Optimal pricing using online auction experiments: A P\'olya tree approach

We show how a retailer can estimate the optimal price of a new product using observed transaction prices from online second-price auction experiments. For this purpose we propose a Bayesian P\'olya tree approach which, given the limited nature of the data, requires a specially tailored implementation. Avoiding the need for a priori parametric assumptions, the P\'olya tree approach allows for flexible inference of the valuation distribution, leading to more robust estimation of optimal price than competing parametric approaches. In collaboration with an online jewelry retailer, we illustrate how our methodology can be combined with managerial prior knowledge to estimate the profit maximizing price of a new jewelry product.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS503 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Optimal Design Of English Auctions With Discrete Bid Levels

This paper considers a form of ascending price English auction widely used in both live and online auctions. This discrete bid auction requires that the bidders submit bids at predetermined discrete bid levels, and thus, there exists a minimal increment by which the bid price may be raised. In contrast, the academic literature of optimal auction design deals almost solely with continuous bid auctions. As a result, there is little practical guidance as to how an auctioneer, seeking to maximize its revenue, should determine the number and value of these discrete bid levels, and it is this omission that is addressed here. To this end, a model of a discrete bid auction from the literature is considered, and an expression for the expected revenue of this auction is derived. This expression is used to determine both numerical and analytical solutions for the optimal bid levels, and uniform and exponential bidder’s valuation distributions are compared. Finally, the limiting case where the number of discrete bid levels is large is considered. An analytical expression for the distribution of the optimal discrete bid levels is derived, and an intuitive understanding of how this distribution maximizes the revenue of the auction is developed

An Experimental Assessment of Confederate Reserve Price Bids in Online Auction

internet auctions, bid shilling, reserve price, internet fraud, market design

Seller strategies on eBay: Does size matter?

We examine seller strategies in 1177 Internet auctions on eBay, to understand the diversity of strategies used, and their impacts. Dimensions of strategic choice include the use of a ‘Buy it Now’ option, the level of the starting price, and the use of a secret reserve price. A major focus of our analysis is on differences across sellers with different volumes of sales. The largest volume sellers (termed “retailers”) in our sample employ uniform selling strategies, but lower volume sellers exhibit a wide variety of strategic choices. While some components of sellers’ strategies appear important in raising seller revenue, including starting the auction with a ‘Buy it Now’ offer, the overall impact of seller strategy choices on the outcome appears to be quite small. We interpret this as evidence for the competitiveness of the online auction market for frequently traded items with conventional retail alternatives. An exception is provided by the use of a secret reserve price, which raises the winning bid conditional on a sale, but reduces the probability of a sale. Depending on sellers’ risk aversion and impatience, this may also be an efficient outcome

Buy-It-Now prices in eBay Auctions - The Field in the Lab

Electronic commerce has grown extraordinarily over the years, with online auctions being extremely successful forms of trade. Those auctions come in a variety of different formats, such as the Buy-It-Now auction format on eBay, that allows sellers to post prices at which buyers can purchase a good prior to the auction. Even though, buyer behavior is well studied in Buy-It-Now auctions, as to this point little is known about how sellers set Buy-It-Now prices. We investigate into this question by analyzing seller behavior in Buy-It-Now auctions. More precisely, we combine the use of a real online auction market (the eBay platform and eBay traders) with the techniques of lab experiments. We find a striking link between the information about agents provided by the eBay market institution and their behavior. Information about buyers is correlated with their deviation from true value bidding. Sellers respond strategically to this information when deciding on their Buy-It-Now prices. Thus, our results highlight potential economic consequences of information publicly available in (online) market institutions