Evaluating pricing strategy using e-commerce data: evidence and estimation challenges. (English) Zbl 1426.62371

Summary: 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 Amazon.com. 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.


62P20 Applications of statistics to economics
91B24 Microeconomic theory (price theory and economic markets)
Full Text: DOI arXiv Euclid


[1] Aron, R., Sundararajan, A. and Viswanathan, S. (2006). Intelligent agents in electronic markets for information goods: Customization, preference revelation and pricing. Decision Support Systems 41 764–786.
[2] Bakos, Y. and Brynjolfsson, E. (1999). Bundling information goods: Pricing, profits and efficiency. Management Sci. 45 1613–1630. · Zbl 1231.91296
[3] Bapna, R., Jank, W. and Shmueli, G. (2004). Price formation and its dynamics in online auctions. Working paper, Smith School of Business, Univ. Maryland. Available at www.smith.umd.edu/faculty/wjank/auctionDynamics.pdf.
[4] Brynjolfsson, E., Hu, Y. and Smith, M. (2003). Consumer surplus in the digital economy: Estimating the value of increased product variety at online booksellers. Management Sci. 49 1580–1596.
[5] Chevalier, J. and Goolsbee, A. (2003). Measuring prices and price competition online: Amazon.com and Barnes and Noble.com. Quantitative Marketing and Economics 1 203–222.
[6] Choudhary, V., Ghose, A., Mukhopadhyay, T. and Rajan, U. (2005). Personalized pricing and quality differentiation. Management Sci. 51 1120–1130.
[7] Ghose, A., Huang, K. and Sundararajan, A. (2005). Versions, successive generations and pricing strategies in software markets: Theory and evidence. Working paper, Stern School of Business, New York Univ.
[8] Ghose, A., Smith, M. and Telang, R. (2006). Internet exchanges for used books: An empirical analysis of product cannibalization and welfare impact. Information Systems Research 17 3–19.
[9] Ghose, A. and Sundararajan, A. (2005a). Software versioning and quality degradation? An exploratory study of the evidence. Working Paper CeDER-05-20, Stern School of Business, New York Univ.
[10] Ghose, A. and Sundararajan, A. (2005b). Pricing security software: Theory and evidence. In Proc. Fourth Workshop on the Economics of Information Security . Harvard Univ.
[11] Ghose, A., Telang, R. and Krishnan, R. (2005). Effect of electronic secondary markets on the supply chain. J. Management Information Systems 22 (2) 91–120.
[12] Greene, W. H. (2000). Econometric Analysis , 4th ed. Prentice-Hall, Upper Saddle River, NJ.
[13] Hausman, J. (1994). Valuation of new goods under perfect and imperfect competition. Working paper 4970, National Bureau Economic Research.
[14] Jank, W. and Shmueli, G. (2006). Functional data analysis in electronic commerce research. Statist. Sci. 21 155–166. · Zbl 1426.62375
[15] Mas-Collel, A., Whinston, M. and Green, J. (1995). Microeconomic Theory. Oxford Univ. Press, New York. · Zbl 1256.91002
[16] Pareto, V. (1896/1897). Cours d’économie politique professé a l’Université de Lausanne 1 , 2 . F. Rouge, Lausanne.
[17] Quandt, R. E. (1964). Statistical discrimination among alternative hypotheses and some economic regularities. J. Regional Sci. 5 1–23.
[18] Sundararajan, A. (2004a). Managing digital piracy: Pricing and protection. Information Systems Research 15 287–308.
[19] Sundararajan, A. (2004b). Nonlinear pricing of information goods. Management Sci. 50 1660–1673. · Zbl 1232.91445
[20] Wooldridge, J. (2002). Econometric Analysis of Cross Section and Panel Data . MIT Press, Cambridge, MA. · Zbl 1441.62010
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.