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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/27743
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| Title: | Evaluating Pricing Strategy Using e-Commerce Data: Evidence and
Estimation Challenges |
| Authors: | Sundararajan, Arun
Ghose, Anindya |
| Keywords: | Electronic commerce
pricing strategy
price discrimination
versioning
quality differentiation
sales rank |
| Issue Date: | 6-Nov-2008 |
| Series/Report no.: | CeDER-PP-2006-05 |
| 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 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. |
| URI: | http://hdl.handle.net/2451/27743 |
| Appears in Collections: | CeDER Published Papers
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