Abstract: Systems and methods create new viewership estimates to drive linear ad schedule optimizations. The systems use machine learning techniques to predict granular-level television viewership metrics for any consumer segment measurable at a national level. The systems provide capabilities beyond merely estimating and forecasting television viewership for age- and gender-based demographic segments. The systems accurately estimate viewership for any consumer segment, including behavioral, demographic, and other segmentation techniques and provide reliable viewership predictions. The systems model viewership by training an ensemble of machine learning models using historical consumer segments data and TV viewership data.

Abstract: Systems and methods create new viewership estimates to drive linear ad schedule optimizations. The systems use machine learning techniques to predict granular-level television viewership metrics for any consumer segment measurable at a national level. The systems provide capabilities beyond merely estimating and forecasting television viewership for age- and gender-based demographic segments. The systems accurately estimate viewership for any consumer segment, including behavioral, demographic, and other segmentation techniques and provide reliable viewership predictions. The systems model viewership by training an ensemble of machine learning models using historical consumer segments data and TV viewership data.