Customer segmentation using bisecting k-means algorithm based on recency, frequency, and monetary (RFM) model

Novianti Puspitasari, J. A. Widians, N. B. Setiawan
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引用次数: 9

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.
基于最近、频率和货币(RFM)模型的分割k-均值算法的客户细分
公司需要了解客户忠诚度特征,以改善对客户的服务。基于交易数据的客户细分模型可以提供这些信息。本研究使用来自最近,频率和货币(RFM)模型的参数来确定客户细分和分割k-means算法来确定集群的数量。该数据集使用了2017年PT Dinar Energi Utama的588笔销售交易。采用剪影系数法对分割k-means和k-means算法形成的聚类进行检验。等分k-means算法可以将最佳客户细分为三组,即偶尔、典型和黄金,剪影系数为0.58132。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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6 weeks
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