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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
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