I. Introduction

Consumer data has become the new raw material for the global economy and today serves as a fuel for online business activities. Enterprises have recognised that ‘collection and processing of data’ would give them a competitive advantage over other competitors as it significantly reduces the operating cost for the firms in the digital sphere. However, The consumer data may not be easily accessible for the new entrants. The Federal Trade Commission and other agencies have highlighted that the costs incurred in collecting, aggregating, and updating the data can create significant barriers to entry in the market.[1]Additionally, having access to huge amount of consumer data also helps enterprises acquire a competitive edge and entry barriers for the potential competitors who may not have the access to an equivalent economic ability required to carry out complex data analytics. It is has been deliberated that ‘access to data’ leads to ‘tipping’ of the market creating a foreclosure effect for the potential competitors in the market[2]. There are arguments both in favour and against the foreclosure ensued by data processing and analytics. Hence, analysing the effects created by data collection has become essential specially in the two-sided markets for instance, Platform market. Such markets cater to the needs of two different sets of consumers (Suppliers and customers), largely relying on data and its processing to run their business activities and to garner more profits. Although, competition agencies have recognised the role played by data in ascertaining cases brought forth them, it is still far from being actually applied in practice being a non-price factor and criticism attached to it.

The collection of data may not be much of a concern, however, the way enterprise use consumer data or leverage it must be assessed. It is no secret that most of the digital markets have very few market players operating and competing fiercely and the ones present have become too big in size. This exponential growth has put some of the big incumbent in a position that largely remains unaffected by potential entrants. Having access to database and its algorithms, makes it difficult for the entrants to get access to the data bases which otherwise would require undertaking multiple R&D initiatives and incurring huge costs in the process. The cross-border commercial use of the data raises concerns like privacy, data protection, foreclosing the market that would ultimately affect the consumers and their interests. Being a relatively new aspect, it is required to first understand as to what can constitute consumer data and how far is it relevant to consider it as a factor in any assessment. The paper thus aims to study the meaning, relevance of data and the potential threats created by it of ‘foreclosure’ that may arise from data along with how it have been interpreted by other prominent jurisdictions.

(A) Research Questions

The research questions thus are drilled down to the following –

1. What can constitute consumer’s data?
2. While adjudicating, how far can data being a non-price factor be considered relevant?
3. How the accumulation of consumer’s data helps enterprises in leveraging their positions and create a foreclosing effect for the new entrants?
4. What are the major takeaways and solutions that can address the impact of data over competition law enforcement across the world?

(B) Literature Review

The existing literature has advanced many crucial strands and contradictory opinions over the competitive relevance of consumer data. While few suggests that use of data cannot be used anti-competitively others support the view that it requires enforcement agency’s attention. Richard Posner suggests that digital markets are characterized by uniquely low barrier to entry[3]. According to Manne and Sperry, the antitrust concerns related to data are mostly a myth. It is contented that data is non-rivalrous in nature and can be procured easily thus foreclosure effect is not possible in data-driven markets[4]. Organisation for Economic Cooperation and Development(OECD, 2016), first took notice of the implication related to data and has been quite vigilant about the issue and its anti-competitive concerns by studying data-driven innovation cycle. Adam Candeub in his work suggested that digital platforms are able to erect barriers and prohibit the entry of other potential competitors by creating proprietary based ecosystems. Such an ecosystem makes it difficult for users to shift to any other platform owing to high switching costs involved.[5] Zuboff discusses that Consumer data helps the digital firms personalize the output and hence consumers now have gradually turned into a raw material/source for such enterprises. Prima facie, it might seem good as it helps in making cyber space more personal but it may lead market to become more concentrated, foreclosing it for other medium and small enterprises.[6]. John M. Newman discusses the reasons for entry barrier in the market and factors like massive sunk costs, acquiring market players operating in the horizontal market, access to proprietary datasets as some of the reasons for foreclosure among others[7]. A particular type of strategic conduct by an incumbent even if legal can also disincentivize entry and innovation. Josh Obear asserts that digital firms particularly that are dominant have the ability to replicate the features offered by new entrants and are able to ride freely and thus may restrict the potential competitors from trying to enter the market.[8] The argument of creation of high barriers to entry is further reinforced by the fact that the value added by data creates a long lasting edge and that its marginal value depreciates very slowly creating stronger barriers. The view is supported by Argenton and Prufer who state that in markets like that of search engines, large amount of data collected in the past helps in offering high quality goods and services which is the ultimate goal of competition law and policy.[9] On the other hand, Kennedy J. suggests that use of data does affect competition and high concentration of data in possession of few big players does represents a barrier.[10] Use of data coupled with network effect gives the incumbents and other firms operating an edge which lead to creation of barriers for others. The depreciation of consumer data takes time which is the reason for a new entrant not being able to match up to the incumbent’s years of learning specially for markets such as search engines.[11] Thus, it is acknowledged that due to the network effects and entry barriers it is difficult to maintain a competitive position in the market with incumbents like Google for instance.[12] Further, Roger McNamee also suggest that by virtue of increasingly rapid learning process like ‘Artificial Intelligence’ that run over accumulation of exponential value of datasets, it’s easier for the firms to reach a critical mass in a digital market[13]. Oxera, The European Economics Consultancy Group while deliberating upon digital markets pointed out that a large share of data residing with a few firms could also represent a barrier to entry and limiting competition. In addition, consumers do not always know or understand where or how their data is being collected or used having no control over it. It is thus essential to study the role of consumer data and the way it limits competition through foreclosure.

II. Meaning, relevance and foreclosure effects of consumer data

(A) Meaning of Consumer Data for the purpose of Antitrust Scrutiny

The authorities have mostly referred to the term Big Data recognising that humongous amount of data is floated and processed on internet every minute. It describes the ability of storing the information with the help of technological tools, its aggregation, processing, information overload that invades businesses and society. However, consumer data differs from Big data and it can be referred as one of the categories of Big Data. Data can take various forms.

As the consumer base operating over internet expands, it leads to huge amount of information being created through interaction among various groups of people. Data differs on the basis of how it has been floated over the cyberspace and can be largely divided into – a) Volunteered Data – the one which consumers share intentionally and while being aware , an example of it could be the sharing of personal details, photos, location etc. b) Observed Data which has been acquired through tracking the activities of the consumers over online platforms, an example of which can be targeted advertisements being floated based on the search history of any user, and recommendations of webpages based on the search history c) Inferred data which is neither provided or acquired from the consumer but is based on the derivation of volunteered and observed.[14]

The World Economic Forum identified the three types of consumer data and how it has become an asset for the enterprises. In addition to the categories enumerated above, OECD recognised another category named   Acquired data[15], that can be obtained from data brokers commercially or even through non-commercial means as well. This is mostly contractual in nature with terms of use attached to it.

(B) Relevance of Consumer Data – A Non-Price Factor

The focus of antitrust agencies have shifted to the analysis of non-price factors in addition to price factors. Data, as a non-price factor, generated from the consumer base helps enterprises to procure competitive advantage and is today increasingly used to maximise their profits. The process of collecting end-user’s information isn’t peculiar to the online markets but in brick and mortar structure, the process was quite cumbersome, slow and required resources to gather the required information. A stark distinction between the use of data in offline and online mode is that in latter, a relatively large amount of data can be accessed and processed rapidly. Focusing on an individual consumer and providing personalised services is now much more feasible with the advent of new technologies. It not only collects the information but user and search behaviour are also tracked and processed to produce effective results.  The Behavioural Value Reinvestment Cycle[16] shows how data is collected, assessed, processed and is exploited through internal analytics and programming, used as an opportunity and is sold to advertisers using consumers as an accessory and consumer’s data as a resource to fuel their profits. Hence, making services personalised become easier for the enterprises having access to such data. Huge investment is poured into data analytic tools which helps in increasing switching costs for the consumers by working to build a  loyal consumer base.[17] The relevance of data thus cannot be ignored and requires discussion when tactics in digital markets are questioned and in markets where human experience is used as a raw material. Another way of carving out the relevance of data could be to analyse it through switching costs involved. High Switching Costs for any consumer does act as a barrier for other firms in the digital space. It makes customer sticky as the learning from existing customers is translated into improvements in the product or services, not only for the future consumers but for the existing ones as well. The access to personalised database also act as a deterrent for consumers to shift to any competitor or a new entrant.

(C) Consumer Data and Market Foreclosure

The efficiency involved in the data driven markets is based on the ability of enterprises to collect and process the consumer’s data subsequently monetising it.[18] One such example could be of targeted advertisement which creates benefits on one hand for the consumers while foreclosing the market for other enterprises by executing exclusive agreements with the advertisers and sellers. However, it can be argued that access and use of data certainly cannot act as a barrier being easily available to all the market participants and using it should not be considered as creating any anti-competitive effect. The involved  efficiency has resulted in agencies allowing the mergers and joint venture to happen however, with such ventures, enterprises end up getting access to huge amount of data making market more foreclosed for others. Hence, the other side of the debate cannot be completely ignored which argues that Consumers do not actually get the services for free and their personal data is actually being used as a currency which is paid by the consumers in return of the services provided and that there are negative implication to it one of them being creation of high and strong entry barriers. The investments needed in physical assets are significantly lower than those required in industries typically associated with high barriers to entry, such as utilities. In fact, small online search engines such as DuckDuckGo, Ecosia, Yahoo etc. have managed to enter and remain viable.[19]

One view suggests that these markets are characterized by high barriers to entry, due to supply-side economies of scale, switching costs and demand-side network effects. Accordingly, dominant firms’ competitive advantage stems from the inherent features of the market in the form of entry barriers, making it all but impossible for other businesses to compete on the merits.[20] The switching cost barriers are low as competitors are believed to be just a click away. Consumers instead of making a choice between different platforms, can resort to Multi-homing. Multi-homing tends to intensifies competition and is believed to reduce barriers in the market[21]. However, this argument tends to fail when different platform earlier existed as competitors are subsequently acquired by an incumbent and data over one is readily available and is shared between such platforms.

With the advantages attached to consumer data and its use, there are negative implications attached to it as well, one of them being the foreclosure of the market for existing and potential competitors. Rapid learning cycles peculiar to the online platforms makes it easy for the enterprises to have a quick access to the market but with time it makes it harder for competitors who do not have a quick adaptive enhancement mechanism in place and can lead to foreclosing of the market for such firms even though productive but are at a disadvantage in terms of machine-led learning. Another impediment that arise as a result of such machine-led learning is strong network effect that are data enabled. In such cases, even if the platforms stops innovating, it is not easy to overcome the network effect which continuous to create value for the enterprise. Thus users would want to be on the same social platform due to strong network effects as other users. Thus, it might to a situation where the innovation gets stifled because of weak and almost no other competitor in the market to compete with the incumbent. One of the major reason for stifling of innovation can be the reliance placed over data to make the platform more personalised and offer valuable services based on the data so collected which is not available to the new entrants at the very outset. One of the argument could be that platform do not innovate but just personalise the use based on the data collected which hits the very foundation of competition which requires innovation to be brought to the market.

Given the time constraints, it is difficult for competitors to offer product that can compete and is also equally valuable compared to an incumbent’s product in cases where the latter has created a loyal consumer base by leveraging the inter-play of data and network effects. Thus, idea of competition being just a click away doesn’t make sense in practical due to high switching costs involved.

Another factor is the ‘Marginal Utility’ that helps in asserting the satisfaction of consumers in the market and is believed to depreciate with every additional unit being consumed. The economics of Marginal Utility tend to differ in the digital market. Marginal value of the learnings derived from consumer data remains high giving enterprise an advantage over its rivals. However, it has been argued by scholars that such enterprises soon experience a point where the marginal value begins to decrease and the longer it takes to drop/depreciate, the stronger the entry barrier is[22]. Hence, when data-driven enterprises experience a slow marginal fall, it becomes a determinant for barrier to entry. Data offers this leverage to the enterprises and it is difficult to break the position acquired by the enterprises built on consumer’s information. An example could be that of Facebook and Google that consistently keeps the software updated along with the activities done by the users. Few examples from real life scenarios could be – Tracking the location and asking to review or rate a particular place on Google Maps, asking users to continue watching the video they left in between over Facebook, Tracing the pages that are visited frequently on one platform and suggesting those pages on other platform (for instance Facebook and Instagram). Not only this, it suggests the users to visit the account of people ‘whom users may know’ based on the access they have to contact list of such users.(through applications like WhatsApp).

Thus, it is difficult to break a position which runs on such intricate collection of data who’s depreciation value is low and is of great value to the enterprises. An example could be of Microsoft and Google where former even after investing millions in the market has failed to shake the dominance of the latter. Due to the externalities associated with data, the combination of identified and unidentified consumer data can assist firms in gaining insights about individuals or to facilitate activities which makes it difficult for other players to find a place amongst the already established players in the market. This either leads them to exit or drop the idea of entering into such market at the first instance.

Department of Justice, U.S. recognises that in its effort to enter into any market wherein collection and processing of data plays a major role, a firm may face barriers in form of collecting and constructing a database through systematic organisation of thousands of sources, developing an expertise in a particular market or a country, etc. Therefore, a new entrant or an expansion of an already existing small firm is unlikely to dislocate a position established by an incumbent[23].

It has been considered by FTC that it is difficult for other firms to replicate the data generated internally by big enterprises specially in cases wherein the data collected is used as an input and to be able to offer services to downstream cross-platform audience.[24] Enterprises having access to individual-level demographic data was considered as a barrier to entry by the FTC.[25] In addition, the authority expressed concerns over acquisitions undertaken by monopolist making it even more difficult for potential competitors to enter the market and effectively compete.[26] The existence of data creates a barriers which appears self-reinforcing and thus can be of a major concern. Collecting proprietary data in itself may not help a potential entrants as with time the incumbent players are able to acquire a position which is based on working of network effect.

Even in presence of the incumbent, the structure of data driven markets is such that it keeps witnessing disruption. It cannot be certainly predicted that there would be new players in the near future trying to enter the market but competitive pressure has at regular intervals disrupted the incumbents activities.[27] There have been instances wherein though only initially but new entrants were able to shake the position of dominant enterprises forcing them to rethink their competition strategies. It is essential thus to let consumers have more control over their data.

III. Conclusion and plausible solutions

The changing market dynamics have made it imperative for the agencies to consider consumer data related aspects. Data provides a competitive edge, the possession and processing of which puts other potential competitors at risk of losing out on the access to market. A foreclosure of competition acquired through non-price factors can lead to exploitative abuse. It can function to the determent of customers and for players on the other side of the market owing to multi-sided interactions. It is not contested that using data has benefitted the customers in terms of enhanced goods and services being offered however, at the same time market foreclosure induced through use of data has reduced the possibilities of new entrants to enter and offer their services. Agencies like Federal Trade Commission and Organisation like OECD and ICN have made considerable development however, the jurisprudence over data and market foreclosure is still developing. It still needs to be given much awaited attention in countries from where major part of user base is procured like India.

It can be suggested that stricter regulatory conditions shall be imposed on the incumbent that can assist in making the market more open and receptive to new market players. There’s a growing debate regarding data portability[28] thus if the personal data is made portable across platforms, it would be easy for consumers to switch between different enterprises’ services and behave more rationally.[29] Additionally, the antitrust agencies should keep under its purview the fact that just the possession of data by firm does not yield foreclosure alone but the features on which such market functions also have a role to play. Thus, it is essential to strike a balance among the two competing interest while delving into cases involving data related issues that are likely to intensify in future.

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FOOTNOTES

[1] Federal Trade Commission, United States (2019)

[2] ICN, Unilateral Conduct Working group, ‘Survey Report on Substantial Market Power in Digital Market’ (July 2020) https://www.internationalcompetitionnetwork.org/wp-content/uploads/2020/07/UCWG-Report-on-domin ance-in-digital-markets.pdf.

[3] Richard A. Posner, “Antitrust in the New Economy”, Antitrust Law Journal 68 (2001)

[4] Geoffrey Manne; Ben Sperry ‘Debunking the Myth of a Data Barrier to Entry for Online Services’ (March 2015) https://truthonthemarket.com/2015/03/26/debunking-the-myth-of-a-data-barrier-to-entry-for-online-services/.

[5]Adam Candeub, ‘Behavioural Economics, Internet Search, and Antitrust, A Journal of Law and Policy for the Information. Society, 408 (2014)

[6] Shosana Zuboff,”The Age of Surveillance Capitalism” HLR, Ch. 3 (2019)

[7] John M. Newman, Antitrust in Digital Markets, Vol. 72:5, Vanderbilt Law Review, 1497 (2019)

[8] Josh Obear, ‘Note, Move Fast and Take Things: Facebook and Predatory Copying’, CBLR, 994 (2018)

[9] Argenton and Prufer, ‘Search Engine Competition With Network Externalities’, JCLE (2012)

[10] J. Kennedy,‘The Myth of Data Monopoly: Why Antitrust Concerns About Data Are Overblown’, ITIF, 7 (2017)

[11] Julian Wright and Andrei Hagiu, ‘When data creates competitive advantage’ HBR (2020)

[12] Terrell Mcsweeny & Brian O’dea, ‘Data, Innovation, And Potential Competition In Digital Markets – Looking Beyond Short-Term Price Effects In Merger Analysis’, FTC (2018)

[13] Mcnamee, ‘The Antitrust Case Against Facebook, Google, Amazon And Apple’, WSJ. (Jan. 2018), https://www.wsj.Com/Articles/The-Antitrust-Case-Against-Facebook-Google-Amazon-And-Apple-1516121561.

[14] ‘Personal Data: The Emergence of a New Asset Class’ , World Economic Forum (Jan. 2011)

[15]‘Consumer Data Rights and Competition’, OECD (June 2020) https://one.oecd.org/document/DAF/COMP(2020)1/en/pdf.

[16] Andreas Herteux, ‘Behavioural Capitalism and Surveillance Capitalism – A Comparison of Two Interpretations of a Development of Capitalism’, (Oct. 2019) https://www.andreasherteux.com/2019/10/11/behavioural-capitalism-and-surveillance-capitalism-a-comparison-of-two-interpretations-of-a-development-of-capitalism/.

[17] John F. Quigley, ‘Consumer Behaviour in Digital Markets’ (May, 2015) http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.937.5618&rep=rep1&type=pdf and ‘Unlocking digital competition’, Report of the Digital Competition Expert Panel (2019) https://www.gov.uk/government/publications/unlocking-digital-competition-report-of-the-digital-competition-expert-panel.

[18] Daniel Sokol & Roisin Comerford, ‘Antitrust and Regulating Big Data’, 23, GMLR, 1129 (2016)

[19] Zingales L., Rolnik G. & Lancieri F. M. ‘Stigler Committee on Digital Platforms’, (2019) https://research.chicagobooth.edu/stigler/media/news/committee-on-digital-platforms-final-report.

[20] Jordi Casanova, ‘Online Search Competition and Risk of Unintended Consequence of data access’, Competition Policy International (Nov. 2020)

[21] Facebook/Whatsapp Merger, Case No. COMP/M. 7217

[22] Julian Wright and Andrei Hagiu, ‘When data creates competitive advantage’, HBR (Jan-Feb 2020) https://hbr.org/2020/01/when-data-creates-competitive-advantage.  

[23] Case No. 1:08-cv-00262, United States v. Thomson Corporation and Reuters Group (2008)

[24] Supra Note 20

[25] Nielsen Holdings N.V. and Arbitron Inc., FTC (2014)

[26] Supra Note 13

[27] Jacob Kucharczyk, ‘Why Data is not a barrier to entry into Digital Market’(May 2015) https://www.linkedin.com/pulse/why-data-barrier-entry-digital-markets-jakob-kucharczyk.

[28] Spencer Weber Waller, ‘Antitrust and Social Networking’, 90, NCLR, 1788 (2012)

[29]Germany Federal Cartel Office, ‘Digital Economy’ (Feb. 2019) https://www.bundeskartellamt.de/Shar edDocs/Publikation/EN/Pressemitteilungen/2019/072_02_2019_Facebook.pdf?__blob=publicationFile&v=2.