Ad Slotting and Pricing: New Media Planning Models for New Media

This thesis focuses on the planning, scheduling, and pricing of ad slots in technology-enabled modes of advertising, including webpage banner ads, video games, electronic outdoor billboards, and the next generation of digital TV. This work contributes to practice by being the first academic work to rigorously study the scheduling of dynamic in-game advertising; contributes to the body of theory by formally studying the aggregation of audience segments using duality theory; generates managerial insights for directing pricing strategy; and makes numerous modeling contributions, hence the title: New Media Planning Models for New Media. A synopsis of the three main chapters follows. Chapter 1: “Scheduling of Dynamic In-Game Advertising” Dynamic in-game advertising is a new form of advertising in which ads are served to video game consoles in real-time over the Internet. We present a model for the in-game ad scheduling problem faced by Massive Inc., a wholly-owned subsidiary of Microsoft, and a leading global network provider of in-game ad space. Our model has two components: 1) a linear program (solved periodically) establishes target service rates, and 2) a real-time packing heuristic (run whenever a player enters a new level) tracks these service rates. We benchmark our model against Massive’s legacy algorithm: When tested on historical data, we observe 1) an 80-87% reduction in make-good costs (depending on forecast accuracy), and 2) a shift in the age distribution of served ad space, leaving more premium inventory open for future sales. As a result of our work, Massive has increased the number of unique individuals that see each campaign by on average 26% per week, and achieved 33% smoother campaign delivery, as measured by standard deviation of hourly impressions served.