Optimizing Cost per Click for Digital Advertising Campaigns

Abstract

Cost per click is a common metric to judge digital advertising campaign performance. In this paper, we discuss an approach that generates a feature targeting recommendation to optimise cost per click. We also discuss a technique to assign bid prices to features without compromising on the number of features recommended. Our approach utilises impression and click stream data sets corresponding to real-time auctions that we have won. The data contains information about device type, website, and RTB Exchange ID. We leverage data across all campaigns that we have access to while ensuring that recommendations are sensitive to both individual campaign level features and globally well-performing features as well. We model Bid recommendation around the hypothesis that a click is a Bernoulli trial and click stream follows Binomial distribution, which is then updated based on live performance, ensuring week-over-week improvement. This approach has been live tested over 10 weeks across 5 campaigns. We see Cost per click gains of 16-60% and click-through rate improvement of 42-137%. At the same time, the campaign delivery was competitive.