A deep dive into the accuracy of IP geolocation databases and its impact on online advertising

The quest for every time more personalized Internet experience relies on the enriched contextual information about each user. Online advertising also follows this approach. Among the context information that advertising stakeholders leverage, location information is certainly one of them. However, when this information is not directly available from the end users, advertising stakeholders infer it using geolocation databases, matching IP addresses to a position on earth. The accuracy of this approach has often been questioned in the past: however, the reality check on an advertising stakeholder shows that this technique accounts for a large fraction of the served advertisements. In this paper, we revisit the work in the field, that is mostly from almost one decade ago, through the lenses of big data. More specifically, we, i) benchmark two commercial Internet geolocation databases, evaluate the quality of their information using a ground-truth database of user positions containing over 2 billion samples, ii) analyze the internals of these databases, devising a theoretical upper bound for the quality of the Internet geolocation approach, and iii) we run an empirical study that unveils the monetary impact of this technology by considering the costs associated with a real-world ad impressions dataset.This work was supported in part by European Union's Horizon 2020 innovation action programme under the PIMCITY Project under Grant 871370, in part by TESTABLE Project under Grant 101019206, in part by Agencia Estatal de Investigacion (AEI) under the ACHILLES Project under Grants PID2019-104207RB-I00/AEI/10.13039/501100011033, in part by the Spanish Ministry of Economic Affairs and Digital Transformation and European Union-Next GenerationEU through the UNICO 5G I+D 6G-RIEMANN-FR, in part by the agreement between the Community of Madrid and the Universidad Carlos III de Madrid for the funding of research projects on SARS-CoV-2 and COVID-19 disease, through project name Multi-source and multi-method prediction to support COVID-19 policy decision making, which was supported with REACT-EU funds from the European regional development fund a way of making Europe, and in part by TAPTAP-UC3M Chair in advanced AI and Data Science applied to advertising and marketing