Evaluating Pricing Strategy Using Ecommerce Data: Evidence and Estimation Challenges

28 Pages Posted: 7 Apr 2006

See all articles by Anindya Ghose

Anindya Ghose

New York University (NYU) - Leonard N. Stern School of Business

Arun Sundararajan

NYU Stern School of Business; New York University (NYU) - Center for Data Science

Date Written: April 3, 2006

Abstract

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 ecommerce panel data set gathered from Amazon.com. We describe the key parameters relating to demand and cost that must be reliably estimated in order to successfully accomplish this research, and 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 ecommerce data. Our analysis raises many new challenges to the reliable statistical analysis of ecommerce data, and we conclude with a brief summary of some salient ones.

Keywords: Electronic commerce, Pricing strategy, Price discrimination, Versioning,

JEL Classification: C23, C40, C80, D43, M31, L11, L86

Suggested Citation

Ghose, Anindya and Sundararajan, Arun, Evaluating Pricing Strategy Using Ecommerce Data: Evidence and Estimation Challenges (April 3, 2006). Available at SSRN: https://ssrn.com/abstract=895188 or http://dx.doi.org/10.2139/ssrn.895188

Anindya Ghose (Contact Author)

New York University (NYU) - Leonard N. Stern School of Business ( email )

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Arun Sundararajan

NYU Stern School of Business ( email )

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United States

HOME PAGE: http://digitalarun.ai/

New York University (NYU) - Center for Data Science ( email )

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