使用机器学习的客户细分的RFM分析:十年研究的调查

Sushilkumar Chavhan, R. Dharmik, Sachin Jain, K. Kamble
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引用次数: 0

摘要

客户细分是一种基于共同特征将企业客户划分为不同群体的方法。在本研究中,我们研究了不同的客户细分方法,并通过使用各种聚类算法执行RFM分析。根据客户的RFM值(Recent、Frequency和Cost),将公司客户成功分类为具有可比较行为的组。客户保留被认为比获得新客户更重要,在两个不同的数据库中进行了分析。结果表明了每种方法的意义。比较有助于选择更好的客户细分。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
RFM analysis for customer segmentation using machine learning: a survey of a decade of research
Customer segmentation is a method of categorizing corporate clients into groups based on shared characteristics. In this study, we looked at the different customer segmentation methods and execute RFM analysis by using various clustering algorithms. Based on RFM values (Recent, Frequency, and Cost) of customers, the successful classification of company customers is divided into groups with comparable behaviors. Customer retention is thought to be more significant than acquiring new clients are analyzed on two different databases. Results show the significance of each method. Comparison is helps for selection of better customer segmentation.
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