Customer Segmentation Menggunakan K-Means dan Analisis RFM Pada Data Transaksi Digital
Keywords:
Customer Segmentation, RFM Analysis, K-Means Clustering, Data Transaksi Digital, Elbow MethodAbstract
Pertumbuhan transaksi digital menghasilkan volume data pelanggan yang semakin besar, tetapi data tersebut belum selalu dimanfaatkan secara optimal untuk memahami karakteristik dan nilai pelanggan. Penelitian ini bertujuan melakukan segmentasi pelanggan pada data transaksi digital menggunakan analisis Recency, Frequency, Monetary (RFM) yang dipadukan dengan algoritma K-Means. Tahapan penelitian meliputi praproses data, agregasi transaksi berdasarkan pelanggan, pembentukan atribut RFM, normalisasi data, penentuan jumlah cluster menggunakan metode Elbow, penerapan K-Means, serta interpretasi karakteristik setiap segmen. Hasil analisis menunjukkan bahwa (k=4) merupakan jumlah cluster optimal berdasarkan metode Elbow. Segmentasi menghasilkan empat kelompok pelanggan, yaitu Champions, Loyal Customers, Potential Customers, dan At Risk, dengan karakteristik Recency, Frequency, dan Monetary yang berbeda. Champions memiliki Recency terendah sebesar 9,6 hari, Frequency tertinggi sebesar 18,6 transaksi, dan Monetary tertinggi sebesar Rp2.286.104. Sebaliknya, At Risk memiliki Recency tertinggi sebesar 158,7 hari dengan Frequency 2,5 transaksi dan Monetary Rp221.955. Hasil segmentasi dapat menjadi dasar penyusunan strategi pemasaran yang lebih personal dan sesuai dengan karakteristik setiap kelompok pelanggan.
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