This chapter deals with a market arrangement at a moment of uncertainty and concern around its continued existence. This arrangement is what I call the impersonal price, or a system of impersonal retail prices, elsewhere also referred to as a system of fixed prices. These phrases describe markets where prices are visible, homogeneous across customers, and non-negotiable. As the chapter shows, impersonal retail prices did not just happen. They are the product of complex interactions between legal regulation, material-technical arrangements, and economic theories, as mobilized in different historical contexts to different socio-political and economic ends.

Over the years, businesses have been trying to identify ways to segment their customer base and engage in price discrimination. The objective is to provide different prices to different consumers based on a range of factors, such as age, location, income, and other demographic characteristics, which are considered capable of revealing the reserve price of buyers. With the increasing use of digital technology, this practice has become even more accurate and sophisticated, leading to the emergence of personalized pricing. This pricing approach utilizes advanced algorithms and data analytics to approximate the exact willingness to pay of each purchaser with greater precision.

The United States is the Wild West of algorithmic personalized pricing. It is practiced (and researched) extensively, possibly more than anywhere else in the world, and at the same time, it is less regulated than in many of the jurisdictions surveyed in this Handbook, most notably the European Union (EU) and China. This is not necessarily puzzling. American corporations have been the driving force behind many of the technological innovations associated with the rise and development of algorithmic personalized pricing. However, there is a long tradition in the US of opposition to regulating markets, and algorithmic personalized pricing exemplifies this approach.

Many of our pressing questions about price personalization concern its current practice and potential regulations. We could be tempted to move directly to those hard questions because many – but not all – consumers, scholars, and regulators already believe with some confidence that price personalization harms consumers or treats them unfairly. In this chapter, I pause to unpack intuitions about harm and unfairness and consider systematically what the normative problems with price personalization might be so that our understanding can inform what we look for in existing practice and what we aim to achieve with new regulations.

In an unprecedented ruling, in 2018, the Brazilian Consumer Protection Authority applied a fine to a popular online travel company named Decolar.com for allegedly favouring foreign consumers over Brazilian residents during the 2016 Olympics held in Rio de Janeiro. The accusation was that Decolar.com had offered hotel reservations at different prices according to the consumer’s location as identified through their internet protocol address, or IP address.

To our knowledge, this is the only case thus far in Brazil that reviewed the practice of charging different prices from different consumers based on their specific characteristics.

The digital age, characterized by the rapid development and ubiquitous nature of data analytics and machine learning algorithms, has ushered in new opportunities and challenges for businesses. As the digital evolution continues to reshape commerce, it has empowered firms with unparalleled access to in-depth consumer data, thereby enhancing the implementation of a variety of personalization strategies. These strategies utilize sophisticated machine learning algorithms capable of attaining personal preferences, which can better tailor products and services to individual consumers. Among these personalization strategies, the practice of personalized pricing, which hinges on leveraging customer-specific data, is coming to the forefront.

Personalized pricing is a form of pricing where different customers are charged different prices for the same product depending on their ability to pay, based on the information that the trader holds of a potential customer. Pricing plays a relevant role in the decision-making process by the consumers, and a firm’s performance can be determined by the ability of the business entities to execute a pricing strategy accordingly. Further, pricing also determines the quality, value, and willingness to buy. Usually the willingness of a consumer depends on transparency and fairness.

Technological developments have enabled online sellers to personalize prices of the goods and services.

As the personalization of e-commerce transactions continues to intensify, the law and policy implications of algorithmic personalized pricing (APP) should be top of mind for regulators. Price is often the single most important term of consumer transactions. APP is a form of online discriminatory pricing practice whereby suppliers set prices based on consumers’ personal information with the objective of getting as close as possible to their maximum willingness to pay. As such, APP raises issues of competition, privacy, personal data protection, contract, consumer protection, and anti-discrimination law.

This book chapter looks at the legality of APP from a Canadian perspective in competition, commercial consumer law, and personal data protection law.

In the era of digital economy, business operators often collect and utilize information such as consumers’ browsing history and past purchases to build user profiles and capture consumer needs. Based on such data, business operators would be able to provide personalized search results for their consumers. Arguably, this mode of operation is a boon to consumers and operators alike. It provides convenience and increases efficiency for the consumers, and their increased likelihood to purchase in turn generates profits and commercial returns for the business operators. In fact, the potential for personalized services is arguably one of the reasons driving the success of e-commerce.

What’s the price of your product? In the past, one would probably assume that by your we mean the product you are selling. With the advent of massive information regarding prospective consumers, we are approaching an era in which your is more likely to stand for the product you are buying.

Firms want to maximize profits, and if they are constrained to charge a single price for every potential consumer, they might leave money on the table. However, if a customer reveals her willingness to pay (the key concept in this chapter), sellers may charge different prices to different people for the same goods or services.

Algorithmic pricing did not arise in a vacuum but is part of a wider phenomenon of using personal data to profile individuals on the market and make predictions about their preferences and behaviour in future market settings. The potential for price personalization is one of the most important and salient aspects of the wider phenomenon of algorithms and big data analytics that have come to dominate consumer market. The personalization of the contract should not be regarded separately from the personalization of other elements of a market relationship, neither theoretically nor from a practical perspective.

With the continued advances in big data analytics and artificial intelligence (AI), it is now possible to rapidly adjust prices of goods and services offered in digital consumer markets. In particular, traders may try to increase their surplus from a purchase based on the availability of a variety of consumers’ data. This may result in different prices being charged to consumers based on their predicted willingness to pay.

The prospects of personalized pricing have sparked a vigorous debate in Europe. Although wide deployment of this practice in the European Union (EU) markets has not been evidenced, it has already become a cause of concern.

Machine learning and artificial intelligence (AI) allow collecting and processing massive amounts of data obtained from people’s online records. Data is of particular importance regarding consumers and their activity in online markets because it allows access to (many) consumers’ personal and family characteristics, as well as prior consumption history. This, in turn, grants the ability to derive design proxies about preferences, interests, and personal valuations of goods and services. Naturally, this has significant economic impact.

Given the enormous size of the population of consumers, the use of big data, through AI, makes the approximation to individual consumer’s preferences, needs, concerns, and interests very accurate.

Of the many concerns triggered by the rapid growth of digital commerce and the expansion of the data-based economy, price personalization occupies a prominent yet peculiar position. For many firms, the availability of big data and refined algorithmic tools has opened unprecedented avenues to learn about consumers’ financial and personal standing, market preferences, and transactional behaviour patterns. Building on these insights, firms have (at least to some degree) obtained an ability to make behavioural predictions about the future conduct of their clients, including their interest in a particular assortment of products, responsiveness to certain forms of advertising, and – not least importantly – their willingness to pay a certain price.