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Title:Dynamic pricing with reference price effects
Author(s):Hu, Zhenyu
Director of Research:Chen, Xin
Doctoral Committee Chair(s):Chen, Xin
Doctoral Committee Member(s):Olshevsky, Alex; Petruzzi, Nicholas C.; Wang, Qiong
Department / Program:Industrial&Enterprise Sys Eng
Discipline:Industrial Engineering
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(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.
Issue Date:2015-06-04
Type:Thesis
URI:http://hdl.handle.net/2142/87956
Rights Information:Copyright 2015 Zhenyu Hu
Date Available in IDEALS:2015-09-29
Date Deposited:August 201


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