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Position Ranking and Auctions for Online Marketplaces

Author

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  • Leon Yang Chu

    (University of Southern California, Los Angeles, Los Angeles, California 90089)

  • Hamid Nazerzadeh

    (University of Southern California, Los Angeles, Los Angeles, California 90089)

  • Heng Zhang

    (University of Southern California, Los Angeles, Los Angeles, California 90089)

Abstract

Online e-commerce platforms, such as Amazon and Taobao, connect thousands of sellers and consumers every day. In this work, we study how such platforms should rank products displayed to consumers and utilize the top and most salient slots. We present a model that considers consumers’ search costs and the externalities sellers impose on each other. This model allows us to study a multiobjective optimization, whose objective includes consumer and seller surplus as well as the sales revenue, and derive the optimal ranking decision. In addition, we propose a surplus-ordered ranking mechanism for selling some of the top slots. This mechanism is motivated in part by Amazon’s sponsored search program. We show that the Vickrey–Clarke–Groves mechanism would not be applicable to our setting and propose a new mechanism. This mechanism is near optimal, performing significantly better than those that do not incentivize sellers to reveal their private information regarding each consumer purchase, such as their profit. Moreover, we generalize our model to settings in which platforms can provide partial information about the products and facilitate the consumer search and show the robustness of our findings.

Suggested Citation

  • Leon Yang Chu & Hamid Nazerzadeh & Heng Zhang, 2020. "Position Ranking and Auctions for Online Marketplaces," Management Science, INFORMS, vol. 66(8), pages 3617-3634, August.
    • Handle: RePEc:inm:ormnsc:v:66:y:2020:i:8:p:3617-3634
    DOI: 10.1287/mnsc.2019.3372
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    References listed on IDEAS

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    Cited by:

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    2. Dipankar Das, 2023. "A Model of Competitive Assortment Planning Algorithm," Papers 2307.09479, arXiv.org.
    3. Santiago R. Balseiro & Antoine Désir, 2023. "Incentive-Compatible Assortment Optimization for Sponsored Products," Management Science, INFORMS, vol. 69(8), pages 4668-4684, August.
    4. Tino Werner, 2023. "Quantitative robustness of instance ranking problems," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 75(2), pages 335-368, April.
    5. Tino Werner, 2022. "Elicitability of Instance and Object Ranking," Decision Analysis, INFORMS, vol. 19(2), pages 123-140, June.
    6. Chen Jin & Luyi Yang & Kartik Hosanagar, 2023. "To Brush or Not to Brush: Product Rankings, Consumer Search, and Fake Orders," Information Systems Research, INFORMS, vol. 34(2), pages 532-552, June.
    7. Wenjia Ba & Haim Mendelson & Mingxi Zhu, 2020. "Sales Policies for a Virtual Assistant," Papers 2009.03719, arXiv.org.
    8. Alison Watts, 2021. "Fairness and Efficiency in Online Advertising Mechanisms," Games, MDPI, vol. 12(2), pages 1-11, April.
    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.
    10. Rafael P. Greminger, 2022. "Heterogeneous Position Effects and the Power of Rankings," Papers 2210.16408, arXiv.org, revised Dec 2023.
    11. Shen, Bin & Xu, Xiaoyan & Yuan, Quan, 2020. "Selling secondhand products through an online platform with blockchain," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 142(C).
    12. Ying‐Ju Chen, 2021. "Optimal Design of Revenue‐Maximizing Position Auctions with Consumer Search," Production and Operations Management, Production and Operations Management Society, vol. 30(9), pages 3297-3316, September.

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