Customer segmentation using bisecting k-means algorithm based on recency, frequency, and monetary (RFM) model
Open Access
- 18 November 2019
- journal article
- Published by Institute of Research and Community Services Diponegoro University (LPPM UNDIP) in Jurnal Teknologi dan Sistem Komputer
- Vol. 8 (2), 78-83
- https://doi.org/10.14710/jtsiskom.8.2.2020.78-83
Abstract
Information on customer loyalty characteristics in a company is needed to improve service to customers. A customer segmentation model based on transaction data can provide this information. This study used parameters from the recency, frequency, and monetary (RFM) model in determining customer segmentation and bisecting k-means algorithm to determine the number of clusters. The dataset used 588 sales transactions for PT Dinar Energi Utama in 2017. The clusters formed by the bisecting k-means and k-means algorithm were tested using the silhouette coefficient method. The bisecting k-means algorithm can form the best customer segmentation into three groups, namely Occasional, Typical, and Gold, with a silhouette coefficient of 0.58132.Keywords
Funding Information
- Universitas Mulawarman, Indonesia
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