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Data-driven mergers and personalization

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Listed:
  • Zhijun Chen
  • Chongwoo Choe
  • Jiajia Cong
  • Noriaki Matsushima

Abstract

Recent years have seen growing cases of data-driven tech mergers such as Google/Fitbit, in which a dominant digital platform acquires a relatively small firm possessing a large volume of consumer data. The digital platform can consolidate the consumer data with its existing data set from other services and use it for personalization in related markets. We develop a theoretical model to examine the impact of such mergers across the two markets that are related through a consumption synergy. The merger links the markets for data collection and data application, through which the digital platform can leverage its market power and hurt competitors in both markets. Personalization can lead to exploitation of some consumers in the market for data application. But insofar as competitors remain active, the merger increases total consumer surplus in both markets by intensifying competition. When the consumption synergy is large enough, the merger can result in monopolization of both markets, leading to further consumer harm when stand-alone competitors exit in the long run. Thus, there is a trade-off where potential dynamic costs can outweigh static benefits. We also discuss policy implications by considering various merger remedies.

Suggested Citation

  • Zhijun Chen & Chongwoo Choe & Jiajia Cong & Noriaki Matsushima, 2020. "Data-driven mergers and personalization," ISER Discussion Paper 1108, Institute of Social and Economic Research, The University of Osaka.
  • Handle: RePEc:dpr:wpaper:1108
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    References listed on IDEAS

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    Cited by:

    1. Dubus, Antoine & Legros, Patrick, 2022. "The Sale of Data: Learning Synergies Before M&As," CEPR Discussion Papers 17404, C.E.P.R. Discussion Papers.
    2. Yiquan Gu & Leonardo Madio & Carlo Reggiani, 2022. "Data brokers co-opetition [The impact of big data on firm performance: an empirical investigation]," Oxford Economic Papers, Oxford University Press, vol. 74(3), pages 820-839.
    3. DELBONO Flavio & REGGIANI Carlo & SANDRINI Luca, 2021. "Strategic data sales to competing firms," JRC Working Papers on Digital Economy 2021-05, Joint Research Centre.
    4. Choe, Chongwoo & Matsushima, Noriaki & Tremblay, Mark J., 2022. "Behavior-based personalized pricing: When firms can share customer information," International Journal of Industrial Organization, Elsevier, vol. 82(C).
    5. David Bounie & Antoine Dubus & Patrick Waelbroeck, 2023. "Competition Between Strategic Data Intermediaries with Implications for Merger Policy," Working Papers hal-03336520, HAL.
    6. Zhijun Chen & Chongwoo Choe & Jiajia Cong & Noriaki Matsushima, 2022. "Data‐driven mergers and personalization," RAND Journal of Economics, RAND Corporation, vol. 53(1), pages 3-31, March.
    7. CARBALLA SMICHOWSKI Bruno & DUCH BROWN Nestor & GOMEZ LOSADA Alvaro & MARTENS Bertin, 2021. "When ‘the’ market loses its relevance: an empirical analysis of demand-side linkages in platform ecosystems," JRC Working Papers on Digital Economy 2021-07, Joint Research Centre.
    8. David Bounie & Antoine Dubus & Patrick Waelbroeck, 2021. "Competition and Mergers with Strategic Data Intermediaries," CESifo Working Paper Series 9339, CESifo.

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    JEL classification:

    • D43 - Microeconomics - - Market Structure, Pricing, and Design - - - Oligopoly and Other Forms of Market Imperfection
    • L13 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Oligopoly and Other Imperfect Markets
    • L41 - Industrial Organization - - Antitrust Issues and Policies - - - Monopolization; Horizontal Anticompetitive Practices
    • K21 - Law and Economics - - Regulation and Business Law - - - Antitrust Law

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