Rethinking Consumer Knowledge in Data-Driven Markets

Abstract

This paper examines how data-driven marketing restructures consumer knowledge in the digital economy. At the micro level, recommendation systems, personalization, and dynamic pricing alter how consumers perceive options, make decisions, and learn about markets. At the meso level, firms adopt algorithms as infrastructures of knowledge production, embedding predictive models into strategy and innovation. At the macro level, platforms and regulators define the rules of visibility, accountability, and access, shaping the distribution of knowledge across economies. Case illustrations of Netflix, Amazon, Alibaba, and fintech platforms show how these mechanisms operate in both developed and emerging markets, revealing tensions between efficiency and fairness, autonomy and personalization, and innovation and inequality. The paper argues that data-driven marketing should be seen not only as a set of commercial techniques but as an epistemic force that reshapes what can be known, who controls knowledge, and how markets evolve.