Research Details

Abstract

Many online retailers offer personalized recommendations to help consumers make their choices. While standard recommendation algorithms are designed to guide consumers to the most relevant items, retailers may deviate from them and instead 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 an 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 as well as to estimate how personalized rankings affect consumer welfare. Our results reveal that, although the current algorithm does put positive weight on profitability, personalized rankings still increase consumer surplus. Using this case study, we argue that online retailers may generally have incentives to adopt consumer-centric personalization algorithms as a way to retain consumers and maximize long-term growth.

Type

Article

Author(s)

Ilya Morozov, Robert Donnelly, Ayush Kanodia

Date Published

2023

Citations

Morozov, Ilya, Robert Donnelly, and Ayush Kanodia. 2023. Welfare Effects of Personalized Rankings. Marketing Science.(1)