Position ranking and auctions for online marketplaces

Leon Yang Chu, Hamid Nazerzadeh, Heng Zhang

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)3617-3634
Number of pages18
JournalManagement Science
Issue number8
StatePublished - Aug 2020
Externally publishedYes


  • Consumer search
  • E-commerce platforms
  • Mechanism design
  • Position auctions
  • Seller externalities
  • Social surplus maximization

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research


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