Finding the Sweet Spot: Ad Scheduling on Streaming Media

Abstract

A majority of US households view content on online video streaming services, consuming on demand. Not surprisingly, ad spending on such services is growing rapidly. We develop a three-stage approach to deliver an optimal ad schedule that balances the interests of the viewer (content consumption) with that of the streaming platform (ad exposure). In the first stage, we use theoretical findings to develop two parsimonious metrics – Bingeability and Ad Tolerance – to capture the interplay between content consumption and ad exposure. Bingeability represents the number of completely viewed unique episodes of a show while Ad Tolerance represents the willingness of a viewer to continue watching after ad exposure. The second stage uses detailed data on viewing activity and ad delivery to predict these metrics for a viewing session using causal machine learning methods. This is achieved via tree-based algorithms combined with instrumental variables to accommodate the non-randomness in ad delivery. In the third stage, we use the predicted metrics as inputs to a novel constrained optimization procedure that provides the optimal ad schedule.