Learning Product Characteristics and Consumer Preferences from Search Data

Luis Armona (), Greg Lewis () and Georgios Zervas ()
Additional contact information
Luis Armona: Harvard University, Cambridge, Massachusetts 02138
Greg Lewis: Independent Researcher, Somerville, Massachusetts 02143
Georgios Zervas: Boston University, Boston, Massachusetts 02215

Marketing Science, 2025, vol. 44, issue 4, 838-855

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.

Keywords: e-commerce; search; demand estimation; transfer learning; embeddings (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormksc:v:44:y:2025:i:4:p:838-855

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