Using Transaction Data for the Design of Sequential, Multi-unit, Online Auctions

1 We acknowledge the helpful comments on earlier versions of this work from seminar participants at Abstract Internet auctions for consumers' goods are an increasingly popular selling venue. The Internet's computational ability makes possible the sale of multiple units of the same good in a single auction. Many sellers, instead of offering the entire inventory at a single auction, split it into sequential auctions of smaller lots, to reduce the negative market impact of large lots. We investigate how the available inventory should be split into multiple lots and how many sequential auctions should be run. We also investigate how we can leverage information technology to improve the design of future auctions. Assuming a truth-revealing ascending auction model, we quantify the effect of auction lot size on the closing price. We then develop a model for allocating inventory across multiple auctions. Solving the dynamic programming formulation, we prove that the lot size drops from period to period. The intensity of the decline increases in the holding costs and the website's traffic intensity, while decreasing in the dispersion of consumers' valuations of the good. Finally, we extend this model to dynamically incorporate the results of previous auctions as feedback into the design of consecutive auctions, updating the lot size and number of auctions. We demonstrate how information signals from previous auctions should be used to update the auctioneer's belief s about the customers' valuation distribution, thereby significantly increasing the sellers' profit potential. We use several examples to show how the benefits of using detailed transaction data for the design of sequential, multi-unit, online auctions is influenced by the inventory holding costs, bid traffic, and the dispersion of consumers' valuations.