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Algorithmic Personalized Pricing

Dr. Pascale Chapdelaine, Associate Professor Windsor Law

ABSTRACT: Price is an essential term at the heart of supplier-consumer transactions and relationships increasingly taking place in “micro-marketplace chambers”, where points of comparison with similar relevant products may be increasingly hard to discern and time consuming. This article critically reviews recent legal and economic academic literature, policy reports on algorithmic personalized pricing (i.e. setting prices according to consumers’ personal characteristics to target their willingness to pay), as well as recent developments in privacy regulation and competition law and policy discourse, to derive the guiding norms that should inform the regulation of this practice, predominantly from a consumer protection perspective. Looking more closely at algorithmic personalized pricing through prevailing and conflicting norms of supplier freedom, competition, market efficiency, innovation, as well as equality, fairness, privacy, autonomy and transparency, raises important concerns about certain forms of algorithmic personalized pricing. The article provides parameters to delineate when algorithmic personalized pricing should be banned as a form of unfair commercial practice. This ban would address the substantive issues that algorithmic personalized pricing raise. Resorting to mandatory disclosure requirements of algorithmic personalized pricing would address some of the concerns at a procedural level only, and for this reason are not the preferred regulatory approach. As such, our judgment on the (un)acceptability of algorithmic personalized pricing as a commercial practice is a sort of litmus test for how we should regulate the indiscriminate extraction and use of consumer personal data in the future.


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