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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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    References listed on IDEAS

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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. Linyi Li & Shyam Gopinath & Stephen J. Carson, 2022. "History Matters: The Impact of Online Customer Reviews Across Product Generations," Management Science, INFORMS, vol. 68(5), pages 3878-3903, May.
    3. 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.
    4. Rustam Ibragimov & Johan Walden, 2010. "Optimal Bundling Strategies Under Heavy-Tailed Valuations," Management Science, INFORMS, vol. 56(11), pages 1963-1976, November.
    5. 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.
    6. Anna Ye Du & Sanjukta Das & Ram D. Gopal & Ram Ramesh, 2014. "Optimal Management of Digital Content on Tiered Infrastructure Platforms," Information Systems Research, INFORMS, vol. 25(4), pages 730-746, December.
    7. John Aloysius & Cary Deck & Amy Farmer, 2013. "Sequential Pricing of Multiple Products: Leveraging Revealed Preferences of Retail Customers Online and with Auto-ID Technologies," Information Systems Research, INFORMS, vol. 24(2), pages 372-393, June.
    8. Octavian Carare, 2012. "The Impact Of Bestseller Rank On Demand: Evidence From The App Market," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 53(3), pages 717-742, August.
    9. Wang, Xia & Ding, Ying, 2022. "The impact of monetary rewards on product sales in referral programs: The role of product image aesthetics," Journal of Business Research, Elsevier, vol. 145(C), pages 828-842.
    10. 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.

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