Customer Segmentation in E-commerce Using Big Data Clustering Techniques

Abstract

Customer segmentation is a pivotal strategy in e-commerce, enabling personalized marketing efforts and tailored product recommendations. Leveraging big data clustering techniques, this study aims to segment customers effectively based on their purchasing behaviors, preferences, and demographic attributes. By analyzing large volumes of transactional data, we identify distinct customer segments, each with unique characteristics and buying patterns. We employ advanced clustering algorithms such as k-means, hierarchical clustering, and DBSCAN to partition customers into homogeneous groups. Through this segmentation, e-commerce businesses can gain profound insights into their customer base, optimize marketing strategies, enhance customer satisfaction, and ultimately drive sales and revenue growth. This paper presents a comprehensive overview of customer segmentation in e-commerce, emphasizing the significance of big data analytics and clustering techniques in understanding consumer behavior and fostering sustainable business growth.