Abstract

As targeted advertising becomes prevalent in a wide variety of media vehicles, planning models become increasingly important to ad networks that need to match ads to appropriate audience segments, provide a high quality of service (meet advertisers' goals), and ensure that ad serving opportunities are not wasted. We define Guaranteed Targeted Display Advertising (GTDA) as a class of media vehicles that include webpage banner ads, video games, electronic outdoor billboards, and the next generation of digital TV, and formulate the GTDA planning problem as a transportation problem with quadratic objective. By modeling audience uncertainty, forecast errors, and the ad server's execution of the plan, we derive sufficient conditions that state when our quadratic objective is a good surrogate for several ad delivery performance metrics. Moreover, our quadratic objective allows us to construct duality-based bounds for evaluating aggregations of the audience space, leading to two efficient algorithms for solving large problems: the first intelligently refines the audience space into successively smaller blocks, and the second uses scaling to find a feasible solution given a fixed audience space partition. Near-optimal schedules can often be produced despite significant aggregation.

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