Faculty Research and Creative Profiles

In addition to being excellent teachers and mentors, our faculty are on the frontier of research with the goal of expanding the overall knowledge and understanding of mass media and communications.

STUDY: Utilizing Artificial Neural Network algorithms (ANNs) to develop a model of effective ad curation

Beth Egan

Beth Egan

Associate Professor


The ability to measure audiences during television commercial breaks versus the overall program has created a new challenge for broadcasters. Advertising revenues provide the foundation of a networks ability to invest in programming, including their news product. The ability to maintain audiences during commercial breaks increases the revenue potential and helps maintain a network’s ability to produce independent journalism. With high commercial clutter currently, viewership declines during commercial breaks is high, reducing the amount of revenue network’s earn. Accurate estimation of the extent to which viewership drops when a commercial is presented can have many benefits:

  • Advertisers can better estimate the effectiveness of specific commercials
  • Broadcasters can optimize the scheduling of commercials to reduce clutter while maintaining effectiveness and protecting revenue
  • More relevant and engaging commercials would be welcomed by consumers.

Artificial Neural Network algorithms (ANNs) have been applied to many predictive and forecasting problems and often outperform traditional regression tasks. Recent developments in Deep Learning algorithms have found application in consumer devices, addressing difficult pattern recognition problems that arise in image processing and speech recognition. The proposed work will apply such algorithms to the prediction of reduction in commercial viewership.

  • The neural network algorithms will be implemented to:
  • Predict the effect of various attributes of commercials on viewership reduction;
  • Analyze the relationship between television program content and attributes of commercials;
  • Evaluate the effect of scheduling choices on viewership; and
  • Suggest optimal choices in the design and placement of commercials.

Professors Beth Egan and Fiona Chew of the Newhouse School, and Chilukuri Mohan of the College of Engineering and Computer Science, combine expertise from advertising, media studies and machine learning. Much of the research data provided by Comscore.

This study has received a 2019 CUSE Grant.

Beth Egan

About Beth Egan

Beth Donnelly Egan is a 25-year veteran of the media industry, having served most recently as managing partner and account director with MEC.

View full profile