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

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.

Discovering needs for digital capitalism: The hybrid profession of data science

“…Yet these accounts often offer explanations centred on technical skills. The number of social scientific studies on data science is moderately increasing but remains rather low (Brandt, 2016; González-Bailón, 2017; Knox and Nafus, 2018; Kotras, 2020; Passi and Sengers, 2020; Saner, 2019; Slota et al, 2020). The few existing studies tend to take an inside perspective, following the practices of data scientists themselves in their work and quest for institutionalization.…”

“…This paper focuses on data scientists, an emerging group of tech professionals increasingly responsible for capturing, translating and commodifying human experiences through and into digital technologies, performing key operations for so-called ‘surveillance capitalism’ (Zuboff, 2019), ‘data colonialism’ (Couldry and Mejias, 2019) or ‘program earth’ (Gabrys, 2016). Data science originally emerged in the United States and has since been institutionalized within various contexts worldwide (Brandt, 2016; González-Bailón, 2017; Hammerbacher, 2009; Kotras, 2020; Metcalf and Crawford, 2016; Slota et al, 2020). Yet data is not new, and even increased quantities of digital data do not automatically call for the creation of a new occupation to handle large-scale data.…”

Artificial intelligence and mass personalization of communication content—An ethical and literacy perspective

“…Thereby, AI allows to personalize content at unprecedented speed, scale, intensity, and responsiveness. Correspondingly, Kotras (2020) defined mass personalization as “algorithmic processes in which the precise adjustment of prediction to unique individuals involves the computation of massive datasets, compiling the behaviors of very large populations” (p. 2). Thus, mass personalization and algorithms are inextricably intertwined.…”

Geo-Marketing Segmentation with Deep Learning

“…Descriptive methods such as logistic regression or cluster-based techniques face serious limitations. Kotras demonstrated in his study how predictive algorithms can increase the performance of customer segmentation [36].…”