ANALISIS SEGMENTASI PELANGGAN MENGGUNAKAN KOMBINASI RFM MODEL DAN TEKNIK CLUSTERING
DOI:
https://doi.org/10.21460/jutei.2018.21.76Keywords:
cluster segmentation, data mining, clustering, CRISP-DM, RFM-model, K-MeansAbstract
Intisari – Persaingan yang ketat di bidang bisnis memotivasi sebuah usaha kecil dan menengah (UKM) untuk mengelola pelayanan terhadap konsumen tetap (pelanggan) secara maksimal. Meningkatkan kesetiaan pelanggan dengan mengelompokkan pelanggan menjadi beberapa kelompok dan menentukan strategi pemasaran yang tepat dan efektif untuk setiap kelompok. Segmentasi pelanggan dapat dilakukan melalui pendekatan data mining dengan metode clustering. Tujuan utamanya segmentasi pelanggan dan mengukur kesetiaan mereka terhadap sebuah produk UKM. Dengan menggunakan metode CRISP-DM yang terdiri dari enam fase, yakni pemahaman bisnis (business understanding), pemahaman data (data understanding), persiapan data (data preparation), pemodelan (modelling), evaluasi (evaluation), dan penerapan (deployment). Algoritma K-means digunakan untuk pembentukan klaster dan RapidMiner sebagai tool yang digunakan untuk mengevaluasi klaster-klaster yang terbentuk. Pembentukan klaster didasarkan pada analisa RFM ( Recency, Frequency, dan Monetary). Davies Bouldin Index (DBI) digunakan untuk menemukan jumlah cluster (k) yang optimal. Hasilnya kelompok pelanggan yang terbentuk ada tiga kelompok dengan kelompok pertama berjumlah 30 pelanggan masuk dalam kategori typical customer, kelompok kedua terdapat 8 pelanggan yang masuk dalam kategori superstar dan kelompok ketiga berjumlah 89 pelanggan pada kategori dormant customer.
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