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We consider the problem of pricing guaranteed contracts in online display advertising. This problem has two key characteristics that when taken together distinguish it from related offline and online pricing problems: (1) the guaranteed contracts are sold months in advance, and at various points in time, and (2) the inventory that is sold to guaranteed contracts - user visits - is very high-dimensional, having hundreds of possible attributes, and advertisers can potentially buy any of the very large number (many trillions) of combinations of these attributes. Consequently, traditional pricing methods such as real-time or combinatorial auctions, or optimization-based pricing based on self- and cross-elasticities are not directly applicable to this problem. We hence propose a new pricing method, whereby the price of a guaranteed contract is computed based on the prices of the individual user visits that the contract is expected to get. The price of each individual user visit is in turn computed using historical sales prices that are negotiated between a sales person and an advertiser, and we propose two different variants in this context. Our evaluation using real guaranteed contracts shows that the proposed pricing method is accurate in the sense that it can effectively predict the prices of other (out-of-sample) historical contracts.
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