Customer Profilling for Precision Marketing using RFM Method, K-MEANS algorithm and Decision Tree - Budilaksono, Sularso; Jupriyanto, Jupriyanto; Suwarno, M.Anno; Suwartane, I Gede Agus; Azhari, Lukman; Fauzi, Achmad; Mahpud, Mahpud; Mariana, Novita; Effendi, Maya Syafriana (Politeknik Ganesha Medan, 2021)
Precision marketing is the companys ability to offer products specifically made to customers. This decision can give the company the ability to attract customers to always buy continuously. This study presents a trend model for accurately predicting monthly supply quantities / The method used in the first stage is the RFM (Recency, Frequency, Monetary) method for selecting attributes to group customers into different groups. The output of the first stage is clustered using the K-Means Algorithm. The output of clustering is then classified using the Decision Tree and compared with the K Nearest Neighbor method. The dataset that is processed is sales data from Syifamart As-Syifa Boarding School in Subang with 351,158 rows of data. The clustering process produces 4 optimal clusters. The four clusters are then classified using the Decision Tree algorithm to determine the potential and non-potential characteristics of each customer.