Buy-it-now or Take-a-chance: A New Pricing Mechanism for Online Advertising

Increasingly sophisticated tracking technology oers publishers the ability to oer targeted advertisements to advertisers. Such targeting enhances advertising eciency by improving the match quality between advertisers and users, but also thins the market of interested advertisers. Using bidding data from Microsoft's Ad Exchange (AdECN) platform, we show that there is often a substantial gap between the highest and second highest willingness to pay. This motivates our new BIN-TAC mechanism, which is eective in extracting revenue when such a gap exists. Bidders can \buy- it-now", or alternatively \take-a-chance" in an auction, where the top d > 1 bidders are equally likely to win. The randomized take-a-chance allocation incentivizes high valuation bidders to buy-it-now. We show that for a large class of distributions, this mechanism achieves similar allocations and revenues as Myerson's optimal mechanism, and outperforms the second-price auction with reserve. For the AdECN data, we use structural methods to estimate counterfactual revenues, and nd that our BIN-TAC mechanism improves revenue by 11% relative to an optimal second-price auction

Buy-it-now or Take-a-chance: A New Pricing Mechanism for Online Advertising

Increasingly sophisticated tracking technology oers publishers the ability to oer targeted advertisements to advertisers. Such targeting enhances advertising eciency by improving the match quality between advertisers and users, but also thins the market of interested advertisers. Using bidding data from Microsoft's Ad Exchange (AdECN) platform, we show that there is often a substantial gap between the highest and second highest willingness to pay. This motivates our new BIN-TAC mechanism, which is eective in extracting revenue when such a gap exists. Bidders can \buy- it-now", or alternatively \take-a-chance" in an auction, where the top d > 1 bidders are equally likely to win. The randomized take-a-chance allocation incentivizes high valuation bidders to buy-it-now. We show that for a large class of distributions, this mechanism achieves similar allocations and revenues as Myerson's optimal mechanism, and outperforms the second-price auction with reserve. For the AdECN data, we use structural methods to estimate counterfactual revenues, and nd that our BIN-TAC mechanism improves revenue by 11% relative to an optimal second-price auction

Allocating online advertisement space with unreliable estimates

We study the problem of optimally allocating online advertisement space to budget-constrained advertisers. This problem was defined and studied from the perspective of worst-case online competitive analysis by Mehta et al. Our objective is to find an algorithm that takes advantage of the given estimates of the frequencies of keywords to compute a near optimal solution when the estimates are accurate, while at the same time maintaining a good worst-case competitive ratio in case the estimates are totally incorrect. This is motivated by real-world situations where search engines have stochastic information that provide reasonably accurate estimates of the frequency of search queries except in certain highly unpredictable yet economically valuable spikes in the search pattern. Our approach is a black-box approach: we assume we have access to an oracle that uses the given estimates to recommend an advertiser every time a query arrives. We use this oracle to design an algorithm that provides two performance guarantees: the performance guarantee in the case that the oracle gives an accurate estimate, and its worst-case performance guarantee. Our algorithm can be fine tuned by adjusting a parameter α, giving a tradeoff curve between the two performance measures with the best competitive ratio for the worst-case scenario at one end of the curve and the optimal solution for the scenario where estimates are accurate at the other end. Finally, we demonstrate the applicability of our framework by applying it to two classical online problems, namely the lost cow and the ski rental problems