Optimizing Cost per Click for Digital Advertising Campaigns

TL;DR

This paper presents a data-driven method for optimizing cost per click in digital advertising by recommending features and bid prices, leading to significant improvements in click metrics while maintaining campaign competitiveness.

Contribution

It introduces a novel bid and feature recommendation approach that adapts in real-time using click stream data, improving CPC and CTR across campaigns.

Findings

Cost per click reduced by 16-60%

Click-through rate increased by 42-137%

Method maintained campaign delivery competitiveness

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, 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.