Penerapan Analisis RFM dan K-Means Clustering untuk Segmentasi Pelanggan E-Commerce: Studi Kasus Dataset Olist Brazil
Keywords:
segmentasi pelanggan, RFM, K-Means Clustering, e-commerce, CRISP-DM, OlistAbstract
Kemampuan memahami perilaku pelanggan secara individual menjadi faktor kritis dalam persaingan platform e-commerce, khususnya di pasar berkembang yang didominasi pembelian satu kali (one-time buyer). Penelitian ini menerapkan analisis RFM (Recency, Frequency, Monetary) yang dikombinasikan dengan algoritma K-Means Clustering untuk mensegmentasi 93.357 pelanggan unik dari dataset Olist Brazilian E-Commerce (2016–2018) dalam kerangka CRISP-DM. Preprocessing data mencakup filter status delivered, penanganan missing value bertingkat, eliminasi duplikat, serta normalisasi menggunakan transformasi log1p dilanjutkan StandardScaler. Jumlah klaster optimal ditentukan melalui kombinasi Elbow Method dan Silhouette Score pada rentang K=2 hingga K=10; K=2 dipilih karena menghasilkan Silhouette Score tertinggi (0,7028) dengan selisih yang signifikan terhadap K=3 (0,4144). Model akhir menghasilkan Silhouette Score 0,7139 dan Davies-Bouldin Index 0,4709, mengindikasikan pemisahan klaster yang kompak dan terdefinisi dengan baik. Dua segmen teridentifikasi: Loyal Customer (3,0%, n=2.801) dengan rata-rata monetary BRL 260,05 dan frequency 2,11 order, serta At Risk (97,0%, n=90.556) dengan rata-rata monetary BRL 137,96 dan frequency 1,00. Dominasi segmen At Risk mencerminkan karakteristik struktural pasar e-commerce Brazil yang didominasi one-time buyer temuan yang konsisten dengan literatur segmentasi di pasar berkembang. Berdasarkan profil tiap segmen, rekomendasi strategi pemasaran yang berbeda dirumuskan untuk memaksimalkan retensi pelanggan bernilai tinggi sekaligus mengaktifkan kembali pelanggan yang tidak aktif.
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