Invited Session Tue.1.MA 043

Tuesday, 10:30 - 12:00 h, Room: MA 043

Cluster 8: Game theory [...]

Game-theoretic models in operations

 

Chair: Ilan Lobel

 

 

Tuesday, 10:30 - 10:55 h, Room: MA 043, Talk 1

Ilan Lobel
Intertemporal price discrimination: Structure and computation of optimal policies

Coauthor: Omar Besbes

 

Abstract:
We consider the problem of a firm selling goods over time to customers with heterogeneous patience levels. We let customer valuations be correlated with their willingness-to-wait and look for a dynamic pricing policy that maximizes the long-term revenue of the firm. We prove that the optimal pricing policy is composed of cycles with a period that is at most twice the maximum willingness-to-wait. We also prove that the prices typically follow a nonmonotonic cyclic behavior. Finally, we show that optimizing over dynamic pricing policies can be accomplished in time that is polynomial on the maximum willingness-to-wait among all customers.

 

 

Tuesday, 11:00 - 11:25 h, Room: MA 043, Talk 2

Hamid Nazerzadeh
Buy-it-now or take-a-chance: A mechanism for real-time price discrimination

 

Abstract:
I present a simple sequential mechanism to allocated online
advertisement space. The mechanism is motivated by increasingly
sophisticated
consumer tracking technology that allow advertisers to reach narrowly
targeted consumer demographics.
Such targeting enhances advertising efficiency by improving the
matching quality between advertisers and users, but can also result in
thin markets for particular demographic groups.

 

 

Tuesday, 11:30 - 11:55 h, Room: MA 043, Talk 3

Georgia Perakis
Markdown optimization for a fashion e-tailer: The impact of returning customers

Coauthors: Pavithra Harsha, Zachary Leung

 

Abstract:
We study a model for markdown optimization, i.e., how to set prices to maximize revenues from selling a fashion good in the context of an e-tailer. Due to the convenience of Internet shopping, a significant proportion of customers may wait for the price of a fashion item to decrease, strategically returning multiple times to check on the price. This is an important issue that e-tailers need to account for when pricing their products. In this talk, we propose a model that incorporates returning customer behavior. We focus on the case of a monopolist e-tailer selling a single product over a finite horizon. For classes of demand functions, we develop convex reformulations that are tractable. We derive general insights on pricing strategies in the presence of returning customers, We compare the prices and revenue of a myopic pricing policy, which treats returning customers the same as first-time customers, to the optimal pricing policy. This allows us to estimate the value of smart pricing.

 

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