Segmentasi Pelanggan Anhesa Gym Berbasis RFM, CLV, dan Algoritma K-Means untuk Strategi Segmentation, Targeting, and Positioning: Anhesa Gym Customer Segmentation Based on RFM, CLV, and K-Means for Segmentation, Targeting, and Positioning Strategies

Ricky Mangihut Rajagukguk; Budi Prasetiyo

doi:10.57152/malcom.v6i3.2795

Authors

Ricky Mangihut Rajagukguk Universitas Negeri Semarang
Budi Prasetiyo Universitas Negeri Semarang

DOI:

https://doi.org/10.57152/malcom.v6i3.2795

Keywords:

Algoritma K-Means, CLV, RFM, Segmentasi Pelanggan, Strategi STP

Abstract

Anhesa Gym menghadapi tantangan dalam mempertahankan loyalitas pelanggan dan mengoptimalkan strategi pemasaran karena kurangnya pemahaman mendalam terhadap karakteristik tiap segmen pelanggan. Penelitian ini bertujuan untuk merancang strategi pemasaran Segmentation, Targeting, dan Positioning (STP) yang tepat sasaran. Berbeda dari penelitian terdahulu yang umumnya mengandalkan perhitungan tradisional, penelitian ini memberikan kontribusi melalui integrasi model Recency, Frequency, Monetary (RFM) dan algoritma K-Means Clustering yang divalidasi dengan pembobotan Analytic Hierarchy Process (AHP) dalam menghitung Customer Lifetime Value (CLV). Kombinasi ini diterapkan untuk meminimalisir bias asumsi manajerial dan memberikan bobot yang objektif pada tiap variabel perilaku. Hasil analisis menunjukkan terbentuknya tiga klaster optimal dengan nilai Davies-Bouldin Index (DBI) 0,436. Melalui AHP, variabel Monetary diidentifikasi paling berpengaruh (0,650). Klaster 2 ditetapkan sebagai segmen Loyal (skor CLV: 1,000), diikuti Klaster 0 sebagai Potential (0,327), dan Klaster 1 sebagai Lost/Churn (0,217). Berdasarkan hasil tersebut, dirumuskan strategi STP berupa penawaran layanan premium eksklusif bagi segmen Loyal serta program reaktivasi harga bagi segmen Lost/Churn. Penelitian ini secara praktis memfasilitasi pihak manajemen dalam mengambil keputusan pemasaran berbasis data.

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