Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal RAGAM: Journal of Statistics and Its Application
Muhammad Hafidz Anshary
Program Studi Statistika Fakultas MIPA Universitas Lambung Mangkurat
Humanities Computer Science & IT Economics, Econometrics & Finance Mathematics Public Health

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
SEGMENTASI PELANGGAN MENGGUNAKAN METODE K-MEANS CLUSTERING BERDASARKAN MODEL RFM (RECENCY, FREQUENCY, MONETARY) Muhammad Hafidz Anshary; Oni Soesanto; Ayatullah Ayatullah
RAGAM: Journal of Statistics & Its Application Vol 1, No 1 (2022): RAGAM: Journal of Statistics and Its Application
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/ragam.v1i1.7382

Abstract

Companies or entrepreneurs must better understanding customers data in all aspects, including detecting similarities and differences among customers, predicting their behavior, and offering better options and opportunities to customers. Customer segmentation is carried out to obtain this information, which is part of CRM (Customer Relationship Management). One of the general models in the application of customer segmentation is the RFM (Recency, Frequency, and Monetary) model. This research method uses a combination of the RFM model and clustering. RFM is used as a description of customer behavior in conducting transactions. Clustering is a process that is widely used and is designed to categorize data. Clustering uses the K-Means Algorithm to determine the number of clusters using the Elbow and Silhouette methods. The application of RFM analysis and the K-Means resulted in two customer segments, namely potential customers and non-potential customers. Potential customers have the characteristics of frequent transactions and also large expenses. Non-potential customers have the characteristics of infrequent transactions and also standard expensesKeywords:  Customer Segmentation, RFM Model, K-Means Clustering
Co-Authors Ayatullah Ayatullah Soesanto, Oni