Cable Technology Feature Article
Rovi Corporation Announces Availability of New Predictive Analytics Products
By Nathesh, TMCnet Contributor
With the newly released new predictive analytics products from Rovi Corporation, one can leverage audience segmentation to manage advanced advertising campaigns and targeting on-air promotions.
The new Rovi Audience (News - Alert) Management Solution products which include the Ad Optimizer and Promotion Optimizer supposedly utilize advanced algorithms to read into behavioral data and leverage Big Data in order to help broadcast networks, cable TV operators, service providers and other television distributors to easily build highly targeted campaigns.
John Moakley, executive vice president of data solutions at Rovi Corp, said, “The entertainment landscape continues to evolve dramatically, creating new opportunities for television networks and service providers to better understand their audiences. Using Ad Optimizer and Promotion Optimizer, our customers can use big data sets that come from multiple platforms; and through deeper analysis of the data, plan advertising or promotion to reach a more specific audience segment.”
The Rovi Audience Management solution suite brings together big data and predictive analysis to offer TV audience insights and advertising campaign management. The suite easily meshes with the users’ existing solutions and workflows.
According to Rovi, the Ad Optimizer can be deployed by networks and service providers to increase advertising revenue, maximize the value and performance of ad inventory across linear, VOD and TVE platforms, and realize a proactive, data-driven approach to managing audiences and inventory.
The Promotion Optimizer enables cable and broadcast networks to build new means for on-air promos targeted to data-rich audience segments using past viewing data. It uses the same platform and predictive analysis capabilities as the Ad Optimizer. The Promotion Optimizer helps users to sort out past data to drive future predictions, and leverage data from multiple platforms and sources and also identify placement opportunities for multiple campaigns competing for the same airtime.
Edited by Ryan Sartor