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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.

Suggested Citation

  • Gal OEstreicher-Singer & Barak Libai, 2011. "Assessing Value in Product Networks," Working Papers 11-29, NET Institute, revised Sep 2011.
    • Handle: RePEc:net:wpaper:1129
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    File URL: http://www.netinst.org/Carmi_Libai_11_29.pdf
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    References listed on IDEAS

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    1. Gérard P. Cachon & Christian Terwiesch & Yi Xu, 2008. "On the Effects of Consumer Search and Firm Entry in a Multiproduct Competitive Market," Marketing Science, INFORMS, vol. 27(3), pages 461-473, 05-06.
    2. 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.
    3. Daniel Fleder & Kartik Hosanagar, 2009. "Blockbuster Culture's Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity," Management Science, INFORMS, vol. 55(5), pages 697-712, May.
    4. Xavier Gabaix & Rustam Ibragimov, 2011. "Rank - 1 / 2: A Simple Way to Improve the OLS Estimation of Tail Exponents," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(1), pages 24-39, January.
    5. Erik Brynjolfsson & Yu (Jeffrey) Hu & Mohammad S. Rahman, 2009. "Battle of the Retail Channels: How Product Selection and Geography Drive Cross-Channel Competition," Management Science, INFORMS, vol. 55(11), pages 1755-1765, November.
    6. 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).
    7. Charles F. Manski, 2000. "Economic Analysis of Social Interactions," Journal of Economic Perspectives, American Economic Association, vol. 14(3), pages 115-136, Summer.
    8. 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.
    9. Wendy W. Moe & Peter S. Fader, 2004. "Dynamic Conversion Behavior at E-Commerce Sites," Management Science, INFORMS, vol. 50(3), pages 326-335, March.
    10. 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.
    11. Michaela Draganska & Michael Mazzeo & Katja Seim, 2009. "Beyond plain vanilla: Modeling joint product assortment and pricing decisions," Quantitative Marketing and Economics (QME), Springer, vol. 7(2), pages 105-146, June.
    12. 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.
    13. 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.
    14. James D. Hess & Eitan Gerstner, 1987. "Loss Leader Pricing and Rain Check Policy," Marketing Science, INFORMS, vol. 6(4), pages 358-374.
    15. De los Santos, Babur, 2018. "Consumer search on the Internet," International Journal of Industrial Organization, Elsevier, vol. 58(C), pages 66-105.
    16. Hinz, Oliver & Eckert, Jochen & Skiera, Bernd, 2011. "Drivers of the Long Tail Phenomenon: An Empirical Analysis, Journal of Management Information Systems (JMIS), 27 (4), 43-69," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 63391, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    17. S. Sriram & Pradeep K. Chintagunta & Manoj K. Agarwal, 2010. "Investigating Consumer Purchase Behavior in Related Technology Product Categories," Marketing Science, INFORMS, vol. 29(2), pages 291-314, 03-04.
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    More about this item

    Keywords

    networks; product networks; electronic commerce; ecommerce; recommender systems; long tail;
    All these keywords.

    JEL classification:

    • D4 - Microeconomics - - Market Structure, Pricing, and Design

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