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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/14114
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| Title: | Evaluating Pricing and Product Line Strategy Using eCommerce Data:
Evidence and Estimation Challenges |
| Authors: | Ghose, Anindya
Sundararajan, Arun |
| Issue Date: | 15-Oct-2005 |
| Publisher: | Stern School of Business, New York University |
| Series/Report no.: | CeDER-05-24 |
| 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 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
Amazon.com. 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. |
| URI: | http://hdl.handle.net/2451/14114 |
| Appears in Collections: | CeDER Working Papers
IOMS: Information Systems Working Papers
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