An Empirical Study of Mobile Ad Targeting

TL;DR

This study empirically analyzes mobile ad targeting, revealing prevalent application-, time-, location-, and user-based targeting methods through a large-scale data collection and statistical analysis.

Contribution

It provides the first comprehensive empirical analysis of mobile ad targeting, quantifying the prevalence of various targeting techniques using a large dataset.

Findings

Application- and time-based targeting are nearly universal.

Location-based targeting appears in 43% of ads.

User-based targeting is present in 39% of ads.

Abstract

Advertising, long the financial mainstay of the web ecosystem, has become nearly ubiquitous in the world of mobile apps. While ad targeting on the web is fairly well understood, mobile ad targeting is much less studied. In this paper, we use empirical methods to collect a database of over 225,000 ads on 32 simulated devices hosting one of three distinct user profiles. We then analyze how the ads are targeted by correlating ads to potential targeting profiles using Bayes' rule and Pearson's chi squared test. This enables us to measure the prevalence of different forms of targeting. We find that nearly all ads show the effects of application- and time-based targeting, while we are able to identify location-based targeting in 43% of the ads and user-based targeting in 39%.