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Competing for Recommendations: The Strategic Impact of Personalized Product Recommendations in Online Marketplaces

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  • Bo Zhou

    (Robert H. Smith School of Business, University of Maryland, College Park, Maryland 20742)

  • Tianxin Zou

    (Warrington College of Business, University of Florida, Gainesville, Florida 32611)

Abstract

We study how an online marketplace’s personalized product recommendations and its consumer profiling accuracy affect third-party sellers’ competition and the market outcomes. Sellers strategically adjust prices to compete for the marketplace’s recommendations. As the marketplace more accurately predicts consumers’ preferences, the equilibrium price first decreases and then increases, and both the marketplace’s and the sellers’ profits may decrease despite the improved recommendation accuracy. Moreover, recommending the most profitable product for each recommendation may reduce profits of the marketplace and the sellers, and the marketplace can benefit from excluding pricing information in its recommendation decisions to prevent sellers’ recommendation competition. Counterintuitively, regulations that bar recommendations from considering profit margin information can lead to higher prices and thus harm consumers. These results are driven by competing sellers’ three distinctive incentives: competing for recommendations, exploiting targeted consumers, and undercutting rivals’ prices. We also find that our key insights remain qualitatively unchanged if the marketplace recommends products based on consumer surplus, and the equilibrium price will be lower in comparison. Finally, various extensions demonstrate the robustness of these results.

Suggested Citation

  • Bo Zhou & Tianxin Zou, 2023. "Competing for Recommendations: The Strategic Impact of Personalized Product Recommendations in Online Marketplaces," Marketing Science, INFORMS, vol. 42(2), pages 360-376, March.
  • Handle: RePEc:inm:ormksc:v:42:y:2023:i:2:p:360-376
    DOI: 10.1287/mksc.2022.1388
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    References listed on IDEAS

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    Cited by:

    1. Xingchen Xu & Stephanie Lee & Yong Tan, 2023. "Algorithmic Collusion or Competition: the Role of Platforms' Recommender Systems," Papers 2309.14548, arXiv.org.

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