When do you Zap: The Effects of Ad Delivery in Streaming Media

Abstract

Free ad-supported streaming of on-demand content is growing. Platforms that provide this service need to better understand how ad delivery affects the consumption experience – do viewers zap (leave a program incomplete) during immediate ad exposure or subsequent (program) content exposure? Using debiased machine learning with Hausman instruments, we capture the causal effect of four levers of ad delivery on immediate ad zapping and subsequent content zapping behavior. Our results find that on average, an increase in number of pods (ad breaks), length of pods or repetition of ads, results in a larger increase in subsequent content zapping than immediate ad zapping. On the other hand, an increase in spacing till the next pod, results in a larger decrease in immediate ad zapping than subsequent content zapping. We also investigate differences in effects across sub-types of zapping: (a) switching to another episode of same TV show, (b) switching to new TV show or movie and (c) stop watching. We find that the platform can face tradeoffs between preventing ad zapping or content zapping for some ad delivery levers based on whether the platform wants to promote stickiness to the content or to the platform.

Keywords: Streaming Platforms, Ad Delivery, Zapping, Causal Inference, Debiased Machine Learning

JEL Classification: M31, M37, C14, C36, C61