Dynamic pricing with reference price effects

Hu, Zhenyu

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https://hdl.handle.net/2142/87956 Copy

Description

Title

Dynamic pricing with reference price effects

Author(s)

Hu, Zhenyu

Issue Date

2015-06-04

Director of Research (if dissertation) or Advisor (if thesis)

Chen, Xin

Doctoral Committee Chair(s)

Chen, Xin

Committee Member(s)

• Olshevsky, Alex
• Petruzzi, Nicholas C.
• Wang, Qiong

Department of Study

Industrial&Enterprise Sys Eng

Discipline

Industrial Engineering

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• dynamic pricing
• reference price effects

Abstract

This dissertation mainly focuses on the models and the corresponding dynamic pricing problems that incorporate reference price effects, a concept developed in economics and marketing literature that try to capture the dependency of consumers purchasing behavior on past prices. Conceptually, reference price is a price expectation consumers develop from their observations of historical prices. Since it can not be physically observed, various models have been proposed to operationalize its formation. We empirically compare some of the models in the literature and extend the literature by proposing a new reference price model. In addition, we present analysis on the dynamic pricing problems under these models assuming consumers are loss/gain neutral or loss-averse. We find that constant pricing strategies are a robust solution to the problem regardless of which reference price models one may choose. Empirical evidences, however, indicate that loss/gain neutral or loss-averse behavior may not be a universal phenomenon. We analyze the dynamic pricing problem when consumers exhibit gain-seeking behavior. In sharp contrast to the loss-averse case, even myopic pricing strategies can result in complicated cyclic price paths. We show for a special case that a cyclic skimming pricing strategy is optimal and provide conditions to guarantee the optimality of high-low pricing strategies. With the understanding of the qualitative behavior of the optimal pricing strategies under various settings, we develop efficient algorithms to compute the optimal prices in both loss-averse and gain-seeking case. We demonstrate the efficiency and robustness of our algorithms by applying them to a practical problem with real data. Finally, we extend the above considered single-product setting to multi-product setting and analyze the corresponding dynamic pricing problems.

Graduation Semester

2015-8

Type of Resource

text

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http://hdl.handle.net/2142/87956

Copyright and License Information

Copyright 2015 Zhenyu Hu

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