Improving Ads-Profitability Using Traffic-Fingerprints

TL;DR

This paper proposes traffic-fingerprints, a 24-dimensional vector representation of web page traffic, which correlates with ad profitability patterns, enabling significant revenue improvements through cluster-based traffic management.

Contribution

Introduction of traffic-fingerprints and clustering method to predict and enhance ad profitability across web pages.

Findings

Traffic-fingerprints correlate with ad conversion rates.

Cluster-based traffic management increased revenue by over 50%.

Traffic patterns can predict profitability on low-traffic pages.

Abstract

This paper introduces the concept of traffic-fingerprints, i.e., normalized 24-dimensional vectors representing a distribution of daily traffic on a web page. Using k-means clustering we show that similarity of traffic-fingerprints is related to the similarity of profitability time patterns for ads shown on these pages. In other words, these fingerprints are correlated with the conversions rates, thus allowing us to argue about conversion rates on pages with negligible traffic. By blocking or unblocking whole clusters of pages we were able to increase the revenue of online campaigns by more than 50%.