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Designing an Online Retail Marketplace: Leveraging Information from Sponsored Advertising

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  • Fei Long

    (Kenan-Flagler Business School, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599)

  • Kinshuk Jerath

    (Columbia Business School, Columbia University, New York, New York 10027)

  • Miklos Sarvary

    (Columbia Business School, Columbia University, New York, New York 10027)

Abstract

E-commerce platforms, such as Amazon and Alibaba, enable hundreds of millions of consumers to search and purchase products offered by millions of independent sellers who can advertise their products on the platforms. In this paper, we study how to design such a marketplace when there are two types of asymmetric information—sellers have some private information about their products that the platform does not have, whereas the platform has private information about consumers that the sellers do not have. Using a game theory model, we formulate the marketplace design problem as maximizing marketplace profit (comprising ad revenues and sales commissions) by jointly considering the following elements: ensuring sellers join the platform, specifying the auction that enables sellers to advertise in the sponsored list, leveraging the information revealed in the ad auction to refine the organic list, and setting the commission rate on sales to consumers who are rational and heterogeneous in their preferences. We show that sellers’ bids in ad auctions, through which sponsored slots are allocated, can reveal the sellers’ private information to the platform (“information effect”), which it can optimally combine with information that it has about consumers to improve the placement of organic results, a practice we call “strategic listing.” However, by introducing an externality between the sponsored and organic sides, strategic listing also leads to more competition in the ad auction (“competition effect”), thus reducing the incentive of sellers to join the platform. The platform can incentivize sellers to join by reducing the commission rate on sales; however, under certain conditions, the platform must also reduce the degree of strategic listing (i.e., commit to limiting the influence of the sponsored ad auction outcomes on the placement of organic results). If the sellers’ participation is sufficiently difficult to induce, the platform obtains a larger proportion (under some conditions, all) of its revenue from advertising than from sales commissions. Our results shed light on the variation in practices across different platforms and provide timely guidance for platforms to refine their marketplace design.

Suggested Citation

  • Fei Long & Kinshuk Jerath & Miklos Sarvary, 2022. "Designing an Online Retail Marketplace: Leveraging Information from Sponsored Advertising," Marketing Science, INFORMS, vol. 41(1), pages 115-138, January.
    • Handle: RePEc:inm:ormksc:v:41:y:2022:i:1:p:115-138
    DOI: 10.1287/mksc.2021.1307
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    References listed on IDEAS

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    Cited by:

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    4. Joonhyuk Yang & Navdeep S. Sahni & Harikesh S. Nair & Xi Xiong, 2024. "Advertising as Information for Ranking E-Commerce Search Listings," Marketing Science, INFORMS, vol. 43(2), pages 360-377, March.
    5. Patrick Ding & Guido Imbens & Zhaonan Qu & Yinyu Ye, 2024. "Computationally Efficient Estimation of Large Probit Models," Papers 2407.09371, arXiv.org, revised Sep 2024.
    6. Hu, Li & Zhang, Weiyi & Du, Shaofu & Sun, Xiahui, 2025. "How to achieve targeted advertising with the e-commerce platform’s membership system?," Omega, Elsevier, vol. 130(C).
    7. Fei Long & Yunchuan Liu, 2024. "Platform Manipulation in Online Retail Marketplace with Sponsored Advertising," Marketing Science, INFORMS, vol. 43(2), pages 317-345, March.
    8. W. Jason Choi & Amin Sayedi, 2024. "Agency Market Power and Information Disclosure in Online Advertising," Marketing Science, INFORMS, vol. 43(6), pages 1279-1298, November.
    9. Margarida V. B. Santos & Isabel Mota & Pedro Campos, 2023. "Analysis of online position auctions for search engine marketing," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(3), pages 409-425, September.

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