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Pricing and Allocation for Quality-Differentiated Online Services

Author

Listed:
  • Ravi Bapna

    (Department of Operations and Information Management, School of Business, University of Connecticut, Storrs, Connecticut 06269)

  • Paulo Goes

    (Department of Operations and Information Management, School of Business, University of Connecticut, Storrs, Connecticut 06269)

  • Alok Gupta

    (Information and Decision Sciences Department, Carlson School of Management, University of Minnesota, Minneapolis, Minnesota 55455)

Abstract

We explore the problem of pricing and allocation of unique, one-time digital products in the form of data streams. We look at the short-term problem where the firm has a capacitated shared resource and multiple products or service levels. We formulate the allocatively efficient Generalized Vickrey Auction (GVA) for our setting and point out the computational challenges in determining the individual discriminatory transfer payments. We propose an alternative uniform-price, computationally efficient, revenue-maximizing knapsack formulation called the Multiple Vickrey Auction (MVA). While not incentive compatible, the MVA mechanism achieves bounded posterior regret and can be solved in real time. It has the added benefit of realizing imputed commodity prices for the various services, a feature lacking in the discriminatory GVA approach. For service providers that are concerned about the incentive compatibility but want imputed service prices, we suggest a maximal MVA (mMVA) uniform-pricing scheme that trades off revenue maximization for allocative efficiency. For sake of completeness we discuss the properties of a first-price pay-your-bid scheme. While NP-hard and not incentive compatible, this formulation has the perceived benefit of cognitive simplicity on the parts of sellers and bidders.

Suggested Citation

  • Ravi Bapna & Paulo Goes & Alok Gupta, 2005. "Pricing and Allocation for Quality-Differentiated Online Services," Management Science, INFORMS, vol. 51(7), pages 1141-1150, July.
    • Handle: RePEc:inm:ormnsc:v:51:y:2005:i:7:p:1141-1150
    DOI: 10.1287/mnsc.1050.0419
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    References listed on IDEAS

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    Cited by:

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    2. Xi, Haoning & Liu, Wei & Waller, S. Travis & Hensher, David A. & Kilby, Philip & Rey, David, 2023. "Incentive-compatible mechanisms for online resource allocation in Mobility-as-a-Service systems," Transportation Research Part B: Methodological, Elsevier, vol. 170(C), pages 119-147.
    3. Mochen Yang & Gediminas Adomavicius & Alok Gupta, 2019. "Efficient Computational Strategies for Dynamic Inventory Liquidation," Information Systems Research, INFORMS, vol. 30(2), pages 595-615, June.
    4. De Liu & Jianqing Chen & Andrew B. Whinston, 2010. "Ex Ante Information and the Design of Keyword Auctions," Information Systems Research, INFORMS, vol. 21(1), pages 133-153, March.
    5. Ravi Bapna & Sanjukta Das & Robert Garfinkel & Jan Stallaert, 2008. "A Market Design for Grid Computing," INFORMS Journal on Computing, INFORMS, vol. 20(1), pages 100-111, February.
    6. Tobias Widmer & Paul Karaenke & Vijayan Sugumaran, 2021. "Two‐sided service markets: Effects of quality differentiation on market efficiency," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 42(3), pages 588-604, April.

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