AI-driven customer segmentation in e-commerce: A data-centric approach to personalized retail
Year: 2025
Type: article
Abstract: This article explores the evolution and implementation of AI-driven customer segmentation in e-commerce environments. Beginning with the transition from demographic to behavioral segmentation, it examines the theoretical frameworks underlying modern segmentation algorithms, including clustering techniques and predictive modeling approaches. The discussion addresses critical data requirements and integration challenges, highlighting the importance of data quality dimensions and strategies for unifying customer information across disparate retail platforms. Through implementation case studies, the article identifies common technical and organizational hurdles while extracting best practices from successful deployments. Actionable strategies for retail professionals are presented, focusing on translating segmentation insights into effective marketing campaigns, personalizing customer journeys, implementing real-time segmentation adjustments, and measuring return on investment. The article provides a comprehensive framework for understanding both the potential and practical considerations of applying artificial intelligence to customer segmentation in contemporary retail environments.
Author: Harsha Koundinya Cheruku
Language: English
Subfield: Marketing
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4411464817