Using Machine Learning to Predict Future TV Ratings
TV ratings are used to predict the future. They set expectations and affect programming decisions from one season to the next, and they help set the cost of advertising (advertising rates) well in advance of when a program goes on the air. In the U.S. for instance, TV networks sell the majority of their premium ad inventory for the year at the “upfront,” a group of events that occur annually each spring. For each network, the upfront is a coming-out party to introduce new programs and build up excitement for the upcoming season, but behind the curtains, it’s very much a marketplace for advertisers to buy commercial time on television well ahead of schedule.
As a result, media companies have invested considerable effort to project future ratings. Reliable forecasts can help industry players make faster, more accurate and less subjective decisions, not just at the upfront, but also in the scatter planning that occurs during the season. And if reliable forecasts can be produced through an automated system, they can be used to enable advanced targeting on emerging programmatic TV platforms.
In this paper, we discuss a recent pilot project where Nielsen worked with one of our key clients to innovate and improve the practice of ratings projections. Through collaboration, we aimed to develop a more accurate (better performance metrics), more efficient (better cycle time) and more consistent (reduced variability) system to improve their existing practice and lay the foundation for an automated forecasting infrastructure.