The master's thesis, entitled »Algorithmic pricing and consumer protection«, focuses on the practice of personalizing prices based on data collected on consumers. With increased digitalization and the rise of online sales, more and more data on individuals are available to companies, which are becoming increasingly aware of the economic potential and benefits of using this data. This is also true in the area of pricing of goods and services, where companies are increasingly relying on data and data processing to set different prices for different consumers or consumer groups. The latter is particularly relevant for online sales, which is the focus of this master's thesis.
For this purpose, an outline of how algorithmic price discrimination works is given at the outset, and the concepts of so-called big data and algorithms are introduced.
With technological advances in data analytics and machine learning, companies can use the data collected to create increasingly detailed profiles about individuals, their wants and preferences. In this way, companies can assess with an increasing accuracy the willingness to pay of individual consumers, which also constitutes a core construct of algorithmic price discrimination. As a consequence, companies can set different prices in such a way that the price set is as close as possible to the maximum price that an individual consumer or group of consumers is willing to pay for a particular good or service.
However, such online algorithmic price discrimination raises new concerns, regarding the protection of consumers and their fundamental rights and interests. The next section of the paper therefore takes the consumer perspective, first by outlining the economic impact of the practice on consumers and second by presenting their attitudes towards it.
The main part of the master's thesis focuses on an analysis of the existing European Union (»EU«) legal framework, relevant to this issue of algorithmic price discrimination. The three most relevant fields of law from a consumer perspective are presented, i.e., consumer protection law, data protection law and anti-discrimination law. After their analysis, thesis takes the view that the existing EU law is not yet able to successfully counter the pervasive practice of algorithmic price discrimination. Therefore, the final part of the paper presents further regulatory options and recommendations that could contribute to ensuring a higher level of consumer protection, while at the same time not overly suppressing its benefits and the further development of the digital economy.
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