CUSTOMER SEGMENTATION FOR ONLINE E-COMMERCE USING CLUSTERING ALGORITHMS

The rapid expansion of online e-commerce platforms has generated massive volumes of customer transactional and behavioral data, creating new opportunities for data-driven business decision-making. However, many organizations struggle to effectively utilize this data for understanding customer purchasing patterns and developing personalized marketing strategies. Traditional segmentation techniques based on demographic attributes or manual rules often fail to capture the complexity of modern consumer behavior, resulting in ineffective targeting and reduced customer engagement. This study proposes a machine learning-based approach for customer segmentation in online e-commerce environments using clustering algorithms. The system utilizes unsupervised learning techniques, specifically KMeans clustering and hierarchical clustering, to identify meaningful customer groups without requiring predefined labels. The dataset undergoes several preprocessing steps including missing value handling, duplicate removal, normalization, and feature engineering through Recency, Frequency, and Monetary (RFM) analysis to enhance clustering effectiveness. The optimal number of clusters is determined using the Elbow Method and Silhouette Score to ensure accurate segmentation. Additionally, Principal Component Analysis (PCA) is applied to reduce dimensionality and visualize clusters for better interpretability. The proposed system enables businesses to categorize customers into distinct behavioral groups such as high-value customers, frequent buyers, and low-engagement users. These insights help organizations design targeted marketing campaigns, improve customer retention strategies, and enhance overall business performance. Experimental results demonstrate that clustering-based segmentation provides valuable insights into customer purchasing patterns and supports data-driven decision-making in ecommerce platforms. The proposed approach highlights the effectiveness of machine learning techniques in transforming raw transactional data into actionable customer intelligence