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dc.contributor.authorGhose, Anindya-
dc.contributor.authorSundararajan, Arun-
dc.description.abstractAs 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 a program of research that aims to assess the optimality of price discrimination in the software industry using a large ecommerce panel data set gathered from We describe the 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 and variable costs 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.en
dc.format.extent241848 bytes-
dc.publisherStern School of Business, New York Universityen
dc.titleEvaluating Pricing and Product Line Strategy Using eCommerce Data: Evidence and Estimation Challengesen
dc.typeWorking Paperen
dc.description.seriesInformation Systems Working Papers SeriesEN
Appears in Collections:CeDER Working Papers
IOMS: Information Systems Working Papers

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