Product Ranking on Online Platforms

58 Pages Posted: 6 Mar 2018 Last revised: 29 Apr 2020

See all articles by Mahsa Derakhshan

Mahsa Derakhshan

University of Maryland

Negin Golrezaei

Massachusetts Institute of Technology (MIT) - Sloan School of Management

Vahideh Manshadi

Yale School of Management

Vahab Mirrokni

Google Inc.

Date Written: February 26, 2018

Abstract

On online platforms, consumers face an abundance of options that are displayed in the form of a position ranking. Only products placed in the first few positions are readily accessible to the consumer, and she needs to exert effort to access more options. For such platforms, we develop a two-stage sequential search model where in the first stage, the consumer sequentially screens positions to observe the preference weight of the products placed in them and forms a consideration set. In the second stage, she observes the additional idiosyncratic utility that she can derive from each product and chooses the highest-utility product within her consideration set. For this model, we first characterize the optimal sequential search policy of a welfare-maximizing consumer. We then study how platforms with different objectives should rank products. We focus on two objectives: (i) maximizing the platform’s market share and (ii) maximizing the consumer’s welfare. Somewhat surprisingly, we show that ranking products in decreasing order of their preference weights does not necessarily maximize market share or consumer welfare. Such a ranking may shorten the consumer’s consideration set due to the externality effect of high-positioned products on low-positioned ones, leading to insufficient screening. We then show that both problems—maximizing market share and maximizing consumer welfare—are NP-complete. We develop novel near-optimal polynomial-time ranking algorithms for each objective. Further, we show that even though ranking products in decreasing order of their preference weights is suboptimal, such a ranking enjoys strong performance guarantees for both objectives. We complement our theoretical developments with numerical studies using synthetic data in which we show (1) that heuristic versions of our algorithms that do not rely on model primitives perform well and (2) that our model can be effectively estimated using a maximum likelihood estimator.

Keywords: Two-Stage Consumer Search, Product Ranking, Search Costs, Online Platforms

Suggested Citation

Derakhshan, Mahsa and Golrezaei, Negin and Manshadi, Vahideh and Mirrokni, Vahab, Product Ranking on Online Platforms (February 26, 2018). Available at SSRN: https://ssrn.com/abstract=3130378 or http://dx.doi.org/10.2139/ssrn.3130378

Mahsa Derakhshan

University of Maryland ( email )

College Park
College Park, MD 20742
United States

Negin Golrezaei

Massachusetts Institute of Technology (MIT) - Sloan School of Management ( email )

100 Main Street
E62-416
Cambridge, MA 02142
United States
02141 (Fax)

Vahideh Manshadi (Contact Author)

Yale School of Management ( email )

135 Prospect Street
P.O. Box 208200
New Haven, CT 06520-8200
United States

Vahab Mirrokni

Google Inc. ( email )

1600 Amphitheatre Parkway
Second Floor
Mountain View, CA 94043
United States

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