An Empirical Analysis of Price Discrimination Mechanisms and Retailer Profitability

Journal of Marketing Research (JMR)

An Empirical Analysis of Price Discrimination Mechanisms and Retailer Profitability

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Published 11/1/2005

Author: Romana J. Khan and Dipak C. Jain

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Executive Summary
Setting prices to consumers is one of the most critical decisions for the retailer and a primary driver of retailer profitability. To increase profitability, retailers typically engage in some form of price discrimination. In this article, the authors compare the impact on retailer profitability from two price discrimination mechanisms: quantity discounts based on package size (second-degree price discrimination) and store-level pricing or micromarketing (third-degree price discrimination). Although the latter has been well addressed in the marketing literature, there is limited empirical research on the use of quantity discounts for price discrimination.

Using store-level sales data, the authors estimate a structural demand model, accounting for parameter heterogeneity and price endogeneity. They combine the parameter estimates with a model of retailer pricing to conduct optimal pricing and profitability simulations under several scenarios, ranging from constraining the retailer not to engage in any form of price discrimination to the least restrictive scenario of setting nonlinear price schedules specific to each store. The pricing simulations enable the decomposition of profitability due to the different forms of price discrimination. Profits are highest when retailers combine second- and third-degree price discrimination. The authors find that the ability to engage in second-degree price discrimination contributes more to retailer profitability than third-degree price discrimination.

In practical terms, setting a quantity discount schedule at the chain level offers a significant increase in profits and is also much easier to implement than setting store-specific prices. The results suggest that managers can focus on the simpler task of setting quantity discount schedules to increase profits before investing in store-level pricing systems. Although the combination of store-level pricing with quantity discounts contributes significant incremental profits, the complications involved in implementing store-level pricing are great.

Biography
Romana J. Khan is Assistant Professor of Marketing in the McCombs School of Business at the University of Texas at Austin. She received her doctoral degree from Kellogg School of Management at Northwestern University, her master’s degree from Iowa State University, and her bachelor’s degree from Swarthmore College. Her areas of research interest include pricing, price discrimination strategies, and issues related to database marketing.

Dipak C. Jain is Dean of the Kellogg School of Management, Sandy and Morton Goldman Professor in Entrepreneurial Studies, and Professor of Marketing. He received his doctoral degree in Marketing from the University of Texas at Dallas and his master’s degree in Mathematical Statistics from Gauhati University in India. His areas of research include the marketing of high-tech products, market segmentation and competitive market structure analysis, cross-cultural issues in global product diffusion, new product diffusion, and forecasting models. He has had more than 30 articles published in leading academic journals. He teaches courses on marketing research, new products and services, and probabilistic and statistical models in marketing. He has served as Departmental Editor for the Management Science, Area Editor for Marketing Science, Associate Editor for Journal of Business and Economic Statistics, and as a member of the editorial board of Journal of Marketing Research. In 2003, he was appointed as a foreign affairs adviser for the Prime Minister of Thailand. He also serves as a member of the board of directors of Evanston Northwestern Healthcare, Hartmarx Corporation, John Deere and Company, Northern Trust Corporation, Peoples Energy, and United Airlines.

J Marketing Research, Volume 42, Number 4, November 2005
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Journal of Marketing Research (JMR)