Penerapan RFM Analysis dan K-Means Clustering untuk Segmentasi Pelanggan pada Dataset Online Retail II
Keywords:
Analisis RFM, K-Means Clustering, Segmentasi Pelanggan, Customer Intelligence, Data MiningAbstract
Persaingan di industri e-commerce yang semakin ketat mendorong perusahaan untuk lebih mengenali karakteristik pelanggan demi merancang strategi pemasaran yang lebih efektif. Penelitian ini bertujuan untuk melakukan klasifikasi segmen pelanggan pada dataset Online Retail II dengan mengintegrasikan metode Analisis RFM (Recency, Frequency, Monetary) dan algoritma K-Means Clustering. Dataset ini mencakup 1.067.371 transaksi yang tercatat antara Desember 2009 hingga Desember 2011. Proses pembersihan data menghapus baris yang tidak memiliki ID Pelanggan, nilai Kuantitas dan Harga yang tidak valid, serta data yang terduplikasi, sehingga menghasilkan 779.425 transaksi bersih dari 5.878 pelanggan yang berbeda. Indikator RFM dihitung dan diubah dengan logaritmik untuk mengurangi ketidakseimbangan sebelum dinormalisasi dan dikelompokkan. Penentuan jumlah cluster yang optimal menggunakan Metode Elbow dan Skor Silhouette menunjukkan bahwa k=4 adalah jumlah cluster yang tepat, dengan Skor Silhouette sebesar 0,3650. Hasil pengelompokan mengidentifikasi empat jenis pelanggan: Champion/VIP (20,3%), Loyal Customer (24,8%), Potential/New (21,3%), dan At Risk/Churned (33,6%). Penelitian ini merekomendasikan strategi pemasaran yang spesifik untuk masing-masing segmen guna mendukung program manajemen hubungan pelanggan (CRM) yang lebih efisien.
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