SEGMENTASI PELANGGAN PADA JASA PERAWATAN DAN PERBAIKAN PESAWAT TERBANG DENGAN ANALISIS RFM MENGGUNAKAN METODE K-MEANS CLUSTERING DAN K-MEDOIDS CLUSTERING DI PT.X
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Institusion
Universitas Pembangunan Nasional Veteran Jakarta
Author
Fabiola Agata, .
Subject
T Technology (General)
Datestamp
2021-09-22 05:44:16
Abstract :
This research was conducted to identify customer segmentation in PT.X. The purpose of this research is to acquire customer segments in companies with RFM (Recency, Frequency, Monetary) analysis and obtain the best methods on customer clusterization to recommend alternative strategies for each segment formed. In this study, the methods used were the K-Means Clustering method. Based on the results obtained, the K-Means Clustering Method obtained the most optimal cluster results, namely on the number of clusters k =2 with a DBI value of 0.351. Where the results of the method there are 2 segments, namely segment 1 belongs to the dormant customer class that has the characteristics of customers with low frequency, low monetary, and low recency. While segment 2 belongs to the superstar customer class that has the characteristics of customers with high loyalty level, has a high monetary value, has a high frequency, and has a high transaction value. Furthermore, it can recommend alternative strategies for each segment formed so that customers can return to perform aircraft maintenance and the company can maintain loyal customers.
This research was conducted to identify customer segmentation in PT.X. The purpose of this research is to acquire customer segments in companies with RFM (Recency, Frequency, Monetary) analysis and obtain the best methods on customer clusterization to recommend alternative strategies for each segment formed. In this study, the methods used were the K-Means Clustering method. Based on the results obtained, the K-Means Clustering Method obtained the most optimal cluster results, namely on the number of clusters k =2 with a DBI value of 0.351. Where the results of the method there are 2 segments, namely segment 1 belongs to the dormant customer class that has the characteristics of customers with low frequency, low monetary, and low recency. While segment 2 belongs to the superstar customer class that has the characteristics of customers with high loyalty level, has a high monetary value, has a high frequency, and has a high transaction value. Furthermore, it can recommend alternative strategies for each segment formed so that customers can return to perform aircraft maintenance and the company can maintain loyal customers.
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