This paper investigates the effectiveness of artificial intelligence (AI)–based customer segmentation in the Indian retail sector, addressing the limitations of traditional demographic approaches. Using a mixed-method design, we analyzed over 50,000 anonymized transaction records, digital behavioral logs, and psychographic surveys from three leading Indian retailers. AI techniques—including clustering, neural networks, and natural language processing—were applied to identify fine-grained customer micro-segments and evaluate their impact on marketing performance. Results show that AI-driven segmentation significantly outperforms traditional methods, yielding 20–30% higher conversion rates, over 30% sales growth, and a 40% increase in engagement metrics. Interviews with retail managers highlight both opportunities (real-time personalization, improved ROI) and challenges (privacy compliance, infrastructure costs, algorithmic transparency). We conclude that AI-based segmentation can drive measurable business value and hyper-personalization in emerging markets, provided that operational and ethical barriers are addressed.
Gorakh Wakhare (2025) studied this question.