Revenue maximization in reservation-based online advertising through dynamic inventory management

A widely used model in the online advertising industry is one where advertisers pre-purchase a reservation package of online inventory on content sites owned by publishers (e.g., CNN, amazon, etc.). Sales representatives, acting on behalf of publishers, sell inventory (impression) bundles of various types (text, video, multimedia, etc.) while trying to meet advertisers' expectations. The current process of sales is usually ad hoc and oftentimes a publisher uncontrollably runs out of a highly desirable inventory type, failing to meet the demand of his/her more valuable customers (advertisers). In this specific framework of display advertising, we propose a mathematical model for this problem and design a simple and easy to implement online impression allocation policy with provably revenue maximizing performance. Our results represent fundamental extensions to the existing theory of loss networks, given that this new application introduces novel mathematical assumptions and operational constraints.