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Customer clustering using RFM analysis

机译：使用RFM分析的客户聚类

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摘要

In this study, customers' behaviors are determined by detecting natural clusterings using existing reservation and customer data. We also customize their services and sales strategies according to these behaviors. The basic characteristics that provide these existing heuristics have been extracted by the decision tree approach after the K-means is implemented. It is determined that these characteristics are customer's product acquisition channel, specific product preferences, reservation periods, seasonal preference, etc. The fact that these characteristics show significant changes in each clusters indicates that the solution is generally successful and that these characteristics are successfully selected. This work plays an important role in creating campaigns and product packages appropriate for these groups' characteristics.
机译：在本研究中，客户的行为是通过使用现有预留和客户数据检测自然集群的。我们还根据这些行为定制其服务和销售策略。在实现K-Manile之后，决策树方法提取了提供这些现有启发式的基本特征。确定这些特征是客户的产品采集渠道，特定产品偏好，预约期，季节性偏好等。这些特征在每个簇中显示出显着变化的事实表明，该解决方案通常是成功的，并且这些特性成功选择了这些特性。这项工作在创建适合这些群体特征的广告系列和产品包中起着重要作用。

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