An Automated and Data-Driven Bidding Strategy for Online Auctions

Abstract

The flexibility of time and location as well as the availability of an abundance of both old and new products makes online auctions an important part of people's daily shopping experience. While many bidders rely on variants of the well-documented early or last-minutes bidding strategies, neither strategy takes into account the aspect of auction competition: at any point in time, there are hundreds, even thousands of same or similar items up for sale, competing for the same bidder. In this paper, we propose a novel automated and data-driven bidding strategy. Our strategy consists of two main components. First, we develop a dynamic, forward-looking model for price in competing auctions. By incorporating dynamic features of the auction process and its competitive environment, our model is capable of accurately predicting an auction's price, outperforming model-alternatives such as GAM, CART or Neural Networks. Then, using the idea of maximizing consumer surplus, we build a bidding framework around this model that determines the best auction to bid on and the best bid-amount. The best auction to bid on yields the highest predicted surplus and the best bid-amount is the predicted auction price. In simulations, we compare our automated strategy with early and last-minute bidding and find that our approach extracts 97% and 15% more expected surplus, respectively.

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