This paper 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 current model used in our
production system, as well as 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 potential as an aid in future ad pricing.