Dynamic Pricing Through Data Sampling

46 Pages Posted: 10 Jan 2014 Last revised: 1 Dec 2017

See all articles by Maxime Cohen

Maxime Cohen

McGill University

Ruben Lobel

University of Pennsylvania - Operations & Information Management Department

Georgia Perakis

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

Date Written: May 20, 2013

Abstract

We study a dynamic pricing problem, where a firm offers a product to be sold over a fixed time horizon. The firm has a given initial inventory level, but there is uncertainty about the demand for the product in each time period. The objective of the firm is to determine a dynamic pricing strategy that maximizes revenue throughout the entire selling season. We develop a tractable optimization model that directly uses demand data, therefore creating a practical decision tool. We show computationally that regret-based objectives can perform well when compared to average revenue maximization and to a Bayesian approach. The modeling approach proposed in this paper could be particularly useful for risk-averse managers with limited access to historical data or information about the true demand distribution. Finally, we provide theoretical performance guarantees for this sampling-based solution.

Keywords: dynamic pricing, data-driven, sampling based optimization

Suggested Citation

Cohen, Maxime and Lobel, Ruben and Perakis, Georgia, Dynamic Pricing Through Data Sampling (May 20, 2013). Available at SSRN: https://ssrn.com/abstract=2376667 or http://dx.doi.org/10.2139/ssrn.2376667

Maxime Cohen (Contact Author)

McGill University ( email )

1001 Sherbrooke St. W
Montreal, Quebec H3A 1G5
Canada

Ruben Lobel

University of Pennsylvania - Operations & Information Management Department ( email )

Philadelphia, PA 19104
United States

Georgia Perakis

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

100 Main Street
E62-565
Cambridge, MA 02142
United States

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