How online advertising targets consumers: The uses of categories and algorithmic tools by audience planners

Abstract

Recent innovations in online advertising facilitate the use of a wide variety of data sources to build micro-segments of consumers, and delegate the manufacture of audience segments to machine learning algorithms. Both techniques promise to replace demographic targeting, as part of a post-demographic turn driven by big data technologies. This article empirically investigates this transformation in online advertising. We show that targeting categories are assessed along three criteria: efficiency, communicability, and explainability. The relative importance of these objectives helps explain the lasting role of demographic categories, the development of audience segments specific to each advertiser, and the difficulty in generalizing interest categories associated with big data. These results underline the importance of studying the impact of advanced big data and AI technologies in their organizational and professional contexts of appropriation, and of paying attention to the permanence of the categorizations that make the social world intelligible.