Open AccessCustomer Profilling for Precision Marketing using RFM Method, K-MEANS algorithm and Decision Tree
Author(s) -
Sularso Budilaksono,
Jupriyanto Jupriyanto,
Muhammad Anno Suwarno,
I Gede Agus Suwartane,
Lukman Azhari,
Achmad Fauzi,
Mahpud Mahpud,
Novita Mariana,
Maya Syafriana Effendi
Publication year - 2021
Publication title -
sinkron
Language(s) - English
Resource type - Journals
eISSN - 2541-2019
pISSN - 2541-044X
DOI - 10.33395/sinkron.v6i1.11225
Subject(s) - cluster analysis , decision tree , computer science , data mining , k nearest neighbors algorithm , process (computing) , tree (set theory) , k means clustering , sales forecasting , decision tree learning , algorithm , machine learning , mathematics , econometrics , mathematical analysis , operating system
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.