Clustering Customer’s Internet Subscriptions in Apartment Using K-Means Clustering Algorithm - Chow, Sayuti; Hutabarat, Berman; sirya, Damara; Heikal, Jerry (AKADEMIK: Jurnal Mahasiswa Ekonomi & Bisnis, 2025)
In Indonesia’s increasingly digital urban environment, internet service providers (ISPs) face challenges in delivering packages that suit the diverse needs of apartment residents. A one-size-fits-all strategy often leads to service mismatches and customer dissatisfaction. This study aims to address this gap by segmenting apartment-based internet subscribers in Jakarta, Surabaya, and Bandung using the K-Means clustering algorithm. The research utilized secondary data from 12,966 subscribers, incorporating key variables such as service package type, housing classification, payment method, and average revenue per user (ARPU). Through data preprocessing and cluster analysis using IBM SPSS, five distinct customer segments were identified, each representing specific behavioral traits and service preferences. These segments range from budget-conscious families to high-end users with complex service demands. Guided by the STP (Segmentation, Targeting, Positioning) framework and the 7Ps of marketing, two primary target segments Family Value Mid-Tier and Stream Pro Users were highlighted for their high market potential and alignment with strategic pricing. The findings contribute practical insights for ISPs in developing personalized broadband services and more efficient marketing strategies tailored to urban apartment markets. This research also demonstrates the value of data-driven segmentation in improving customer satisfaction and business competitiveness in the digital service sector.