Data-Driven Revenue Management: The Interplay of Data, Model, and Decision

29 Pages Posted: 18 Jan 2023

See all articles by Ningyuan Chen

Ningyuan Chen

University of Toronto at Mississauga - Department of Management; University of Toronto - Rotman School of Management

Ming Hu

University of Toronto - Rotman School of Management

Date Written: January 16, 2023

Abstract

Revenue management (RM) is the application of analytical methodologies and tools that predict consumer behavior and optimize products' availability and prices to maximize a firm's revenue or profit. In the last decade, data has been playing an increasingly crucial role in business decision-making. As firms rely more on collected or acquired data to make business decisions, it brings opportunities and challenges to the RM research community. In this review paper, we systematically categorize the related literature by how a study is "driven" by data and focus on studies that explore the interplay between two or three of the elements: data, model, and decision, in which the data element must be present. Specifically, we cover five data-driven RM research areas, including inference (data to model), predict-then-optimize (data to model to decision), online learning (data to model to decision to new data in a loop), end-to-end decision making (data directly to decision), and experimental design (decision to data to model). Finally, we point out future research directions.

Suggested Citation

Chen, Ningyuan and Hu, Ming, Data-Driven Revenue Management: The Interplay of Data, Model, and Decision (January 16, 2023). Available at SSRN: https://ssrn.com/abstract=4324972 or http://dx.doi.org/10.2139/ssrn.4324972

Ningyuan Chen (Contact Author)

University of Toronto at Mississauga - Department of Management ( email )


Canada

University of Toronto - Rotman School of Management ( email )

105 St. George st
Toronto, ON M5S 3E6
Canada

Ming Hu

University of Toronto - Rotman School of Management ( email )

105 St. George st
Toronto, ON M5S 3E6
Canada
416-946-5207 (Phone)

HOME PAGE: http://ming.hu

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