Customer Intelligence Berbasis RFM Clustering dan Market Basket Analysis untuk Optimalisasi Strategi Bisnis pada Online Retail
Keywords:
Intelijen Pelanggan, RFM, K-Means Clustering, Analisis Keranjang Pasar, FP-Growth, Ritel OnlineAbstract
Persaingan dalam industri retail digital semakin ketat, sehingga pemahaman mendalam terhadap perilaku pelanggan menjadi krusial untuk keberhasilan bisnis. Penelitian ini menerapkan pendekatan Customer Intelligence dengan mengintegrasikan dua teknik data mining, yaitu K-Means Clustering berbasis nilai RFM (Recency, Frequency, Monetary) dan Market Basket Analysis (MBA) menggunakan algoritma FP-Growth, pada dataset Online Retail II dari UCI Machine Learning Repository yang mencakup 1.067.371 transaksi selama periode Desember 2009 hingga Desember 2011. Setelah melalui proses pembersihan data, diperoleh 779.425 baris data bersih (73,0% dari data asli) dan 5.878 pelanggan unik. Nilai RFM dihitung menggunakan tanggal referensi 10 Desember 2011 dengan hasil: rata-rata Recency 201,33 hari, rata-rata Frequency 6,29 transaksi, dan rata-rata Monetary £2.955,90. Metode Silhouette Score menghasilkan nilai tertinggi K=2 sebesar 0,4386. Dua segmen pelanggan yang terbentuk adalah Loyal Customers (2.320 pelanggan, 39,5%; Avg R=50,56 hari, F=12,72x, M=£6.546,97) dan At-Risk/Lost (3.558 pelanggan, 60,5%; Avg R=299,65 hari, F=2,10x, M=£614,34). Analisis FP-Growth dengan minimum support 2% menghasilkan 180 frequent itemsets dan 15 association rules dengan lift lebih dari 1. Aturan dengan lift tertinggi (13,95) ditemukan antara STRAWBERRY CERAMIC TRINKET BOX dan SWEETHEART CERAMIC TRINKET BOX (support 2,25%, confidence 45,62%). Total revenue dataset mencapai £17.374.804. Berdasarkan temuan tersebut dirumuskan rekomendasi strategis berupa program loyalitas, win-back campaign, dan bundling produk untuk meningkatkan retensi dan nilai transaksi pelanggan.
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