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Assessing Value in Product Networks

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    Abstract

    Traditionally, the value of a product has been assessed according to the direct revenues the product creates. However, products do not exist in isolation but rather influence one another's sales. Such influence is especially evident in eCommerce environments, where products are often presented as a collection of webpages linked by recommendation hyperlinks, creating a large-scale product network. Here we present the first attempt to use a systematic approach to estimate products' true value to a firm in such a product network. Our approach, which is in the spirit of the PageRank algorithm, uses easily available data from large-scale electronic commerce sites and separates a product’s value into its own intrinsic value, the value it receives from the network, and the value it contributes to the network. We apply this approach to data collected from Amazon.com and from BarnesAndNoble.com. Focusing on one domain of interest, we find that if products are evaluated according to their direct revenue alone, without taking their network value into account, the true value of the "long tail" of electronic commerce may be underestimated, whereas that of bestsellers might be overestimated.

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    File URL: http://www.netinst.org/Carmi_Libai_11_29.pdf
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    Bibliographic Info

    Paper provided by NET Institute in its series Working Papers with number 11-29.

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    Length: 47 pages
    Date of creation: Sep 2011
    Date of revision: Sep 2011
    Handle: RePEc:net:wpaper:1129

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    Web page: http://www.NETinst.org/

    Related research

    Keywords: networks; product networks; electronic commerce; ecommerce; recommender systems; long tail;

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    1. Michaela Draganska & Michael Mazzeo & Katja Seim, 2009. "Beyond plain vanilla: Modeling joint product assortment and pricing decisions," Quantitative Marketing and Economics, Springer, vol. 7(2), pages 105-146, June.
    2. Babur de los Santos, 2008. "Consumer Search on the Internet," Working Papers 2008-06, Indiana University, Kelley School of Business, Department of Business Economics and Public Policy.
    3. Zsolt Katona & Miklos Sarvary, 2008. "Network Formation and the Structure of the Commercial World Wide Web," Marketing Science, INFORMS, vol. 27(5), pages 764-778, 09-10.
    4. Tucker, Catherine & Zhang, Juanjuan, 2007. "Long Tail or Steep Tail? A Field Investigation into How Online Popularity Information Affects the Distribution of Customer Choices," Working papers 39811, Massachusetts Institute of Technology (MIT), Sloan School of Management.
    5. Andres Hervas-Drane, 2007. "Word of Mouth and Taste Matching: A Theory of the Long Tail," Working Papers 07-41, NET Institute, revised Jan 2009.
    6. Chrysanthos Dellarocas & Zsolt Katona & William Rand, 2010. "Media, Aggregators and the Link Economy: Strategic Hyperlink Formation in Content Networks," Working Papers 10-13, NET Institute.
    7. Xavier Gabaix & Rustam Ibragimov, 2007. "Rank-1/2: A Simple Way to Improve the OLS Estimation of Tail Exponents," NBER Technical Working Papers 0342, National Bureau of Economic Research, Inc.
    8. Hinz, Oliver & Eckert, Jochen & Skiera, Bernd, 2011. "Drivers of the Long Tail Phenomenon: An Empirical Analysis," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 56544, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
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