.

 

Network Effects Lock-in Market Power by Big Data Platforms

Many of these data platforms attract users based on other users participating in those networks, a prime example being social media like Facebook, where consumers become mutually locked-into a service.  This is reinforced as users share information with one another, which in turn means there is data on those networks exclusive to those data platforms.  Metcalf’s Law, named for networking technology pioneer Brian Metcalf, argues that the value of a network increases as the square of the number of users, which means larger networks are exponentially more valuable than even slightly smaller networks, making it extremely hard for runner-ups to thrive in networked industries.[i]

Network effects reinforce market power for already dominant companies in a range of ways. 

  • More Users Attract More Users: Just as the usefulness of telephones exploded in the early part of last century as more people had telephones of their own, so too a social network like Facebook become more useful as a higher proportion of ones’ friends of colleagues are participating.   For those looking to reach a broader audience through social networks, participating in the largest networks like Facebook or Twitter give you far greater chances for shares and reshares that take your message “viral” on a larger scale.  Or as Niraj Dawar writes at the Harvard Business Review blog, “There is only one square in the global village, and it is run by Facebook. Being on a different square from everyone else doesn’t get you anywhere — you just miss the party.”[ii]
  • Dominant Networks Attract Third Party Developer Support: A higher number of consumers of a product or service attracts more third party developers creating other products that work with the core product, which in turn attracts more users to that core product.  A prime example is historically how so much software written for the dominant Windows desktop operating system strengthened its desktop dominance, while the number of software apps developed for the Apple mobile operating system and for the Android mobile software deters users from buying phones/tablets in other networks.
  • Networks Attract Advertisers: Large networks of users obviously attract advertisers based on the scale of opportunity to reach consumers.  However, the dynamics of scarcity in screen space online creates pressure on advertisers to spend on the largest data platforms.   With Google, for example, since ads are listed in order based on the most successful bid at auctions for keywords, any company has to worry not only about reaching the audience but at being better positioned on the screen then their nearest competitor.  Companies often feel they can’t afford to cede the largest bundle of consumers to the competition so the largest networks get the most bids, driving up advertising prices per user compared to smaller networks.[iii]

Because personal data created by users is often linked to other data within the service or to personal information of other users, it is often hard for consumers to switch to an alternative service without a lot of effort and often losing many of the contextual links, comments by other users and other information embedded in the architecture of particular data platforms, making any ability to port all personal information to another service nearly impossible.[iv] 

 

[i] See David A. Balto, “Networks and Exclusivity: Antitrust Analysis to Promote Network Competition,” 7 Geo. Mason L. Rev. 523, 524 (1999) (addressing emerging issues involving exclusivity and networks in antitrust analysis and noting that “what distinguishes a network from most other production facilities is that it also enjoys economies of scale or scope in demand”); Steven C. Salop & R. Craig Romaine, “Preserving Monopoly: Economic Analysis, Legal Standards, and Microsoft,” 7 Geo. Mason L. Rev. 617, 618 (1999) (“finding of monopoly power is not implausible in a market subject to scale economies and strong network effects”).

[iii] Benjamin Edelman and Michael Schwartz, Optimal Auction Design in a Multi-unit Environment:  The Case of Sponsored Search Auctions, Working Paper Harvard Business School, Dec. 2006, http://www.benedelman.org/publications/optimalauction-120806.pdf-  (“the more advertisers that bid for a particular keyword, the higher are search engine revenues for that keyword.”); see also Joseph Farrell & Paul Klemperer, “Coordination and Lock-in: Competition with Switching Costs and Network Effects,” The Handbook Of Industrial Organization 2008 (M. Armstrong and R. Porter eds., 2007); Paul Klemperer, “What Really Matters in Auction Design,” 16 J. Econ. Persp. 169, 172 (2002). 

[iv] Lanier, Who Will Own the Future? (“Even if you capture every little thing you had uploaded, you can’t save it in the context of interactions with other people. You have to lose a part of yourself to leave Facebook once you become an avid user.”)