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Evaluating Pricing Strategy Using e-Commerce Data: Evidence and Estimation Challenges

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  • Anindya Ghose
  • Arun Sundararajan

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.

Suggested Citation

  • Anindya Ghose & Arun Sundararajan, 2006. "Evaluating Pricing Strategy Using e-Commerce Data: Evidence and Estimation Challenges," Papers math/0609170, arXiv.org.
  • Handle: RePEc:arx:papers:math/0609170
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    File URL: http://arxiv.org/pdf/math/0609170
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    References listed on IDEAS

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    1. Erik Brynjolfsson & Yu (Jeffrey) Hu & Michael D. Smith, 2003. "Consumer Surplus in the Digital Economy: Estimating the Value of Increased Product Variety at Online Booksellers," Management Science, INFORMS, vol. 49(11), pages 1580-1596, November.
    2. Arun Sundararajan, 2003. "Managing Digital Piracy: Pricing, Protection and Welfare," Law and Economics 0307001, EconWPA.
    3. Yannis Bakos & Erik Brynjolfsson, 1999. "Bundling Information Goods: Pricing, Profits, and Efficiency," Management Science, INFORMS, vol. 45(12), pages 1613-1630, December.
    4. Jerry A. Hausman, 1996. "Valuation of New Goods under Perfect and Imperfect Competition," NBER Chapters,in: The Economics of New Goods, pages 207-248 National Bureau of Economic Research, Inc.
    5. Anindya Ghose & Vidyanand Choudhary & Tridas Mukhopadhyay & Uday Rajan, 2002. "Personalized Pricing and Quality Differentiation on the Internet," Review of Marketing Science Working Papers 2-1-1005, Berkeley Electronic Press.
    6. Arun Sundararajan, 2003. "Nonlinear pricing of information goods," Industrial Organization 0307003, EconWPA.
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    Cited by:

    1. Nikolay Archak & Anindya Ghose & Panagiotis G. Ipeirotis, 2007. "Deriving the Pricing Power of Product Features by Mining Consumer Reviews," Working Papers 07-36, NET Institute.
    2. Nikolay Archak & Anindya Ghose & Panagiotis G. Ipeirotis, 2011. "Deriving the Pricing Power of Product Features by Mining Consumer Reviews," Management Science, INFORMS, vol. 57(8), pages 1485-1509, August.
    3. Chris Forman & Anindya Ghose & Avi Goldfarb, 2006. "Geography and Electronic Commerce: Measuring Convenience, Selection, and Price," Working Papers 06-15, NET Institute, revised Sep 2006.
    4. Kretschmer, Tobias & Peukert, Christian, 2014. "Video killed the radio star? Online music videos and digital music sales," LSE Research Online Documents on Economics 60276, London School of Economics and Political Science, LSE Library.
    5. Rustam Ibragimov & Johan Walden, 2010. "Optimal Bundling Strategies Under Heavy-Tailed Valuations," Management Science, INFORMS, vol. 56(11), pages 1963-1976, November.

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