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Learning from Seller Experiements in Online Markets

Author

Listed:
  • Liran Einav

    (Economics Department, Stanford University)

  • Theresa Kuchler

    (Economics Department, Stanford University)

  • Jonathan Levin

    (Economics Department, Stanford University)

  • Neel Sundaresan

    (eBay Research Labs)

Abstract
The internet has dramatically reduced the cost of varying prices, dis- plays and information provided to consumers, facilitating both active and passive experimentation. We document the prevalence of targeted pricing and auction design variation on eBay, and identify hundreds of thousands of experiments con- ducted by sellers across a wide array of retail products. We show how this type of data can be used to address questions about consumer behavior and market outcomes, and provide illustrative results on price dispersion, the frequency of over-bidding, the choice of reserve prices, ?buy now?options and other auction design parameters, and on consumer sensitivity to shipping fees. We argue that leveraging the experiments of market participants takes advantage of the scale and heterogeneity of online markets and can be a powerful approach for testing and measurement.

Suggested Citation

  • Liran Einav & Theresa Kuchler & Jonathan Levin & Neel Sundaresan, 2011. "Learning from Seller Experiements in Online Markets," Discussion Papers 10-033, Stanford Institute for Economic Policy Research.
    • Handle: RePEc:sip:dpaper:10-033
    as

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    File URL: http://www-siepr.stanford.edu/repec/sip/10-033.pdf
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    References listed on IDEAS

    as
    1. Patrick Bajari & Ali Hortaçsu, 2004. "Economic Insights from Internet Auctions," Journal of Economic Literature, American Economic Association, vol. 42(2), pages 457-486, June.
    2. repec:bla:jindec:v:49:y:2001:i:4:p:541-58 is not listed on IDEAS
    3. Michael R. Baye & John Morgan & Patrick Scholten, 2004. "Price Dispersion In The Small And In The Large: Evidence From An Internet Price Comparison Site," Journal of Industrial Economics, Wiley Blackwell, vol. 52(4), pages 463-496, December.
    4. Steven Anderson & Daniel Friedman & Garrett Milam & Nirvikar Singh, 2004. "Buy it Now: A Hybrid Internet Market Institution," Industrial Organization 0412003, University Library of Munich, Germany.
    5. Michael D. Smith & Erik Brynjolfsson, 2001. "Consumer Decision-making at an Internet Shopbot: Brand Still Matters," NBER Chapters, in: E-commerce, pages 541-558, National Bureau of Economic Research, Inc.
    6. Jennifer Brown & Tanjim Hossain & John Morgan, 2010. "Shrouded Attributes and Information Suppression: Evidence from the Field," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 125(2), pages 859-876.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Takayuki Mizuno & Tsutomu Watanabe, 2013. "Why Are Product Prices in Online Markets Not Converging?," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-7, August.
    2. Liran Einav & Dan Knoepfle & Jonathan Levin & Neel Sundaresan, 2014. "Sales Taxes and Internet Commerce," American Economic Review, American Economic Association, vol. 104(1), pages 1-26, January.
    3. Jonathan Levin, 2011. "The Economics of Internet Markets," Discussion Papers 10-018, Stanford Institute for Economic Policy Research.
    4. Philippe Jehiel & Laurent Lamy, 2015. "On absolute auctions and secret reserve prices," RAND Journal of Economics, RAND Corporation, vol. 46(2), pages 241-270, June.
    5. Liran Einav & Jonathan Levin, 2014. "The Data Revolution and Economic Analysis," Innovation Policy and the Economy, University of Chicago Press, vol. 14(1), pages 1-24.
    6. Nicola Lacetera & Bradley J. Larsen & Devin G. Pope & Justin R. Sydnor, 2016. "Bid Takers or Market Makers? The Effect of Auctioneers on Auction Outcome," American Economic Journal: Microeconomics, American Economic Association, vol. 8(4), pages 195-229, November.
    7. Matthew Backus & Gregory Lewis, 2016. "Dynamic Demand Estimation in Auction Markets," NBER Working Papers 22375, National Bureau of Economic Research, Inc.
    8. Tim Willems, 2017. "Actively Learning by Pricing: A Model of an Experimenting Seller," Economic Journal, Royal Economic Society, vol. 127(604), pages 2216-2239, September.
    9. Wang, Zhongmin & Xu, Minbo, 2013. "Selling a Dollar for More Than a Dollar? Evidence from Online Penny Auctions," RFF Working Paper Series dp-13-15, Resources for the Future.
    10. Benjamin Edelman, 2012. "Using Internet Data for Economic Research," Journal of Economic Perspectives, American Economic Association, vol. 26(2), pages 189-206, Spring.
    11. Liran Einav & Chiara Farronato & Jonathan D. Levin & Neel Sundaresan, 2013. "Sales Mechanisms in Online Markets: What Happened to Internet Auctions?," NBER Working Papers 19021, National Bureau of Economic Research, Inc.
    12. Zhenzhen Yan & Karthik Natarajan & Chung Piaw Teo & Cong Cheng, 2022. "A Representative Consumer Model in Data-Driven Multiproduct Pricing Optimization," Management Science, INFORMS, vol. 68(8), pages 5798-5827, August.

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    More about this item

    JEL classification:

    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • D44 - Microeconomics - - Market Structure, Pricing, and Design - - - Auctions
    • L13 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Oligopoly and Other Imperfect Markets
    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software

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