Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/300816 
Erscheinungsjahr: 
2024
Schriftenreihe/Nr.: 
DIW Discussion Papers No. 2091
Verlag: 
Deutsches Institut für Wirtschaftsforschung (DIW), Berlin
Zusammenfassung: 
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.
Schlagwörter: 
Prediction quality
Web Tracking
Cookies
Data protection
Competition Policy
Internet Regulation
GDPR
JEL: 
C53
D22
D43
K21
L13
L4
Dokumentart: 
Working Paper

Datei(en):
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