Signaling in Online Retail: Efficacy of Public Signals

29 Pages Posted: 26 May 2018 Last revised: 5 Aug 2018

See all articles by David Lingenbrink

David Lingenbrink

Cornell University

Krishnamurthy Iyer

University of Minnesota - Twin Cities - Department of Industrial and Systems Engineering

Date Written: May 15, 2018

Abstract

We consider the problem of inventory and demand signaling in online retail. Customers seek to purchase an item with a finite inventory in one of two time periods. They are strategic and Bayesian but do not know the current inventory or number of other customers. The firm knows these values and seeks to persuade customers to buy in the first time period where the price is higher. We adopt the framework of Bayesian persuasion to formulate and analyze the firm's choice of a revenue-optimal signaling mechanism. We observe that the randomness in the number of customers causes each customer to believe the demand to be stochastically higher. Taking this into consideration, we establish that when customers are homogeneous, the optimal signaling mechanism is a public mechanism where all customers are provided the same information. In particular, the firm does not benefit from providing different information to different customers, such as through personalized email marketing. We exploit the public structure of the optimal mechanism to cast the firm's problem as a fractional knapsack problem. We support our analytical results with numerical computations that show public signaling achieving substantial revenue gains over no information or full information sharing.

Keywords: inventory signaling, information design

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JEL Classification: D40, D82, D83

Suggested Citation

Lingenbrink, David and Iyer, Krishnamurthy, Signaling in Online Retail: Efficacy of Public Signals (May 15, 2018). Available at SSRN: https://ssrn.com/abstract=3179262 or http://dx.doi.org/10.2139/ssrn.3179262

David Lingenbrink

Cornell University ( email )

Ithaca, NY 14853
United States

Krishnamurthy Iyer (Contact Author)

University of Minnesota - Twin Cities - Department of Industrial and Systems Engineering ( email )

111 Church St SE
Minneapolis, MN 55455
United States

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