Pricing and Allocation for Quality-Differentiated Online Services

  • Authors:

  • Ravi Bapna;Paulo Goes;Alok Gupta

  • Affiliations:

  • Department of Operations and Information Management, School of Business, University of Connecticut, Storrs, Connecticut 06269;Department of Operations and Information Management, School of Business, University of Connecticut, Storrs, Connecticut 06269;Information and Decision Sciences Department, Carlson School of Management, University of Minnesota, Minneapolis, Minnesota 55455

  • Venue:
  • Management Science
  • Year:
  • 2005

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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.