IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v44y2025i4p838-855.html

Learning Product Characteristics and Consumer Preferences from Search Data

Author

Listed:
  • Luis Armona

    (Harvard University, Cambridge, Massachusetts 02138)

  • Greg Lewis

    (Independent Researcher, Somerville, Massachusetts 02143)

  • Georgios Zervas

    (Boston University, Boston, Massachusetts 02215)

Abstract

A key idea in demand estimation is to model products as bundles of characteristics. In this paper, we offer an approach for jointly learning latent product characteristics and consumer preferences from search data in order to predict demand more accurately. We combine data on consumers’ web-browsing histories and hotel price/quantity data to test this method in the hotel market. In two distinct applications, we show that closeness in latent characteristic space predicts competition, and parameters learned from search data substantially improve postmerger demand predictions.

Suggested Citation

  • Luis Armona & Greg Lewis & Georgios Zervas, 2025. "Learning Product Characteristics and Consumer Preferences from Search Data," Marketing Science, INFORMS, vol. 44(4), pages 838-855, July.
    • Handle: RePEc:inm:ormksc:v:44:y:2025:i:4:p:838-855
    DOI: 10.1287/mksc.2023.0118
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mksc.2023.0118
    Download Restriction: no
    File URL: https://libkey.io/10.1287/mksc.2023.0118?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
    ---><---

    References listed on IDEAS

    as
    1. Weitzman, Martin L, 1979. "Optimal Search for the Best Alternative," Econometrica, Econometric Society, vol. 47(3), pages 641-654, May.
    2. Amil Petrin, 2002. "Quantifying the Benefits of New Products: The Case of the Minivan," Journal of Political Economy, University of Chicago Press, vol. 110(4), pages 705-729, August.
    3. Raluca M. Ursu, 2018. "The Power of Rankings: Quantifying the Effect of Rankings on Online Consumer Search and Purchase Decisions," Marketing Science, INFORMS, vol. 37(4), pages 530-552, August.
    4. Sergei Koulayev, 2014. "Search for differentiated products: identification and estimation," RAND Journal of Economics, RAND Corporation, vol. 45(3), pages 553-575, September.
    5. Honka, Elisabeth & Seiler, Stephan & Ursu, Raluca, 2024. "Consumer search: What can we learn from pre-purchase data?," Journal of Retailing, Elsevier, vol. 100(1), pages 114-129.
    6. Cardell, N. Scott, 1997. "Variance Components Structures for the Extreme-Value and Logistic Distributions with Application to Models of Heterogeneity," Econometric Theory, Cambridge University Press, vol. 13(2), pages 185-213, April.
    7. 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.
    8. Nevo, Aviv, 2001. "Measuring Market Power in the Ready-to-Eat Cereal Industry," Econometrica, Econometric Society, vol. 69(2), pages 307-342, March.
    9. Kelvin J. Lancaster, 1966. "A New Approach to Consumer Theory," Journal of Political Economy, University of Chicago Press, vol. 74(2), pages 132-132.
    10. Elrod, Terry & Keane, Michael, 1995. "A Factor-Analytic Probit Model for Representing the Market Structure in Panel Data," MPRA Paper 52434, University Library of Munich, Germany.
    11. Patrick Bajari & Denis Nekipelov & Stephen P. Ryan & Miaoyu Yang, 2015. "Machine Learning Methods for Demand Estimation," American Economic Review, American Economic Association, vol. 105(5), pages 481-485, May.
    12. Terry Elrod, 1988. "Choice Map: Inferring a Product-Market Map from Panel Data," Marketing Science, INFORMS, vol. 7(1), pages 21-40.
    13. Liran Einav, 2007. "Seasonality in the U.S. motion picture industry," RAND Journal of Economics, RAND Corporation, vol. 38(1), pages 127-145, March.
    14. José Luis Moraga-González & Zsolt Sándor & Matthijs R Wildenbeest, 2023. "Consumer Search and Prices in the Automobile Market," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 90(3), pages 1394-1440.
    15. Christopher Conlon & Jeff Gortmaker, 2020. "Best practices for differentiated products demand estimation with PyBLP," RAND Journal of Economics, RAND Corporation, vol. 51(4), pages 1108-1161, December.
    16. Deaton, Angus S & Muellbauer, John, 1980. "An Almost Ideal Demand System," American Economic Review, American Economic Association, vol. 70(3), pages 312-326, June.
    17. Babur De los Santos & Matthijs R. Wildenbeest, 2017. "E-book pricing and vertical restraints," Quantitative Marketing and Economics (QME), Springer, vol. 15(2), pages 85-122, June.
    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. Jun B. Kim & Paulo Albuquerque & Bart J. Bronnenberg, 2017. "The Probit Choice Model Under Sequential Search with an Application to Online Retailing," Management Science, INFORMS, vol. 63(11), pages 3911-3929, November.
    2. Raluca Mihaela Ursu & Andrey Simonov & Eunkyung An, 2025. "Online Advertising as Passive Search," Management Science, INFORMS, vol. 71(2), pages 1050-1073, February.
    3. Raluca M. Ursu & Qingliang Wang & Pradeep K. Chintagunta, 2020. "Search Duration," Marketing Science, INFORMS, vol. 39(5), pages 849-871, September.
    4. Raluca Ursu & Stephan Seiler & Elisabeth Honka, 2025. "The sequential search model: A framework for empirical research," Quantitative Marketing and Economics (QME), Springer, vol. 23(1), pages 165-213, March.
    5. Dan Yavorsky & Elisabeth Honka & Keith Chen, 2021. "Consumer search in the U.S. auto industry: The role of dealership visits," Quantitative Marketing and Economics (QME), Springer, vol. 19(1), pages 1-52, March.
    6. Tinghan Zhang, 2025. "A Ranking Representation of Optimal Sequential Search," Papers 2501.07514, arXiv.org, revised May 2026.
    7. Robert Donnelly & Ayush Kanodia & Ilya Morozov, 2024. "Welfare Effects of Personalized Rankings," Marketing Science, INFORMS, vol. 43(1), pages 92-113, January.
    8. Honka, Elisabeth & Seiler, Stephan & Ursu, Raluca, 2024. "Consumer search: What can we learn from pre-purchase data?," Journal of Retailing, Elsevier, vol. 100(1), pages 114-129.
    9. Jae Hyen Chung & Pradeep Chintagunta & Sanjog Misra, 2025. "Simulated maximum likelihood estimation of the sequential search model," Quantitative Marketing and Economics (QME), Springer, vol. 23(1), pages 105-164, March.
    10. Bokhari, Farasat A.S. & Mariuzzo, Franco, 2018. "Demand estimation and merger simulations for drugs: Logits v. AIDS," International Journal of Industrial Organization, Elsevier, vol. 61(C), pages 653-685.
    11. De los Santos, Babur, 2018. "Consumer search on the Internet," International Journal of Industrial Organization, Elsevier, vol. 58(C), pages 66-105.
    12. Ivy Chu Dang & Raluca M. Ursu & Pradeep K. Chintagunta, 2026. "Going back to move forward? How search revisits on a website we built inform us about search outcomes," Quantitative Marketing and Economics (QME), Springer, vol. 24(1), pages 1-39, December.
    13. 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.
    14. Rafael P. Greminger, 2022. "Optimal Search and Discovery," Management Science, INFORMS, vol. 68(5), pages 3904-3924, May.
    15. Yuxin Chen & Song Yao, 2017. "Sequential Search with Refinement: Model and Application with Click-Stream Data," Management Science, INFORMS, vol. 63(12), pages 4345-4365, December.
    16. Charles Murry & Yiyi Zhou, 2020. "Consumer Search and Automobile Dealer Colocation," Management Science, INFORMS, vol. 66(5), pages 1909-1934, May.
    17. Bronnenberg, Bart & Dube, Jean-Pierre, 2016. "The Formation of Consumer Brand Preferences," CEPR Discussion Papers 11648, C.E.P.R. Discussion Papers.
    18. Syed Badruddoza & Modhurima Amin & Jill McCluskey, 2019. "Assessing the Importance of an Attribute in a Demand SystemStructural Model versus Machine Learning," Working Papers 2019-5, School of Economic Sciences, Washington State University.
    19. José Luis Moraga-González & Zsolt Sándor & Matthijs R Wildenbeest, 2023. "Consumer Search and Prices in the Automobile Market," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 90(3), pages 1394-1440.
    20. Ilya Morozov & Stephan Seiler & Xiaojing Dong & Liwen Hou, 2021. "Estimation of Preference Heterogeneity in Markets with Costly Search," Marketing Science, INFORMS, vol. 40(5), pages 871-899, September.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:inm:ormksc:v:44:y:2025:i:4:p:838-855. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.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.