In the popular TV game show ‘The Price Is Right’ contestants competed to win a reward by guessing the prices of merchandise. With the advent of personalisation technologies, finding out the ‘right’ price of products and services offered online would become a real challenge for consumers since prices would not be set as a result of the interaction between offer and demand but of how much they would be individually willing to pay.
Personalised pricing can be defined as the practice of setting prices for consumers based on their personal characteristics and behavior . As a result, prices are adjusted in function to the consumers’ ‘Willingness To Pay’ (hereafter WTP) assimilated to the, so far, theoretical scenario of perfect price discrimination.
The economic effects of personalised pricing
Traditional economic theory would tend to suggest that personalised pricing can improve market efficiency. This is because low-end consumers would be able to access products and service which would normally be too expensive for them, while allowing the firm to preserve the profitability of high-end consumers. In order words, consumers with a high WTP would cross-subsidise those consumers that have a WTP below the market price under competitive conditions. As a result, the total welfare in a price personalisation scenario is bigger than under a uniform price scenario.
However, price personalisation does not happen in a vacuum. The technology does not only allow firms to make use of detailed information of their customers’ personal characteristics and consumption habits, but also to implement techniques aiming at lifting the price consumers are willing to pay by exploiting their biases. For example, firms implement different techniques to introduce anxiety on consumers such as ‘last minute deals’ pop-ups or by enhancing competition with other consumers presumably interested on the same offer (e.g. ‘3 other customers are viewing this offer’).
From an economic perspective, that is a very important factor to take into account because, as indicated by existing literature, firms, through the use of unfair practices, can manipulate the demand curve in their own favour therefore increasing the producers’ surplus in detriment of the less sophisticated consumers. As rightly pointed out by the European Commission in its submission to the OECD’s roundtable on personalised pricing in the digital era, EU competition law is concerned about consumer welfare, not total welfare and from this perspective the effect of first-degree price discrimination on consumers is likely to be negative “because the producer captures up to the entire surplus for all consumers, leaving them with potentially no gains from trade”.
The most obvious benefit of personalised pricing would be given by the possibility for consumers to acquire goods and services that they would be otherwise excluded from. This is because firms applying personalised pricing would be able to keep or raise their margin by increasing the price for those with a higher WTP and reduce the price to those willing to pay less. At first this idea seems correct if we take into account the heterogenous expectations of consumers (e.g. some consumers might prioritise quality or branding over price while others prefer or need to mind their wallets.) The opposite can also be true: first, the most affluent consumers can also be the most informed consumers and therefore be more aware of the consequences of the use of personalised pricing and try to avoid it. The less affluent consumers, often more vulnerable to the use of technology, could end up paying more. Secondly, it should not be underestimated that the fluctuation of the consumer purchasing power can play against their own interest in a price personalisation scenario. An example would be seasonal shopping: it is well-know that consumers pay more for goods or services that they need for a certain date, e.g. Christmas, due to the increase of the demand around the same period of time for the same type of goods or services and the hectic of last-minute shopping. In a price personalisation scenario, the WTP can be artificially increased thus allowing firms to extract the maximum possible price from consumers, including for those that would have been under normal conditions below the price of a uniform pricing scenario.
This brings us to the impact of personalised pricing on vulnerable consumers. This is because they are often less sensitive to price increases and could pay significantly more for goods and services under a price personalisation scenario. The distributional impact of personalised pricing is something that should not be underestimated, especially if we look at markets with low switching rates like essential services (e.g. electricity markets) and long-term contracts where inactive consumers can suffer the effects of loyalty penalties. A recent report by the Competition and Markets Authority revealed that consumers in the UK are being harmed by different business practices aiming at locking-in them and gradually increasing prices upon contract renewals. If prices for this type of contracts were to be personalised, it would be increasingly harder for consumers to identify unfair price increases and take action to find another provider.
Finally, this situation becomes more complex when services are bundled. We already know that consumers have difficulties to compare the components of bundled offers. Under a personalised price scenario, they will find it even more difficult to make a meaningful choice between bundled services that would be priced taking into account the user’s profile and consumption habits.
Personalised pricing under EU law
Although personalised pricing is not prohibited per se under EU law, different areas of law are concerned about this pricing technique. In this regard, EU competition law, data protection law and consumer law can simultaneously apply in the case of personalised pricing, not excluding but complementing each other. This has as a practical consequence that – in case of an infringement – different authorities might concur to investigate the same behaviour.
1. Competition Law
First, EU competition law is concerned by the use of personalised pricing mainly due to the fact that the widespread application of this form of pricing could have profound impacts on the demand-side of the market due to its distributional effects. This, in combination with the application of unfair commercial practices, can lead to enlarging the producer’s surplus in detriment of consumers who might end up paying higher prices for goods or services than under a scenario in which prices are set by offer and demand.
Regarding the specific forms of abuse, personalised pricing could be seen as a form of excessive pricing when applied by a dominant undertaking. Although the threshold for intervention in this field is very high, we cannot exclude such scenario if personalised pricing is used in essential markets with limited competition. Personalised pricing could be also considered as an exclusionary abuse when a firm uses personalised pricing to target rival’s customers by offering lower prices in an attempt to foreclose the market. This behaviour can be exacerbated by the use of price monitoring technologies allowing firms to adjust constantly their prices to those of their rivals.
2. Consumer Law
Secondly, EU consumer law is an important tool to fight unfair commercial practices and bring more transparency to this form of pricing. The Unfair Commercial Practices Directive (UCPD) addresses practices which distort or are likely to distort the consumers’ economic behaviour. This would encompass under aggressive practices, a sub-category of unfair commercial practices, spamming consumers with persistent and unwanted commercial communications to push them to make a ‘rushed’ purchasing decision and therefore increase their WTP. Another relevant practice that can concern personalised pricing would be to influence the consumer’s purchasing willingness by providing misleading information related to limited offers e.g. falsely claiming that only few tickets are available. These exploitative practices under a price personalisation scenario could mean consumers paying higher prices.
However, in the EU, neither UCPD nor the Consumer Rights Directive (CRD) require traders to inform consumers when the final price is the result of price personalisation. Under current rules, consumers have the right to receive essential information about the product or service, for example regarding its characteristics or its price. It is worth noting that the proposed Directive on the modernisation and enforcement of consumer law suggests the inclusion of new information requirements related to the parameters of ranking. However, information about the underlying algorithms are not included in the transparency standard. Another example is the Unfair Contract Terms Directive. The current rules are formulated in an abstract way, without giving due consideration to the specificities of data processing, big data, or algorithms. Other pieces of legislation, such as the Price Indication Directive simply do not apply to digital services and do not take into account flexible offers based on algorithms. This is relevant because personalised pricing would de facto impede the ability of consumers to compare prices.
3. Data Protection Law
Finally, European data protection law sets the legal grounds for the processing of personal data in the EU. Since a pre-condition of personalised pricing is the collection of data concerning consumers’ personal characteristics and conduct, the General Data Protection Regulation (GDPR) would usually applies. The GDPR contains a specific provision related to profiling of users. According to Article 22 of the Regulation a consumer (as a data subject) has “the right not to be subject to a decision based solely on automated processing, including profiling, which produces legal effects concerning him or her or similarly significantly affects him or her”. The collection of personal data for the purpose of personalising prices would meet this standard provided that there is no meaningful human intervention in the profiling and that produces legal effects (e.g. the conclusion of a contract between the consumer and the trader applying personalised pricing) or effects that are significantly similar.
According to the guidelines issued by the Article 29 Working Party (now the European Data Protection Board) on the issue of automated decision making under the GDPR, for data processing to significantly affect someone in this context the automated decision must have the potential to significantly affect the circumstances, behaviour or choices of the individuals concerned; have a prolonged or permanent impact on the data subject; or at its most extreme, lead to the exclusion or discrimination of individuals. Automated decision-making that results in differential pricing based on personal data or personal characteristics could therefore very well have a significant effect even if the contract is not concluded if, for example, prohibitively high prices effectively bar someone from certain goods or services. Thus, the use of personal data for personalised pricing based on automated profiling would require the explicit consent of the consumer, unless the provider can claim that it is necessary for the performance of, or entering into, a contract with the consumer or this use of personal data is authorised by specific Union or Member State law.
Although further research would be needed to assess the impact of personalised pricing on consumers and markets, there are reasonable grounds to doubt whether consumers would truly benefit from such pricing technique. Traditional economic theory might provide an optimistic scenario but it is unlikely to reflect how consumers and firms behave in reality: consumers decision-making process is influenced by multiple factors and business would tend to take advantage of that situation to maximise profits. Thus, while the technology is likely to allow higher levels of personalisation, decision-makers need to consider and, where appropriate, adopt tools to mitigate the negative effects that price personalisation could have on individual consumers, on different groups of consumes, and, in what concerns its distributional effects, also on the economy and society as a whole.
BEUC represents 43 independent national consumer associations from 31 European countries. The primary task of BEUC is to act as a strong consumer voice in Brussels and to ensure that consumer interests are given their proper weight in all EU policies.
Within BEUC, Agustín supervises four policy teams (Financial Services, Digital, Consumer Rights and Consumer Redress and Enforcement) and coordinates the organisation’s work on competition law enforcement and policy. He is responsible for providing the consumer viewpoint to the European Commission’s competition directorate in high-profile cases affecting consumer markers.
In 2017 Agustín was elected co-EU chair of the Intellectual Property committee of the Trans-Atlantic Consumer Dialogue, a network of over 75 organisations representing consumers’ interest in the US and the EU.
Agustín obtained his law degree in the National University of Córdoba, Argentina. He studied ICT law in Spain (ICADE, Comillas Pontifical University) and Belgium (CRIDS, University of Namur). He is a Phd. candidate at the University of Bremen, Germany. He often publishes in scientific journals on issues of consumer law and policy. He holds the Argentinean and Belgian nationalities.
He can be contacted at firstname.lastname@example.org
OECD, “Personalised Pricing in the Digital Era – Background Note by the Secretariat”, 28 November 2018, <https://one.oecd.org/document/DAF/COMP(2018)13/en/pdf> (accessed on 14 December 2018)
Perfect price discrimination or first-degree price discrimination is understood as a form of price discrimination where each consumer is charged his or her full willingness to pay.
K. Yeung, “Five fears about mass predictive personalization in an age of surveillance capitalism”,International Data Privacy Law, Volume 8, Issue 3, 1 August 2018, Pages 258–269, <https://doi.org/10.1093/idpl/ipy020> (accessed on 14 December 2018)
Altroconsumo, “C’era una volta un prezzo”, 326 Altroconsumo, June 2018.
 See among others G. Gabaix and D. Laibson, “Shrouded Attributes, Consumer Myopia and Information Suppression in Competitive Markets”, The Quarterly Journal of Economics, May 2006; A. Ezrachi and M. E. Stucke, Virtual Competition, The Promise and Perils of the Algorithmic-Driven Economy, Harvard University Press, Cambridge-London, p.101-116;
European Commission, “Personalised Pricing in the Digital Era – Note by the European Union”, 28 November 2018, p. 5 <https://one.oecd.org/document/DAF/COMP/WD(2018)128/en/pdf> (accessed on 14 December 2018)
Citizens Advice, “A Price of One’s Own. An investigation into personalized pricing in essential markets”, p. 16 <https://www.citizensadvice.org.uk/Global/CitizensAdvice/Consumer%20publications/A%20price%20of%20one's%20own%20final.pdf> (accessed on 14 December 2018)
CMA, “Tackling the loyalty penalty”, <https://www.gov.uk/government/publications/tackling-the-loyalty-penalty/tackling-the-loyalty-penalty>(accessed on 14 December 2018)
 OECD, “Personalised Pricing in the Digital Era – Background Note by the Secretariat”, 28 November 2018, p. 24 <https://one.oecd.org/document/DAF/COMP(2018)13/en/pdf> (accessed on 14 December 2018)
 See European Commission’s Guidance Document on the implementation of the Unfair Commercial Practices Directive, p. 135.
Proposal for a Directive amending Council Directive 93/13/EEC of 5 April 1993, Directive 98/6/EC of the European Parliament and of the Council, Directive 2005/29/EC of the European Parliament and of the Council and Directive 2011/83/EU of the European Parliament and of the Council as regards better enforcement and modernisation of EU consumer protection rules
See BEUC position paper “Automated Decision Making and Artificial Intelligence – A Consumer Perspective”, <https://www.beuc.eu/publications/beuc-x-2018-058_automated_decision_making_and_artificial_intelligence.pdf>
F. Zuiderveen Borgesious and J. Poort, “Online Price Discrimination and EU Data Privacy Law”, Journal of Consumer Policy (2017) 40:347-366
Guidelines on Automated individual decision-making and Profiling for the purposes of Regulation 2016/679