model builds on the dynamic programming framework by Weitzman [Weitzman, M. L. 1979. Optimal search for the best alternative. Econometrica 47(3) 641-654] and combines it with a choice model. It can accommodate highly complex demand patterns at the market level. At the individual level, the model has a number of attractive properties in estimation, including closed-form expressions for the probability distribution of alternative sets of searched goods and breaking the curse of dimensionality. Using numerical experiments, we verify the model's ability to identify the heterogeneous consumer tastes and search costs from product search data. Empirically, the model is applied to the online market for camcorders and is used to answer manufacturer questions about market structure and competition and to address policy-maker issues about the effect of selectively lowered search costs on consumer surplus outcomes. We demonstrate that the demand estimates from our search model predict the actual product sales ranks. We find that consumer search for camcorders at Amazon.com is typically limited to 10-15 choice options and that this affects estimates of own and cross elasticities. In a policy simulation, we also find that the vast majority of the households benefit from Amazon.com's product recommendations via lower search costs.

Suggested Citation

  • Jun B. Kim & Paulo Albuquerque & Bart J. Bronnenberg, 2010. "Online Demand Under Limited Consumer Search," Marketing Science, INFORMS, vol. 29(6), pages 1001-1023, 11-12.
  • Handle: RePEc:inm:ormksc:v:29:y:2010:i:6:p:1001-1023
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    File URL: http://dx.doi.org/10.1287/mksc.1100.0574
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Timothy J. Richards & Stephen F. Hamilton & Koichi Yonezawa, 2017. "Variety and the Cost of Search in Supermarket Retailing," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 50(3), pages 263-285, May.
    2. Elisabeth Honka, 2014. "Quantifying search and switching costs in the US auto insurance industry," RAND Journal of Economics, RAND Corporation, vol. 45(4), pages 847-884, December.
    3. Helmers, Christian & Krishnan, Pramila & Patnam, Manasa, 2015. "Attention and Saliency on the Internet: Evidence from an Online Recommendation System," CEPR Discussion Papers 10939, C.E.P.R. Discussion Papers.
    4. Song Yao & Carl F. Mela, 2011. "A Dynamic Model of Sponsored Search Advertising," Marketing Science, INFORMS, vol. 30(3), pages 447-468, 05-06.
    5. repec:kap:qmktec:v:15:y:2017:i:3:d:10.1007_s11129-017-9184-y is not listed on IDEAS
    6. Jose Luis Moraga-Gonzalez & Zsolt Sandor & Matthijs R. Wildenbeest, 2010. "On the Identification of the Costs of Simultaneous Search," Working Papers 2010-10, Indiana University, Kelley School of Business, Department of Business Economics and Public Policy.
    7. Thomas Blake & Chris Nosko & Steven Tadelis, 2016. "Returns to Consumer Search: Evidence from eBay," NBER Working Papers 22302, National Bureau of Economic Research, Inc.
    8. Paulo Albuquerque & Polykarpos Pavlidis & Udi Chatow & Kay-Yut Chen & Zainab Jamal, 2012. "Evaluating Promotional Activities in an Online Two-Sided Market of User-Generated Content," Marketing Science, INFORMS, vol. 31(3), pages 406-432, May.
    9. Tiago Pires, 2016. "Costly search and consideration sets in storable goods markets," Quantitative Marketing and Economics (QME), Springer, vol. 14(3), pages 157-193, September.
    10. Huang, Yufeng, 2015. "Empirical analysis of consumer behavior," Other publications TiSEM 9cc96a79-43d7-436d-87d3-3, Tilburg University, School of Economics and Management.
    11. Bronnenberg, Bart & Dube, Jean-Pierre, 2016. "The Formation of Consumer Brand Preferences," CEPR Discussion Papers 11648, C.E.P.R. Discussion Papers.
    12. Babur De los Santos & Sergei Koulayev, 2012. "Optimizing Click-through in Online Rankings for Partially Anonymous Consumers," Working Papers 2012-04, Indiana University, Kelley School of Business, Department of Business Economics and Public Policy.
    13. Stephan Seiler, 2013. "The impact of search costs on consumer behavior: A dynamic approach," Quantitative Marketing and Economics (QME), Springer, vol. 11(2), pages 155-203, June.
    14. Martin Gaynor & Carol Propper & Stephan Seiler, 2016. "Free to Choose? Reform, Choice, and Consideration Sets in the English National Health Service," American Economic Review, American Economic Association, vol. 106(11), pages 3521-3557, November.
    15. Bart J. Bronnenberg & Jean-Pierre H. Dubé, 2016. "The Formation of Consumer Brand Preferences," NBER Working Papers 22691, National Bureau of Economic Research, Inc.
    16. Sergei Koulayev, 2014. "Search for differentiated products: identification and estimation," RAND Journal of Economics, RAND Corporation, vol. 45(3), pages 553-575, September.
    17. Carl F. Mela, 2011. "Structural Workshop Paper --Data Selection and Procurement," Marketing Science, INFORMS, vol. 30(6), pages 965-976, November.
    18. Tudón M., José F., 2015. "Pay-what-you-want because I do not know how much to charge you," Economics Letters, Elsevier, vol. 137(C), pages 41-44.
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