Segmentasi Konsumen Berdasarkan Model Recency, Frequency, Monetary dengan Metode K-Means

Abstract

A good company is a company that is responsive to market changes and opportunities by utilizing existing data and information. Company data and information can come from internal or external sources. One of the internal data sources that can be utilized is customer data. This data will be used as the basis for determining customer segmentation. Segmentation is a process to determine customer characteristics with certain similarities, making it easier to extract information related to profitable customers. Customer business behavior can be seen from recency (last transaction period), frequency (number of transactions), and monetary (rupiah issued) or known as RFM analysis. The effective RFM analysis helps achieve the implementation of customer relationship management because this model is an important facility in measuring the profitability of customer value. To consider this RFM model, researchers use clustering which assumes that customers are in the same cluster, then consider customers with customers in the cluster. This clustering will display customer segmentation. This clustering method uses K-Means clustering. From the results of data processing, 3 clusters were formed from 25 customer data. Based on the clusters formed, it can be concluded that customer purchases have a different pattern. Clusters included in the segment of potential customers are cluster 1. Clusters are needed to get customers who previously had low R, high F, and high M values. While the strategy that needs to be improved is cluster 2.

Analisis Recency Frequency Monetary Dan K-Means Clustering Pada Klinik Gigi Untuk Menentukan Segmentasi Pasien

“…Segmentasi pelanggan membutuhkan variabel deskriptif untuk mengidentifikasi pola perilaku pelanggan [9]. Tujuan segmentasi pelanggan adalah untuk mengetahui karakteristik pelanggan agar perusahaan dapat memilah pelanggan mana yang paling menguntungkan atau kurang menguntungkan bagi perusahaan [10]. Dengan metode RFM (Recency Frequency Monetary) pelanggan dapat dikelompokkan berdasarkan waktu kunjungan terakhir, frekuensi seberapa banyak kunjungan, dan total uang yang dikeluarkan pelanggan [11].…”

Penerapan k-Means Clustering Berdasarkan Analisis RFM Terhadap Segmentasi Pembeli untuk Meningkatkan Strategi CRM

“…Penentuan strategi perusahaan melalui analisis pembeli dan dukungan dalam pengambilan keputusan dengan CRM analitik tepat digunakan bagi perusahaan, sebagai contoh yaitu segmentasi pelanggan [5]. Segmentasi pelanggan yaitu aksi memisahkan pembeli menjadi kelompok bagian yang sama sehingga setiap kelompok dapat ditangani sebagai khalayak pemasaran yang seragam [6]. Hal ini dilakukan agar perusahaan dapat mengetahui pembeli yang tidak menghasilkan keuntungan dan yang memberikan keuntungan.…”

“…Penelitian ini bertujuan untuk menunjang tercapainya penerapan CRM dalam mengukur profitabilitas nilai pembeli, berdasarkan metode k-means clustering dengan analisis RFM. Hasil yang didapatkan adalah tiga cluster dimana cluster 1 merupakan segmen pelanggan potensial yang harus dipertahankan supaya pelanggan tetap mempunyai nilai R yang artinya rendah, F artinya tinggi, dan M artinya tinggi, dan cluster 2 yang perlu dioptimalkan lagi dalam segi strategi [6]. Referensi penelitian berikutnya berada di UD.…”

Hybrid Model Of Structural Equation Modeling Pls And Rfm (Recency, Frequency And Monetary) Model To Improve Bank Average Balance

“…For Recency, the variable to be measured is the last transaction per customer in 2016 [22]. For Frequency, the variable to be measured is the number of Transactions per customer (frequency) during the 2016 period, and for Monetary, the variable measured is the volume of transactions per customer in a period of 4 months in 2016 [23].…”