Paper Contents
Abstract
Customer segmentation is vital for modern retail strategies, enabling businesses to tailor their marketing, optimize inventory management, and enhance the overall customer experience. This paper reviews clustering-based segmentation techniques and their applications in retail by analysing four key studies focused on algorithms like K-Means, DBSCAN, and hierarchical clustering, along with the integration of models such as Recency, Frequency, and Monetary (RFM) analysis. It discusses these approachesmethodologies, advantages, and practical implications, highlighting their potential to uncover valuable customer insights. The paper also addresses challenges associated with clustering-based segmentation and offers suggestions for future research, aiming to provide a comprehensive understanding of how these techniques can be effectively used to promote data-driven decision-making and improve retail operations.
Copyright
Copyright © 2025 Aditi Shah. This is an open access article distributed under the Creative Commons Attribution License.
