Optimal design of the online auction channel: Analytical, empirical, and computational insights

Decision Sciences, Fall 2002 by Bapna, Ravi, Goes, Paulo, Gupta, Alok, Karuga, Gilbert

Optimal Design of the Online Auction Channel: Analytical, Empirical, and Computational Insights*

ABSTRACT

The focus of this study is on business-to-consumer (132C) online auctions made possible by the advent of electronic commerce over an open-source, ubiquitous Internet Protocol (IP) computer network. This work presents an analytical model that characterizes the revenue generation process for a popular B2C online auction, namely, Yankee auctions. Such auctions sell multiple identical units of a good to multiple buyers using an ascending and open auction mechanism. The methodologies used to validate the analytical model range from empirical analysis to simulation. A key contribution of this study is the design of a partitioning scheme of the discrete valuation space of the bidders such that equilibrium points with higher revenue structures become identifiable and feasible. Our analysis indicates that the auctioneers are, most of the time, far away from the optimal choice of key control factors such as the bid increment, resulting in substantial losses in a market with already tight margins. With this in mind, we put forward a portfolio of tools, varying in their level of abstraction and information intensity requirements, which help auctioneers maximize their revenues.

Subject Areas: Emerging Supply Chain Channels, Online Auctions, and Simulation.

INTRODUCTION

Online auctions, in the absence of spatial, temporal, and geographic constraints, provide an alternative supply chain channel for the distribution of goods and services. This channel differs from the common posted-price mechanism that is typically used in the retail sector. In consumer-oriented markets, buyers can now experience the thrill of winning a product, potentially at a bargain, as opposed to the typically more tedious notion of buying it. Sellers, on the other hand, have an additional channel to distribute their goods, and the opportunity to liquidate rapidly aging goods at greater than salvage values. The primary facilitator of this phenomenon is the widespread adoption of electronic commerce over an open-source, ubiquitous Internet Protocol (IP) based network.

In this paper, we concentrate on optimizing the design of an emerging business-to-consumer (B2C) distribution channel known as Yankee auctions. Such auctions sell multiple identical units of a good to multiple buyers using an ascending and open auction mechanism, which has its roots in the English auction, yet are significantly different.

This work presents an analytical model that characterizes the revenue generation process of Yankee auctions. To validate the analytical model and to gain a better understanding of the revenue generation process of such auctions, we analyze real-world empirical data collected by a software agent that tracked these auctions round the clock. An interesting by-product of the auction data collection process is our ability to construct empirical demand curves for the auctioned goods. Consumer demand information is an important input in supply chain management. It provides the feedback necessary for making key decisions downstream in the supply chain. We demonstrate, using the capabilities of the Internet, how dynamic pricing mechanisms such as online auctions can provide opportunities for integration of demand information into the mechanism design process. This enhances the mechanisms in two ways. First, by appropriately setting the online auction parameters, auctioneers can maximize their returns. Secondly, by recognizing the demand implication and visualizing the trading process a priori, the eventual allocation can be more equitable, thus resulting in higher welfare for both consumers and the auctioneer.

To validate and complement the analytical model, we also introduce a flexible simulation model, offering auctioneers a portfolio of tools, varying in their level of abstraction and information intensity requirements, to help auctioneers maximize their revenues. In summary, this portfolio of decision-making tools provides a relatively risk-free and cost-effective approach to managing this new, webbased dynamic pricing distribution channel prevalent in the online setting, namely, the Yankee auction.

The rest of this paper is organized as follows. In the next section we describe the revenue generation process of the Yankee auction mechanism. Our understanding of the revenue generation process of Yankee auctions directs us to focus our attention to the combinatorial dynamics of the penultimate rounds of the auction, which forms the basis of our theoretical model in this paper. Later we show how the theoretical results can be applied to actual Yankee auctions by using consumer demand estimates derived from real collected data. That follows with a validation approach to the theoretical analysis using a simulation tool under varying degree of information abstraction. We conclude with an overview of our approach and a summary of the findings of the paper.

THE YANKEE AUCTION MECHANISM