Returns to Data: Evidence from Web Tracking - Hannes Ullrich; Jonas Hannane; Christian Peukert; Luis Aguiar; Tomaso Duso (CESifo GmbH, 2024)
Tracking online user behavior is essential for targeted advertising and is at the heart of the business model of major online platforms. We analyze tracker-specific web browsing data to show how the prediction quality of consumer profiles varies with data size and scope. We find decreasing returns to the number of observed users and tracked websites. However, prediction quality increases considerably when web browsing data can be combined with demographic data. We show that Google, Facebook, and Amazon, which can combine such data at scale via their digital ecosystems, may thus attenuate the impact of regulatory interventions such as the GDPR. In this light, even with decreasing returns to data small firms can be prevented from catching up with these large incumbents. We document that proposed data-sharing provisions may level the playing field concerning the prediction quality of consumer profiles.
Keywords: C53; D22; D43; K21; L13; L40; 330; prediction quality; web tracking; cookies; data protection; competition policy; internet regulation; GDPR