Customer Segmentation Using Data Mining Techniques to Enhance Retention Strategies in E-Commerce Businesses

Ayushi Sharma

Customer Segmentation Using Data Mining Techniques to Enhance Retention Strategies in E-Commerce Businesses

Author(s)	Ms. Ayushi Sharma
Country	India
Abstract	The rapid expansion of e-commerce has intensified competition among online retailers, making customer retention a strategic priority for sustainable growth. While acquiring new customers remains important, the rising cost of acquisition and increasing market saturation have compelled firms to focus on data-driven retention strategies. E-commerce platforms generate extensive transactional and behavioral data, offering significant opportunities to apply advanced analytics for understanding customer patterns. This study examines how customer segmentation using data mining techniques can enhance retention strategies in e-commerce businesses. The research explores analytical approaches such as RFM (Recency, Frequency, Monetary) analysis, clustering techniques including K-Means, and predictive modeling methods such as logistic regression and random forest for churn prediction. By integrating descriptive segmentation with predictive analytics, the study develops a structured framework that links customer groups with targeted retention interventions. The paper highlights how behavioral segmentation enables firms to identify high-value customers, detect churn risk, and allocate marketing resources more efficiently. Through conceptual analysis and applied case illustrations, the study demonstrates that analytics-driven segmentation improves personalization, increases customer lifetime value, and reduces attrition rates. The findings emphasize the strategic importance of combining machine learning techniques with managerial decision-making processes. The research contributes to business analytics literature by bridging technical data mining models with practical retention strategies, offering a scalable framework for competitive advantage in digital retail markets.
Keywords	Customer Segmentation, Data Mining, Customer Retention, E-Commerce Analytics, Churn Prediction, Business Intelligence
Field	Business Administration
Published In	Volume 8, Issue 1, January-February 2026
Published On	2026-02-27

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Customer Segmentation Using Data Mining Techniques to Enhance Retention Strategies in E-Commerce Businesses

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