Data-driven mergers and personalization - Zhijun Chen; Chongwoo Choe; Jiajia Cong; Noriaki Matsushima (Osaka University, Institute of Social and Economic Research (ISER), 2020)
Recent years have seen growing cases of data-driven tech mergers such as Google/Fitbit, inwhich a dominant digital platform acquires a relatively small rm possessing a large volumeof consumer data. The digital platform can consolidate the consumer data with its existingdata set from other services and use it for personalization in related markets. We developa theoretical model to examine the impact of such mergers across the two markets that arerelated through a consumption synergy. The merger links the markets for data collectionand data application, through which the digital platform can leverage its market powerand hurt competitors in both markets. Personalization can lead to exploitation of someconsumers in the market for data application. But insofar as competitors remain active, themerger increases total consumer surplus in both markets by intensifying competition. Whenthe consumption synergy is large enough, the merger can result in monopolization of bothmarkets, leading to further consumer harm when stand-alone competitors exit in the longrun. Thus there is a trade-off where potential dynamic costs can outweigh static bene ts.We also discuss policy implications by considering various merger remedies.
Keywords: D43; L13; L41; K21; 330; big data; personalization; tech mergers