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Optimal Pricing with Recommender Systems

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Abstract

We study optimal pricing in the presence of recommender systems. A recommender system affects the market in two ways: (i) it creates value by reducing product uncertainty for the customers and hence (ii) its recommendations can be offered as add-ons which generate informational externalities. The quality of the recommendation add-on is endogenously determined by sales. We investigate the impact of these factors on the optimal pricing by a seller with a recommender system against a competitive fringe without such a system. If the recommender system is sufficiently effective in reducing uncertainty, then the seller prices otherwise symmetric products differently to have some products experienced more aggressively. Moreover, the seller segments the market so that customers with more inflexible tastes pay higher prices to get better recommendations.

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  • Dirk Bergemann & Deran Ozmen, 2006. "Optimal Pricing with Recommender Systems," Cowles Foundation Discussion Papers 1563, Cowles Foundation for Research in Economics, Yale University.
    • Handle: RePEc:cwl:cwldpp:1563
    Note: CFP 1177
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    File URL: https://cowles.yale.edu/sites/default/files/files/pub/d15/d1563.pdf
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    1. Paul Resnick & Christopher Avery & Richard Zeckhauser, 1999. "The Market for Evaluations," American Economic Review, American Economic Association, vol. 89(3), pages 564-584, June.
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    Cited by:

    1. Dirk Bergemann & Marco Ottaviani, 2021. "Information Markets and Nonmarkets," Cowles Foundation Discussion Papers 2296, Cowles Foundation for Research in Economics, Yale University.
    2. Hong Jun Huang & Jun Yang & Benrong Zheng, 2021. "Demand effects of product similarity network in e-commerce platform," Electronic Commerce Research, Springer, vol. 21(2), pages 297-327, June.
    3. Sebastian Köhler & Thomas Wöhner & Ralf Peters, 2016. "The impact of consumer preferences on the accuracy of collaborative filtering recommender systems," Electronic Markets, Springer;IIM University of St. Gallen, vol. 26(4), pages 369-379, November.
    4. Aridor, Guy & Gonçalves, Duarte, 2022. "Recommenders’ originals: The welfare effects of the dual role of platforms as producers and recommender systems," International Journal of Industrial Organization, Elsevier, vol. 83(C).
    5. Ian Ball & James Bono & Justin Grana & Nicole Immorlica & Brendan Lucier & Aleksandrs Slivkins, 2022. "Content Filtering with Inattentive Information Consumers," Papers 2205.14060, arXiv.org, revised Dec 2023.
    6. Lusi Li & Jianqing Chen & Srinivasan Raghunathan, 2018. "Recommender System Rethink: Implications for an Electronic Marketplace with Competing Manufacturers," Information Systems Research, INFORMS, vol. 29(4), pages 1003-1023, December.
    7. Anindya Ghose & Beibei Li & Siyuan Liu, 2019. "Mobile Targeting Using Customer Trajectory Patterns," Management Science, INFORMS, vol. 65(11), pages 5027-5049, November.

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    More about this item

    Keywords

    Recommender system; Collaborative filtering; Add-ons; Pricing; Information externality;
    All these keywords.

    JEL classification:

    • D42 - Microeconomics - - Market Structure, Pricing, and Design - - - Monopoly
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation

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