Pricing music using personal data: mutually advantageous first-degree price discrimination
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Pricing music using personal data: mutually advantageous first-degree price discrimination Thierry Rayna · John Darlington · Ludmila Striukova
Received: 17 September 2013 / Accepted: 5 August 2014 © Institute of Information Management, University of St. Gallen 2014
Abstract In addition to customized products and services, personal data also enables personalized pricing. However, consumers are often unwilling to accept being price discriminated for fear that they would end up paying more for the same product or service. This article demonstrates that by rewarding consumers for disclosing personal information it is possible to achieve a situation where first-degree price discrimination is mutually advantageous and both buyers and sellers gain by adopting such a pricing model. The conditions required for this to happen are investigated and the impact on social welfare is discussed. Finally, the article considers the robustness of this model when consumers adopt an opportunistic behavior which consists in manipulating personal data in order to masquerade as a consumer with a lower willingness to pay. Keywords First-degree price discrimination · Digital economy · Pricing models · Personal data · Privacy JEL Classification D42 · D82 · L11 · L82 · L86
Responsible editor: Sarah Spiekermann T. Rayna () Novancia Business School Paris, 3 rue Armand Moisant, 75015 Paris, France e-mail: [email protected] J. Darlington Imperial College London, 180 Queen’s Gate, London SW7 2AZ, UK e-mail: [email protected] L. Striukova University College London, Gower Street, London WC1E 6BT, UK e-mail: [email protected]
Introduction Personal data and their aggregation as ‘big data’ have increasingly become a central focus of the research devoted to electronic commerce. In particular, the personalization of products and services is generally considered as one of the key outcomes of the usage of big data. Beyond the creation of tailored products and services, another important aspect of big data is that it also enables price personalization. As mentioned in Spann et al. (2010), electronic media have radically changed price-making decisions. The massive amount of personal data that can be collected via electronic networks provides means to accurately assess the consumers’ willingness to pay, hereby enabling first-degree price discrimination.1 Although other forms of price discrimination (e.g. versioning, freemium) have been commonly used since the early days of the internet, examples of first-degree price discrimination online are still exceedingly rare. The main reason for that is not, however, a technical one. Indeed, in 2000, Amazon delivered a proof of the feasibility of this form of price discrimination when it started to charge its consumers different prices for the same product, based on an estimation of each consumer’s willingness to pay for the product.2 At the time, the willingness to pay of consumers was calculated based on the (crude) information contained in cookies. However, this strategy rapidly bac
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