Towards Data Quality Assessment in Online Advertising

TL;DR

This paper introduces a scalable framework for assessing data quality in online advertising, focusing on evaluating data source relevance when user-level ground truth is unavailable or anonymized.

Contribution

It proposes multiple methodologies for large-scale data quality assessment and demonstrates their application in targeted advertising and audience forecasting.

Findings

Framework effectively evaluates data source similarity to ground truth.

Methodologies compare in terms of accuracy and scalability.

Preliminary results show promising use in targeted advertising.

Abstract

In online advertising, our aim is to match the advertisers with the most relevant users to optimize the campaign performance. In the pursuit of achieving this goal, multiple data sources provided by the advertisers or third-party data providers are utilized to choose the set of users according to the advertisers' targeting criteria. In this paper, we present a framework that can be applied to assess the quality of such data sources in large scale. This framework efficiently evaluates the similarity of a specific data source categorization to that of the ground truth, especially for those cases when the ground truth is accessible only in aggregate, and the user-level information is anonymized or unavailable due to privacy reasons. We propose multiple methodologies within this framework, present some preliminary assessment results, and evaluate how the methodologies compare to each other. We also present two use cases where we can utilize the data quality assessment results: the first use case is targeting specific user categories, and the second one is forecasting the desirable audiences we can reach for an online advertising campaign with pre-set targeting criteria.