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Data-Driven Mergers and Personalization

Zhijun Chen (), Pch346, Chongwoo Choe (), Jiajia Cong and Noriaki Matsushima

No 16-20, Monash Economics Working Papers from Monash University, Department of Economics

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 tradeoff where potential dynamic costs can outweigh static benefits. We also discuss policy implications by considering various merger remedies.

Keywords: big data; personalization; tech mergers (search for similar items in EconPapers)
JEL-codes: D43 K21 L13 L41 (search for similar items in EconPapers)
Pages: 60 pages
Date: 2020-12
New Economics Papers: this item is included in nep-com, nep-cwa, nep-law and nep-pay
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