Personal Data and Collective Value : Data-driven personalisation as network effect

Economists, lawyers and regulators are becoming increasingly attuned to the challenges posed by network effects in the digital economy: namely, the phenomenon whereby a platform become increasingly valuable to its users, the more other users gravitate to that platform. Whether user-generated content on Youtube and Facebook, proprietorial messaging services such as Whatsapp, or two-sided markets such as Uber and Airbnb, it is now widely recognised that many of the most successful technology businesses in the world today enjoy a dominance based upon achieving a critical mass of users, which makes it difficult for less well-used platforms to compete. What is less widely recognised is that data-driven personalisation operates in a comparable (albeit not identical) manner: as the volume of users increases, personalisation becomes ever more sophisticated, generating a “second-order” network effect that can also have significant implications for the viability of competition.This paper unpacks the distinction between first-order and second-order network effects, showing how both can create significant barriers to new market entrants wishing to challenge established incumbents. It also explores the implications of this analysis for policymakers tasked with deciding how to regulate and tax companies that benefit from these network effects