Open Access
May 2006 Evaluating Pricing Strategy Using e-Commerce Data: Evidence and Estimation Challenges
Anindya Ghose, Arun Sundararajan
Statist. Sci. 21(2): 131-142 (May 2006). DOI: 10.1214/088342306000000187


As Internet-based commerce becomes increasingly widespread, large data sets about the demand for and pricing of a wide variety of products become available. These present exciting new opportunities for empirical economic and business research, but also raise new statistical issues and challenges. In this article, we summarize research that aims to assess the optimality of price discrimination in the software industry using a large e-commerce panel data set gathered from We describe the key parameters that relate to demand and cost that must be reliably estimated to accomplish this research successfully, and we outline our approach to estimating these parameters. This includes a method for “reverse engineering” actual demand levels from the sales ranks reported by Amazon, and approaches to estimating demand elasticity, variable costs and the optimality of pricing choices directly from publicly available e-commerce data. Our analysis raises many new challenges to the reliable statistical analysis of e-commerce data and we conclude with a brief summary of some salient ones.


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Anindya Ghose. Arun Sundararajan. "Evaluating Pricing Strategy Using e-Commerce Data: Evidence and Estimation Challenges." Statist. Sci. 21 (2) 131 - 142, May 2006.


Published: May 2006
First available in Project Euclid: 7 August 2006

zbMATH: 05191855
MathSciNet: MR2324073
Digital Object Identifier: 10.1214/088342306000000187

Keywords: electronic commerce , price discrimination , pricing strategy , quality differentiation , sales rank , versioning

Rights: Copyright © 2006 Institute of Mathematical Statistics

Vol.21 • No. 2 • May 2006
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