Evaluating Pricing Strategy Using e-Commerce Data: Evidence andEstimation Challenges

As Internet-based commerce becomes increasingly widespread, large datasets about the demand for and pricing of a wide variety of productsbecome available. These present exciting new opportunities for empiricaleconomic and business research, but also raise new statistical issuesand challenges. In this article, we summarize research that aims toassess the optimality of price discrimination in the software industryusing a large e-commerce panel data set gathered from Amazon.com. Wedescribe the key parameters that relate to demand and cost that must bereliably estimated to accomplish this research successfully, and weoutline our approach to estimating these parameters. This includes amethod for “reverse engineering” actual demand levels fromthe sales ranks reported by Amazon, and approaches to estimating demandelasticity, variable costs and the optimality of pricing choicesdirectly from publicly available e-commerce data. Our analysis raisesmany new challenges to the reliable statistical analysis of e-commercedata and we conclude with a brief summary of some salient ones.