Clustering PT Indosat Tbk B2B Customers Using K-Medoids Algorithm with One Hot Encoding Technique - Naufal Ahmad Muzakky (Universitas Brawijaya, 2026)
Cluster analysis is a method for grouping objects based on characteristic similarities. The K-medoids algorithm, an extension of K-means, offers superior robustness against outliers by utilizing representative objects (medoids). This study analyzes transaction data from Indosat Ooredoo Hutchison’s B2B directorate Malang branch for the second semester of 2024. The objective is to segment B2B customers to assist the directorate in formulating targeted business strategies. Using K-medoids with Gower distance, 81 companies were categorized into five clusters: High-Tech Aggressive Potential (18 companies) with the highest purchasing power and active digitalization; Churn Potential (13 companies) at risk of service termination; High-IT Potential (25 companies) focusing on IT digitalization; Stable Potential (9 companies) showing consistent purchasing behavior; and Low-Tech Moderate Potential (16 companies) with moderate demand for complex technology. These findings enable the B2B directorate to implement data-driven strategies, such as personalized retention programs and tailored technological offerings. This segmentation provides a strategic foundation for optimizing customer relationship management and resource allocation.