Segmentasi Pelanggan dengan Algoritma Clustering Berdasarkan Atribut Recency, Frequency dan Monetary (RFM)
Customer Segmentation with Clustering Algorithm Based on Recency, Frequency, and Monetary (RFM) Attributes
Keywords:
Agglomerative, DBSCAN, K-Means, RFM, Segmentasi PelangganAbstract
Perdagagan bebas yang disepakati dengan negara maju menimbulkan perubahan karakter konsumen di Indonesia [1] ditambah efek pandemi Covid-19 semua komoditi dalam negeri hampir mengalami penuruan penjualan dan laba usaha tiap tahunnya[2]. Perusahaan harus mengubah strategi lain dalam menarik pelanggan menggunakan data karakteristik pelanggan yang berbeda-beda salah satunya data transaksi penjualan [3]. Data tersebut nantinya akan disegmentasi dengan metode clustering. Clustering adalah teknik analisis data yang bertujuan mengelompokkan objek-objek ke dalam grup atau klaster berdasarkan kesamaan karakteristik atau fiturnya. Analisis data dengan clustering diperlukan untuk mengidentifikasi pola dan mengekstrak informasi dari kumpulan data dengan variasi dan jumlah besar, seperti pada identifkasi segmentasi pelanggan. Hasil clustering ini akan memudahkan dalam merumuskan startegi pemasaran berorientasi pelanggan. Penelitian ini menggunakan model Recency, Frequency, and Monetary (RFM) sebagai atribut utama dan teknik clustering k-means, agglomerative, dan DBSCAN. Evaluasi berdasarkan silhouette score, Davis-Bouldin index, dan Calinski-Harabasz index menunjukkan hasil terbaik pada pembentukan tiga cluster dengan algoritma k-means untuk segmentasi pelanggan dalam penelitian ini dengan indeks silhouette score sebesar 0.364, davis-bouldin sebesar 0.93 dan calinski-harabasz sebesar 1303.6 ini menghasilkan 3 klaster pelanggan loyal customer, adequate customer dan churn atau lost customer.
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