Strategi Customer Intelligence Melalui Klasterisasi K-Means untuk Optimalisasi Retensi Pelanggan pada Industri Telekomunikasi
DOI:
https://doi.org/10.64803/jodsie.v1i2.38Keywords:
Customer Intelligence, K-Means, Business Strategy, Customer Retention, Telco ChurnAbstract
Di tengah persaingan ketat industri telekomunikasi, tingginya churn rate pelanggan mengancam stabilitas pendapatan perusahaan. Penelitian ini menerapkan klasterisasi K-Means pada dataset Telco Customer Churn untuk mengidentifikasi segmen pelanggan guna optimalisasi retensi melalui customer intelligence, mengikuti kerangka CRISP-DM. Data diproses dengan imputasi median pada Total Charges, normalisasi StandardScaler, dan penentuan klaster optimal (k=4) via Elbow Method. Hasil mengungkap empat profil: (1) The Newbies (tenure rendah, biaya rendah); (2) High-Value Loyalists (tenure tinggi, biaya tinggi); (3) At-Risk Big Spenders (tenure rendah, biaya sangat tinggi); dan (4) Budget Veterans (tenure tinggi, biaya rendah), divalidasi Silhouette Score solid. Temuan memungkinkan strategi personalisasi: promo onboarding untuk Newbies, VIP rewards untuk Loyalists, kontrak jangka panjang untuk At-Risk, dan upselling bundling untuk Veterans. Pendekatan ini meningkatkan efisiensi alokasi sumber daya pemasaran berbasis data.
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Copyright (c) 2026 Muhammad Azril Surya Ramadhan, Winda Chariska, Mufidah Karimah (Author)
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