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Big Data und Preisdiskriminierung
[Big Data and Price Discrimination]

Author

Listed:
  • Vera Konrad

    (Heinrich-Heine-Universität Düsseldorf)

  • Andreas Polk

    (Hochschule für Wirtschaft und Recht Berlin)

Abstract

Zusammenfassung Unternehmen nutzen Daten zur Optimierung von Preisen. Mit zunehmender Kenntnis individueller Kundenprofile könnte der Spielraum steigen, Individuen gezielt über personalisierte Angebote anzusprechen. Die wettbewerblichen Effekte sind ambivalent: Personalisierte Preise können zur Ausbeutung im Sinne einer Abschöpfung der Konsumentenrente führen, aber auch die Wettbewerbsintensität erhöhen. In der Praxis scheuen sich die Unternehmen bisher weitgehend, individualisierte Preise einzusetzen.

Suggested Citation

  • Vera Konrad & Andreas Polk, 2020. "Big Data und Preisdiskriminierung [Big Data and Price Discrimination]," Wirtschaftsdienst, Springer;ZBW - Leibniz Information Centre for Economics, vol. 100(10), pages 793-798, October.
    • Handle: RePEc:spr:wirtsc:v:100:y:2020:i:10:d:10.1007_s10273-020-2765-5
    DOI: 10.1007/s10273-020-2765-5
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    References listed on IDEAS

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    Cited by:

    1. Haucap, Justus, 2021. "Mögliche Wohlfahrtswirkungen eines Einsatzes von Algorithmen," DICE Ordnungspolitische Perspektiven 109, Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).

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    More about this item

    JEL classification:

    • D40 - Microeconomics - - Market Structure, Pricing, and Design - - - General
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • L11 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Production, Pricing, and Market Structure; Size Distribution of Firms

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