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Article Dans Une Revue Big Data & Society Année : 2020

Mass personalization: Predictive marketing algorithms and the reshaping of consumer knowledge

Résumé

This paper focuses on the conception and use of machine-learning algorithms for marketing. In the last years, specialized service providers as well as in-house data scientists have been increasingly using machine learning to predict consumer behavior for large companies. Predictive marketing thus revives the old dream of one-to-one, perfectly adjusted selling techniques, now at an unprecedented scale. How do predictive marketing devices change the way corporations know and model their customers? Drawing from STS and the sociology of quantification, I propose to study the original ambivalence that characterizes the promise of a mass personalization, i.e. algorithmic processes in which the precise adjustment of prediction to unique individuals involves the computation of massive datasets. By studying algorithms in practice, I show how the active embedding of local preexisting consumer knowledge and punctual de-personalization mechanisms are keys to the epistemic and organizational success of predictive marketing. This paper argues for the study of algorithms in their contexts and suggests new perspectives on algorithmic objectivity.

Domaines

Sociologie
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Dates et versions

hal-02944537 , version 1 (21-09-2020)

Licence

Paternité - Pas d'utilisation commerciale - Pas de modification

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Citer

Baptiste Kotras. Mass personalization: Predictive marketing algorithms and the reshaping of consumer knowledge. Big Data & Society, 2020, 7 (2), ⟨10.1177/2053951720951581⟩. ⟨hal-02944537⟩
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