Deriving Meaningful Metrics from Audience Retention Data.
This article introduces a measure of television ad quality based on audience retention using logistic regression techniques to normalize such scores against expected audience behavior. By adjusting for features such as time of day, network, recent user behavior, and household demographics, we are able to isolate ad quality from these extraneous factors. We introduce the model used in the current Google TV Ads product and two new competing models that show some improvement. We also devise metrics for calculating a model’s predictive power and variance, allowing us to determine which of our models performs best. We conclude with discussions of retention score applications for advertisers to evaluate their ad strategies and as a potential aid in future ad pricing.
Image: Pablo GarciaSaldaña | stocksnap.io