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All Journal Jurnal Teknik Industri Terintegrasi (JUTIN) Serambi Engineering
Sitorus, Ema Rosary
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Decision Sciences, Operations Research & Management Earth & Planetary Sciences Energy Engineering Environmental Science Industrial & Manufacturing Engineering Mechanical Engineering

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Customer Segmentation Analysis with RFM Model (Recency, Frequency, Monetary) and K-Means Clustering: Case Study of Bottled Water Sales at PT XYZ Sitorus, Ema Rosary; Isna Nugraha
Jurnal Serambi Engineering Vol. 10 No. 2 (2025): April 2025
Publisher : Faculty of Engineering, Universitas Serambi Mekkah

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Abstract

Customer segmentation is a crucial process in understanding consumer behavior patterns to support strategic decision making in marketing. The main challenge faced by companies is to accurately group customers based on transaction data. The purpose of this study is to find out and segment customers using the algorithm K-Means clustering based on RFM model (Recency, Frequency, Monetary) on Bottled Water sales transaction data at PT XYZ. The research method involves analysis of 111 customer data processed using software Orange Data Mining, with validation of results using Silhouette Score which is useful in determining the amount cluster ideal. This research produced four cluster customers, with Cluster 4 reflects customers with the highest level of loyalty, marked by a value Frequency And Monetary the dominant one, while Cluster 3 describes customers with low loyalty potential. The results of this study provide a scientific basis for the development of more focused and efficient data-based marketing strategies.
Analisis segmentasi pelanggan dengan model RFM (Recency, Frequency, Monetary) dan K-Means Clustering (Studi kasus: PT XYZ) Sitorus, Ema Rosary; Nugraha, Isna
Jurnal Teknik Industri Terintegrasi (JUTIN) Vol. 8 No. 1 (2025): January
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jutin.v8i1.39447

Abstract

Customer segmentation is a crucial process in understanding consumer behavior patterns to support strategic decision making in marketing. The main challenge companies face is accurately segmenting customers based on transaction data. The purpose of this research is to determine and segment customers using the K-Means clustering algorithm based on the RFM (Recency, Frequency, Monetary) model on AMDK sales transaction data at PT XYZ. The research method involves analysis of 111 customer data processed using Orange Data Mining software, with validation of the results using Silhouette Score which is useful in determining the ideal number of clusters. This research produces four customer clusters, with Cluster 4 reflecting customers with the highest level of loyalty, characterized by dominant Frequency and Monetary values, while Cluster 3 describes customers with low loyalty potential. The results of this research provide a scientific basis for the development of more focused and efficient data-based marketing strategies.
Co-Authors Isna Nugraha Nugraha, Isna