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Bundling Information Goods: Pricing, Profits, and Efficiency

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  • Yannis Bakos

    (Department of Information Systems, Leonard N. Stern School of Business, New York University, New York, New York 10012-1126)

  • Erik Brynjolfsson

    (Sloan School of Management, Massachusetts Institute of Technology, 50 Memorial Drive, Cambridge, Massachusetts 02142)

Abstract

We study the strategy of bundling a large number of information goods, such as those increasingly available on the Internet, and selling them for a fixed price. We analyze the optimal bundling strategies for a multiproduct monopolist, and we find that bundling very large numbers of unrelated information goods can be surprisingly profitable. The reason is that the law of large numbers makes it much easier to predict consumers' valuations for a bundle of goods than their valuations for the individual goods when sold separately. As a result, this "predictive value of bundling" makes it possible to achieve greater sales, greater economic efficiency, and greater profits per good from a bundle of information goods than can be attained when the same goods are sold separately. Our main results do not extend to most physical goods, as the marginal costs of production for goods not used by the buyer typically negate any benefits from the predictive value of large-scale bundling. While determining optimal bundling strategies for more than two goods is a notoriously difficult problem, we use statistical techniques to provide strong asymptotic results and bounds on profits for bundles of any arbitrary size. We show how our model can be used to analyze the bundling of complements and substitutes, bundling in the presence of budget constraints, and bundling of goods with various types of correlations and how each of these conditions can lead to limits on optimal bundle size. In particular we find that when different market segments of consumers differ systematically in their valuations for goods, simple bundling will no longer be optimal. However, by offering a menu of different bundles aimed at each market segment, bundling makes traditional price discrimination strategies more powerful by reducing the role of unpredictable idiosyncratic components of valuations. The predictions of our analysis appear to be consistent with empirical observations of the markets for Internet and online content, cable television programming, and copyrighted music.

Suggested Citation

  • Yannis Bakos & Erik Brynjolfsson, 1999. "Bundling Information Goods: Pricing, Profits, and Efficiency," Management Science, INFORMS, vol. 45(12), pages 1613-1630, December.
  • Handle: RePEc:inm:ormnsc:v:45:y:1999:i:12:p:1613-1630
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    File URL: http://dx.doi.org/10.1287/mnsc.45.12.1613
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    References listed on IDEAS

    as
    1. Ross, Stephen A., 1976. "The arbitrage theory of capital asset pricing," Journal of Economic Theory, Elsevier, vol. 13(3), pages 341-360, December.
    2. Schmalensee, Richard, 1984. "Gaussian Demand and Commodity Bundling," The Journal of Business, University of Chicago Press, vol. 57(1), pages 211-230, January.
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    4. Eric K. Clemons & Bruce W. Weber, 1997. "Information Technology and Screen-Based Securities Trading: Pricing the Stock and Pricing the Trade," Management Science, INFORMS, vol. 43(12), pages 1693-1708, December.
    5. Bakos, Yannis & Brynjolfsson, Erik & Lichtman, Douglas, 1999. "Shared Information Goods," Journal of Law and Economics, University of Chicago Press, vol. 42(1), pages 117-155, April.
    6. Salinger, Michael A, 1995. "A Graphical Analysis of Bundling," The Journal of Business, University of Chicago Press, vol. 68(1), pages 85-98, January.
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    8. A. M. Spence, 1981. "The Learning Curve and Competition," Bell Journal of Economics, The RAND Corporation, vol. 12(1), pages 49-70, Spring.
    9. R. Preston McAfee & John McMillan & Michael D. Whinston, 1989. "Multiproduct Monopoly, Commodity Bundling, and Correlation of Values," The Quarterly Journal of Economics, Oxford University Press, vol. 104(2), pages 371-383.
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