Welfare Effects of Personalized Rankings

Robert Donnelly, Ayush Kanodia () and Ilya Morozov ()
Additional contact information
Ayush Kanodia: Stanford University, Stanford, California 94305
Ilya Morozov: Northwestern University, Evanston, Illinois 60208

Marketing Science, 2024, vol. 43, issue 1, 92-113

Abstract: Many online retailers offer personalized recommendations to help consumers make their choices. Although standard recommendation algorithms are designed to guide consumers to the most relevant items, retailers can instead choose to steer consumers toward profitable options. We ask whether such strategic behavior arises in practice and to what extent it reduces consumers’ benefits from personalized recommendations. Using data from a large-scale randomized experiment in which a large online retailer introduced personalized rankings, we show that personalization makes consumers search more and generates more purchases relative to uniform bestseller-based rankings. We then estimate a model of search and rankings and use it to reverse-engineer the retailer’s objectives and to assess the effect of personalized rankings on consumer welfare. Our results reveal that although the current algorithm does put positive weight on profitability, personalized rankings still substantially increase consumer surplus. This case study suggests that online retailers may have incentives to adopt consumer-centric personalization algorithms as a way to retain consumers and maximize long-term growth.

Keywords: recommender systems; product rankings; consumer search; welfare effects (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormksc:v:43:y:2024:i:1:p:92-113

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