SEGMENTASI PELANGGAN LAYANAN INTERNET PADA
LIFE MEDIA MENGGUNAKAN CLUSTERING K-MEANS
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Institusion
INSTITUT TEKNOLOGI DIRGANTARA ADISUTJIPTO
Author
Arbi Putra Pratama, Mohamad
Subject
Q Science (General)
Datestamp
2024-02-27 03:49:20
Abstract :
Lifemedia is a company that provides internet access services and telecommunications networks for communities, local governments, sub-districts, villages, educational institutions, and commercial institutions. Customer segmentation is needed to group customers based on similar characteristics and to find out consumer behavior so that they can determine the right marketing strategy to drive sales. The RFM model, namely Recency, Frequency, and Monetary variables, can describe the profile or characteristics of each segment. One method that can be used for grouping is K-Means, which functions to classify data into one or more clusters. KMeans is a clustering algorithm with a centroid-based partition method. The graphical results of the Elbow method are used as a reference for selecting the most optimal clusters in the application of the KMeans method. The data used in this study amounted to 1418 records in the timeframe between November 2019 and April 2023. In this research, the most optimal cluster is three clusters. The SSE value obtained for K 3 is 21.48038. With an analysis using the RFM model, the most profitable cluster is the second cluster with 382 members.
Lifemedia is a company that provides internet access services and telecommunications networks for communities, local governments, sub-districts, villages, educational institutions, and commercial institutions. Customer segmentation is needed to group customers based on similar characteristics and to find out consumer behavior so that they can determine the right marketing strategy to drive sales. The RFM model, namely Recency, Frequency, and Monetary variables, can describe the profile or characteristics of each segment. One method that can be used for grouping is K-Means, which functions to classify data into one or more clusters. KMeans is a clustering algorithm with a centroid-based partition method. The graphical results of the Elbow method are used as a reference for selecting the most optimal clusters in the application of the KMeans method. The data used in this study amounted to 1418 records in the timeframe between November 2019 and April 2023. In this research, the most optimal cluster is three clusters. The SSE value obtained for K 3 is 21.48038. With an analysis using the RFM model, the most profitable cluster is the second cluster with 382 members.
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