E-Commerce Customer Segmentation Application Based on the K-Means Algorithm - Nehemia; Jekoniah Nahum Pakage; Veronica Lois; Regina Arieskha (Journal of Artificial Intelligence and Engineering Applications (JAIEA), 2026)
Ineffective e-commerce marketing serves as the background for this research, which aims to develop a customer segmentation application for targeted marketing. The K-Means Clustering method with RFM (Recency, Frequency, Monetary) analysis is applied to data from 178 customers. The research methodology includes data preprocessing, feature transformation, and the determination of the optimal K using the Elbow Method. The results indicate that K=3 is the optimal number of clusters. Three segments were successfully identified: 'Champions' (18.5%, 33 customers) with the highest Frequency/Monetary values, 'Active & Potential' (41%, 73 customers) with the lowest Recency (most recent), and 'At Risk' (40.5%, 72 customers) with the highest Recency (longest duration since last transaction). The study concludes that the developed Streamlit-based application successfully visualizes these segments interactively to support strategic decision-making in marketing.