Baptiste Kotras (2020)
Big Data and Society 7(2), pp. 1-14
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.
Extract. “How do these predictive algorithms for personalized marketing change the way corporations know and act upon their customers? As “personalization” constitutes the horizon of countless contemporary algorithmic devices (Lury and Day, 2019; Mackenzie, 2018), I study how this general promise unfolds in action, analyzing at a fine-grain level the discussions and material practices of the actors involved in the conception and use of predictive marketing algorithms. Drawing from STS and the sociology of quantification, I consider the epistemic and political consequences of these very practices and assemblages on the production of consumer knowledge (Bowker, 2005; Diaz-Bone and Didier, 2016; Espeland and Stevens, 2008). In particular, this qualitative study contributes to the reflections on the new forms of social ordering performed by Big Data analytics, and how these technologies shape and account for individuality (Bolin and Schwarz, 2015; Couldry et al., 2016). It focuses on the original ambivalence of personalized algorithmic marketing, which draws on both instrumental and humanistic arguments, as it simultaneously aims to optimize market strategies, and to take better account of persons, i.e. of each unique customer, defined by her specificities, needs and life trajectory. […] This result also suggests that we need to take seriously the moral claims made by data science, which are keys to its growing pervasiveness. Here, the promise of supporting better market relations, more adjusted to individual authentic aspirations, is part of a longstanding criticism of traditional statistical categories, considered as insufficiently precise to do justice to the specificity of individuals (Boyd and Crawford, 2012; Desrosières, 2011).” (p. 2/11)