What Are Data Network Effects and What Is Their Impact on Market Competition?

The Idea in Brief

A data network effect occurs when the collection of an additional data point makes a product or a service more valuable to its users. Thus, data network effects differ from people network effects, which occur when an additional user makes a product or a service more valuable for existing users. Once a product or service has reached the data tipping point and starts exhibiting data network effects, it no longer depends on the addition of new users to increase in value: while the addition of new users is sufficient to generate additional data points and thus enhance the value of the product or service, it is not necessary to add new users for this to happen. Existing users’ engagement is enough to create a continuous increase in value that makes it extremely difficult for rivals to compete and therefore often leads to market domination. Reduced competition in the market in turn has several negative consequences: higher prices for business users (which will potentially be passed on to private users), less innovation, decreased competitiveness in future markets, and serious harm to the functioning of democratic systems.

Introduction

Data network effects allow companies to build up protective data moats and therefore become dominant in their respective markets.[1] When a company has collected enough behavioral and profile data from its users, it reaches the “data tipping point” at which it can match its users’ preferences with high precision. This enables the company to show the users highly personalized and relevant content, whether it is search results, news articles, products, or something else. Because the users value the precision with which their preferences are being matched, it becomes very difficult for competitors to steal these users away. At this point, the company has successfully built up a data moat that protects its position in the market. Highly personalized and relevant content moreover fosters clicks and user engagement. This engagement creates additional behavioral data that enable the company to match its users’ preferences even more precisely in the future[2] further entrenching the company’s position in the market. Google Search, Facebook, and Amazon illustrate this dynamic: All of them dominate markets (online search, social networking, and e-commerce, respectively) because the vast amount of data they have collected on their users allows them to match users’ preferences better than their competitors can. Their efficiency at matching users’ preferences in turn generates new data that enable them to become even more efficient, making it exceedingly difficult for rival firms to compete.

How Data Network Effects Differ from People Network Effects

Data network effects can be defined in the following way: A data network effect occurs when the collection of an additional data point makes a product or a service more valuable to its users. Thus, data network effects are indirect and differ from direct network effects, or people network effects,[3] which occur when an additional user makes a product or a service more valuable for existing users.[4] Products or services exhibiting data network effects do not depend on the addition of new users to increase in value: while the addition of new users is sufficient to generate additional data points and thus enhance the value of the product or service, it is not necessary to add new users for this to happen. The engagement of existing users with the product or service is enough for new data to be generated and the value of the product or service to be increased. To illustrate this, one can compare the value of a telephone network, which exhibits people network effects, to the value of an online social network, which exhibits data network effects, in month X. If no new users join and no existing users leave the telephone network in month X, the value of the network will not change from the perspective of the existing users.[5] However, if no new users join and no existing users leave the online social network in month X, the value of the network still increases because the engagement of the existing users with the network generates data points that enhance the value of the network from the perspective of the existing users.

Data Network Effects and Their Consequences

Several consequences follow from this. First, products and services that display both data and people network effects do not need to grow their user base in order to become more valuable for existing users. Users’ engagement with the product or service is enough to lead to an increase in value. Second, if the user base of a product or service exhibiting both people and data network effects does grow, the value of the product or service to existing users increases at an even faster rate. In that case, people and data network effects combine to make the product or service ever more valuable to existing users through improvements in preference matching and through growth in the number of people to whom existing users can connect. Third, because a product or service exhibiting data network effects increases in value every time existing users engage with it, it is exceedingly hard for new entrants in the same market to steal away users from the product or service in question.[6]

[1] Vikas Kathuria, ‘Greed for data and exclusionary conduct in data-driven markets’ (2019) 35 Computer Law and Security Review 89

[2] Viktor Mayer-Schönberger & Thomas Ramge, Reinventing Capitalism in the Age of Big Data (John Murray 2018)

[3] Direct network effects can also be called people network effects because they occur when a product or a service becomes more valuable to its users as more people use it.

[4] Jeffrey Church, Neil Gandal & David Krause, Indirect Network Effects and Adoption Externalities (Foerder Institute for Economic Research, Working Paper 2–30, 2002); Nicholas Economides & Steven C. Salop, ‘Competition and Integration Among Complements, and Network Market Structure’ (1992) 40 The Journal of Industrial Economics 105; Joseph Farrell & Garth Saloner, ‘Standardization, Compatibility, and Innovation’ (1985) 16 The RAND Journal of Economics 70; Anu Hariharan, ‘All About Network Effects’ (a16z, 7 March 2016) <https://a16z.com/2016/03/07/all-about-network-effects/> accessed 29 February 2020; Michael L. Katz & Carl Shapiro, ‘Network Externalities, Competition, and Compatibility’ (1985) 75 The American Economic Review 424

[5] This is true unless one assumes that existing connections enabled by the network decay in value over time. In that case, the telephone network might become less valuable in a month in which no new users join and no existing users leave.

[6] ‘Competition Law and Data’ Autorité de la Concurrence and Bundeskartellamt (n 10)