Abstract—Tidal TV is an online video advertising, optimization, and yield management solutions provider. Their current business strategy includes purchasing online video advertising inventory from content providers and delivering ads to their clients, using demographic targeting to maximize value. The problem is to develop, test, and simulate the best bidding strategies for both second price and first price auctions and then to identify the tradeoffs of using the optimized strategies over current existing strategies. Tidal TV will then incorporate learning from this exercise to further develop strategies to bid for online video ads. I. MOTIVATION HIS paper presents a methodology for Tidal TV to provide optimized online video advertisements. The research described in this paper overviews current auction strategies and then reviews proposed algorithms that were developed, tested, and evaluated. We present general concepts to give the reader a broad understanding of the approaches proposed. A. Online Video Advertisement Market Background The online advertising market is expanding rapidly. Looking at Fig. 1, online ad revenue grew from $6 billion in 2002 to $22 billion in 2009 [1]. One of the fastest growing sectors within online advertising is online video (OLV), which grew 38% in 2009 [1]. As the marketplace is neither well established nor well regulated, fast growth forces Manuscript received April 4, 2011. This work was supported by Tidal TV in Baltimore, MD. C. Jacobik is an undergraduate student in the Department of Systems and Information Engineering at the University of Virginia (e-mail: cej9g@ virginia.edu). A. Dang is an undergraduate student in the Department of Systems and Information Engineering at the University of Virginia (e-mail: avd7a@ virginia.edu). T. Fujii is an undergraduate student in the Department of Systems and Information Engineering at the University of Virginia (e-mail: tf8b@ virginia.edu). J. Koerwer is an undergraduate student in the Department of Systems and Information Engineering at the University of Virginia (e-mail: jjk7b@ virginia.edu). D. Schultz is an undergraduate student in the Department of Systems and Information Engineering at the University of Virginia (e-mail: dws5w@ virginia.edu). E. Trouton is an undergraduate student in the Department of Systems and Information Engineering at the University of Virginia (e-mail: ert5e@ virginia.edu). W. Scherer is a professor in the Department of Systems and Information Engineering at the University of Virginia (e-mail: wts@ virginia.edu). participating companies to adapt quickly to rapidly shifting marketplace conditions. Fig. 1. Online Video Revenue chart. Taken from IAB [1]. At the same time, online video advertisements still maintain a relatively small portion of Internet advertising revenues. According to Fig. 2, digital video accounts for approximately 4 percent of overall Internet advertising. Search ads, on the other hand, currently account for the largest amount of Internet advertising revenue with 47 percent of all Internet advertising revenue. Fig. 2. Internet advertising breakdown from IAB [1]. B. Client Background Tidal TV is a third party video advertisement intermediary that utilizes demographic information to target ads on behalf of their clients. Targeted advertisements viewed by a user are more valuable to the advertiser, since the user viewing the ad is more likely to purchase the product or service in the future. According to [2], approximately 80 percent of online Bidding Strategies Optimization for the Online Video Ad Spot Market Craig Jacobik, Au Dang, Tomu Fujii, Joseph Koerwer, David Schultz, Eric Trouton, William T. Scherer, Member, IEEE T Proceedings of the 2011 IEEE Systems and Information Engineering Design Symposium, University of Virginia, Charlottesville, VA, USA, April 29, 2011 FridayPM1Modeling.2 978-1-4577-0447-5/11/$26.00 ©2011 IEEE 110