Facebook and Personalised Advertising

Multiple Facebook’s legal battles have rooted from accusations of dominance and monopoly power abuse over smaller rival firms in the market. 

The Federal Trade Commission has accused Facebook of its purchases of Instagram and Whatsapp in 2012 and 2014 respectively as eliminating threats to its market power. Facebook’s expansion prevents new firms from entering the market- thus giving consumers less choices, and they could only succumb to monopoly prices or strategies employed by the firm. Lawsuits were also filed for Google over its anti-competitive conduct online. Google, as a monopoly search engine, crafted exclusionary agreements that allowed solely itself the primary search avenue of online users worldwide, on top of prohibiting pre-installation of rivals. With the competition in search being restricted, the quality and choices offered to consumers are reduced. Furthermore, Google has become increasingly vertically-integrated, by acquiring the operations of its own payment system and data centers. eMarketer reported that Google monopolises one third of digital advertising in the US. These firms are examples of harmful anti-competitive manners including privacy violation in tracking consumers’ data and the use of personalised advertising in securing market power (Sterling, 2019).

Firms collect consumers’ data to design marketing strategies. This could be done through publicly observing customers’ information; customers’ voluntary registration when creating an account; or operational tracking systems. Publicly available information includes data extractable through their devices or operating systems, including IP addresses. Secondly, customers’ voluntary registration will usually cover personal information ranging from users’ names to postal addresses. Tracking systems like tracking cookies are used across websites to match information with firms, allowing an automatic presentation of customised data to consumers. Consumers’ data is first utilised by firms to differentiate products to increase marketability and to depart from catalogs homogeneous to their rivals. However, consumers may or may not be aware of being tracked and this results in potential privacy violation, leading to a deterioration in consumer’s welfare. 

Firms aim to maximise profits through price discrimination, a tactic applied by firms in an imperfect market where customers have no perfect information about the product or when one firm dominates the industry. Price discrimination occurs when firms charge two consumers different prices for two units of the same, or similar products. It extracts the maximum consumer surplus, turning it into producer surplus instead, benefiting the firm. This only works for firms with a certain amount of market power and when there is no or little chance of arbitrage. Price discrimination has three degrees: personalised pricing; bundling of same goods which allows discounts with more quantity purchased; and group pricing, where firms group and charge customers based on indicators of customers’ willingness to pay (WTP) which are observable and verifible. Personalised pricing and group pricing require firms to have sufficient information about the consumers; but bundling does not. Instead, firms offer a range of packages for customers to choose from, based on their own preferences. 

Knowledge of customers’ preferences often is a signal to firms to customise advertisements for their audience, as a means of effective marketing. Advertisements generally have two types: informative and persuasive. Informative advertising improves consumers’ knowledge about existing firms, their products and characteristics. This increases total demand for a good and softens price competition between firms. Increased awareness of available products and their prices increases consumers’ utility (Chamberlin, 1993). Persuasive advertising increases consumers’ WTP, especially effective in a monopoly setting as it allows profit maximisation. The pivot of personalised advertising is maximising its effects, reflective in sales of products or increased awareness, through a customised setting of timing, placement and recipients. Display banner ads were once popular and subsequently saw a surge in their employment; the trend quickly went downhill with 1% click-through rates (MediaMind 2012). Businesses including Facebook and Google thus used ‘retargeting’, which customises displays of ads based on audiences’ recent browsing or shopping behaviours (Helft and Vega, 2010; Peterson, 2013; Sengupta, 2013). Retargeting entails a specific form of targeting, where customers were shown ads from a particular brand previously explored, while visiting a different site. This tactic can be dynamic, advertising a specific product of the first brand, or generic, showing a generic post. The former is more effective, given customers have well-defined preferences and have collected sufficient information about the product (Lambrecht and Tucker, 2011).

Personalised advertising exposes consumers directly to relevant ads with an interest match, catalysing their access to desired goods and services. This benefits consumers who are now better informed, but this also depends on consumers’ own valuation and aspects concerned (Deutscher, 2018).  Moreover, firms observe lower advertising costs after employing targeted advertising. This stimulates further demand for online marketing and potentially leads to higher revenues obtained by sellers, which can then benefit consumers through higher service quality or lower subscription fees. The depth of targeted advertising depends on the amount of consumer data held by firms, but data collection needed for customising advertisements might violate consumers’ data privacy, thus lowering consumer welfare. 86% of surveyed young adults prefer not to be tracked following their browsing history and patterns (Turow et al, 2009). The European Commission concluded the Facebook-Whatsapp merger case with a statement recognising that the market for online advertising was larger than social media, thus ensuring no antitrust concerns and internet users’ data availability remains out of Facebook’s control. The allowance of control over data usage will provide assurance to consumers, making more information available during collection (Goldfarb and Tucker, 2011c). Firms will then alleviate concerns over negative trade-off between advertisement effectiveness and privacy protection (Tucker, 2011b). 

In an imperfect market, a monopoly charges higher prices to customers with higher WTP. This is the appropriation process, which causes these consumers to be worse off as they now have lower consumer surplus. If the firm in question charges low-WTP customers with lower personalised prices, then the market expansion effect occurs since those unable to afford uniform pricing can now afford the products. Given the demand expansion effect outweighs the appropriation effect, personalised pricing increases consumer surplus. However, personalising pricing incentivises firms to degrade their product quality, offering different qualities at different prices to attract consumers with different levels of WTP (Deneckere and McAffee, 1996). Another strategy is exclusionary, where incumbent firms deliberately offer low targeted prices to customers with chances of switching to other providers or new rivals. This attracts customers to stay with the firms, but in the longer term drives out new entrants in the market and reduces the available options for consumers.

Policies including data protection rules and anti-discrimination laws should exist as legal limits to personalised pricing, to ensure a standard of transparency in order to maintain consumers’ trust in online markets (Bourreau, Streel, Graef, 2017). Since Europe implemented privacy regulation policies, firms now require consumers’ explicit consent when collecting information, discouraging anticompetitive manners in the market. This does not eliminate existing monopolies like Facebook and Google, but ensures fairer conditions for incumbent or new entrants and better-protected consumers.

(Written in 2021)

References

Bourreau, M., Streel A., Graef, I., (2017) Big Data and Competition Policy: Market Power, Personalised Pricing and Advertising.

Chamberlin, E. (1933). The Theory of Monopolistic Competition. Harvard University Press, Cambridge,

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Deneckere, R. and McAffee, R.P. (1996) “Damaged Goods”, Journal of Economics & Management Strategy, 5(2), 149-174.

Deutscher, E (2018) How To Measure Privacy-related Consumer Harm In Merger Analysis? A Critical Reassessment Of The EU Commission’s Merger Control In Data-driven Markets

Goldfarb A, Tucker CE (2011a) Online display advertising: Targeting and obtrusiveness. Marketing Sci. 30(3):389–404.38 (2011b) Privacy regulation and online advertising. Management Sci. 57(1):57–71.

Helft, M, Vega, T (2010) Retargeting ads follow surfers to other sites. New York Times (August 29) http://www.nytimes.com/2010/08/30/technology/30adstalk.html?_r=0.

Lambrecht, A, Tucker, C (2013) When does retargeting work? Information specificity in online advertising. J. Marketing Res. 50(5):561–576.

MediaMind (2012) Global benchmarks report—H1 2012. Report, MediaMind, New York.

http://www2.mediamind.com/Data/Uploads/ResourceLibrary/MediaMind_Benchmark_H1_2012.pdf

Peterson, T (2013) EBay opens up its data for ad targeting—Follows lead of Amazon, Google and Facebook. Adweek (April 8) http://www.adweek.com/news/technology/ebay-opens-itsdata-ad-targeting-148469.

Sengupta, S (2013) What you didn’t post, Facebook may still know. New York Times (March 25)

http://www.nytimes.com/2013/03/26/technology/facebook-expands-targeted-advertisingthrough-outside-data-sources.html?pagewanted=all

Sterling, G (2019) Almost 70% of digital ad spending going to Google, Facebook, Amazon, says analyst firm. Marketing Land. (June 17)

https://marketingland.com/almost-70-of-digital-ad-spending-going-to-google-facebook-amazon-says-analyst-firm-262565

Turow, J., King, J., Hoofnatle, C. J., Beatley, A., and Hennessy, M (2009) “Americans Reject Tailored Advertising and Three Activities That Enable It”, Mimeo.

Tucker, C. (2011b) Social Networks, Personalized Advertising, and Privacy Controls. Mimeo, MIT.