AI-based customer segmentation

This paper examines how artificial intelligence is reshaping customer segmentation in marketing, moving the practice from a periodic, demographics-driven exercise to a continuous, behaviour-driven one. Traditional segmentation relies on fixed variables such as age, income and location, decided in advance by a marketing manager. AI-based segmentation instead uses machine learning techniques such as clustering to discover natural groupings directly from behavioural data — browsing patterns, purchase history, app usage and similar signals — allowing segments to update continuously rather than being revisited only once or twice a year. The paper traces the conceptual shift from the classic STP (Segmentation, Targeting, Positioning) framework and RFM analysis towards AI-driven micro-segmentation, and reviews how this is applied across personalised recommendations, dynamic pricing, churn prediction, and cross-selling. Three case studies are discussed: Amazon's behavioural recommendation system, Netflix and Spotify's taste-based clustering, and Myntra's use of AI-driven micro-segmentation during high-volume sale events in the Indian market. The paper also considers the limitations of this approach, including data privacy concerns under India's Digital Personal Data Protection Act, 2023, data quality issues, the risk of over-segmentation, algorithmic bias, and the interpretability challenges that come with relying on machine-generated clusters. It concludes that AI does not eliminate the need for marketing judgement in segmentation; rather, it shifts that judgement from choosing which variables to segment on to deciding which algorithm-generated segments are commercially worth acting on. Keywords: AI-driven marketing, customer segmentation, machine learning, STP framework, personalization, marketing analytics