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. Information technology also makes it possible to easily collect and
analyze bid data from online auctions. In this seminar we develop and analyze a
new model of sequential online auctions to explore the potential benefits of
using bid data from earlier auctions 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 and investigate how the available inventory
should be split into multiple lots and how many sequential auctions should be
run. Solving the dynamic programming formulation, we prove that the lot size is
monotonically non increasing. The intensity of the decline intensifies in the
holding costs and the website's traffic intensity, while decreasing in the
dispersion of consumers' valuations of the good.
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 that information signals from previous auctions can be used to update the auctioneer's beliefs about the customers' valuation distribution, and then to significantly increase 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, the bid traffic, and the dispersion of consumers' valuations. Time permitting, we plan on discussing how sellers can increase their revenues by offering auctions and a fixed price concurrently, and identify when either a posted price only or a dual channel strategy is optimal for the seller. Our results explain how optimizing the design parameters enable the seller to effectively segment the market so that the two channels reinforce each other and cannibalization is mitigated. We conclude the seminar with some ongoing practical business applications of our theoretical work.