Segmentasi Pelanggan E-Commerce Berdasarkan Pola Pembatalan dan Pengembalian Pesanan Menggunakan K-Means - Yulia Natasya Farah Diba Arifin; Anis Zubair (JUSIFOR : Jurnal Sistem Informasi dan Informatika, 2025)
This study examines e-commerce customer segmentation based on cancellation and return behaviors using K-Means clustering as a proof-of-concept. Using the Pakistan E-Commerce Dataset (2017), we performed preprocessing, behavioral feature engineering (Cancellation Rate, Return Rate, Average Order Value, Discount Sensitivity, Preferred Payment Method, and Total Orders), Min–Max normalization, and K-Means modeling. Cluster number validation relied on the Elbow Method, Silhouette Score, and PCA visualization. Results indicate K = 3 stable clusters: Price-Sensitive Customers (69.48%) high per-order value but price-sensitive; Loyal Customers (13.55%), high frequency and low CR/RR; and High-Risk Customers (16.97%), high return rate with low value contribution. The findings demonstrate K-Means’ effectiveness in identifying cancellation/return patterns and provide a conceptual basis for risk management and further analysis.