Incrementality Bidding & Attribution
published: Dec. 1, 2017, recorded: August 2017, views: 3368
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
The causal effect of showing an ad to a potential customer versus not, commonly referred to as “incrementality,” is the fundamental question of advertising effectiveness. In digital advertising three major puzzle pieces are central to rigorously quantifying advertising incrementality: ad buying/bidding/pricing, attribution, and experimentation. Building on the foundations of machine learning and causal econometrics, we propose a methodology that unifies these three concepts into a computationally viable model of both bidding and attribution which spans randomization, training, cross validation, scoring, and conversion attribution in a causal model of advertising’s effects. Thanks to this method, Netflix has benefited by identifying many cases where traditional models were either overspending or underspending, leading to a significant improvement in the return on investment of advertising.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !