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Cited by 95Ethics and Social Impacts of AIBig Data Technologies and ApplicationsImpact of AI and Big Data on Business and Society

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

Open access
Kotras, Baptiste·2020·Big Data & Society
DOI 10.1177/2053951720951581
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Abstract

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.

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  • Recent ethnographic works describe a re-socialization process of algorithmic categories: to become usable in the organization's routines, audience categories are reprocessed, blended with existing organizational knowledge, renamed, translated into explicable concepts, etc.

    “Recent ethnographic works on data analytic organizations describe a re-socialization process of algorithmic categories: to become usable in the organization's routines, audience categories are reprocessed, blended with existing organizational knowledge, renamed, translated into explicable concepts, etc. (Kotliar, 2020;Kotras, 2020).”

    From How online advertising targets consumers: The uses of categories and algorithmic tools by audience planners (2024) · cites this paper as Kotras, 2020

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  • The paper observes a process of 'hybridization' of demographic categories with algorithmically processed ones such as interests or lookalike audiences.

    “We rather observe a process of “hybridization” (Kotras, 2020) of demographic categories with algorithmically processed ones such as interests or lookalike audiences.”

    From How online advertising targets consumers: The uses of categories and algorithmic tools by audience planners (2024) · cites this paper as Kotras, 2020

  • The articulation between big data and organizational categories can be seen as a condition of their epistemic success, rather than a temporary overlay.

    “this articulation between big data and organizational categories can be seen as a condition of their epistemic success, rather than a temporary overlay.”

    From How online advertising targets consumers: The uses of categories and algorithmic tools by audience planners (2024) · cites this paper as Kotras (2020)

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Research works printed but not linked: 13

  1. Beauvisage T and Mellet K (2019) Mobile consumers and the retail industry: The resistible advent of a new marketing scene. Journal of Cultural Economy 13(1): 1-17.
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  7. Espeland WN and Stevens ML (2008) A sociology of quan- tification. European Journal of Sociology 49(3): 401-436.
  8. Kotras B (2015) L'opinion autoris ee. Requalification commu- nautaire de l'espace social et techniques d' echantillonnage. Revue d'anthropologie. Des Connaissances 9(3): 311-329.
  9. Latour B (1983) Give me a laboratory and i will raise the world. In: Karin K-C and Michael JM (eds) Science Observed: Perspectives on the Social Study of Science. London: Sage, pp.141-170.
  10. Lazarus J (2012) Pr evoir la d efaillance de cr edit : l'ambition du scoring. Raisons Politiques 48(4): 103-118.
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  13. Srnicek N (2017) Platform Capitalism. Theory Redux. Cambridge, UK; Malden, MA: Polity.

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Authors
Kotras, B.
Affiliations
Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (FR)
Corresponding author
Kotras, Baptiste
ORCID
Kotras, Baptiste
Venue
Big Data & Society
Publisher
SAGE Publications
Published
2020-07
Volume
7
Issue
2
ISSN
2053-9517 (ISSN-L)
DOI
10.1177/2053951720951581
OpenAlex
W3086840123
Cited by
95
References
64
Citations by year
2026: 13 · 2025: 26 · 2024: 27 · 2023: 13 · 2022: 8 · 2021: 8
FWCI
9.88
Type
journal article
Language
en
Open access
Licence
Locations
  • Big Data & Society (publishedVersion, cc-by-nc-nd)
  • HAL (submittedVersion, cc-by-nc-nd)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
Keywords
predictive marketing, mass personalization, machine learning algorithms, consumer knowledge, one-to-one marketing, depersonalization, sociology of quantification, epistemic success, customer behavior modeling, big data

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