In this paper, we base our research by dealing with a real-world problem in an enterprise. A RFM (recency, frequency, and monetary) model and K-means clustering algorithm are utilized to conduct customer segmentation and value analysis by using online sales data. Customers are classified into four groups based on their purchase behaviors. On this basis, different CRM (customer relationship management) strategies are brought forward to gain a high level of customer satisfaction. The effectiveness of our method proposed in this paper is supported by improvement results of some key performance indices such as the growth of active customers, total purchase volume, and the total consumption amount.

中文翻译：

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使用RFM模型和K-Means算法按购买行为对客户进行细分的实证研究

在本文中，我们通过处理企业中的实际问题来进行研究。利用在线销售数据，使用RFM（汇率，频率和货币）模型和K均值聚类算法来进行客户细分和价值分析。根据客户的购买行为将其分为四类。在此基础上，提出了不同的CRM（客户关系管理）策略，以提高客户满意度。本文提出的方法的有效性得到一些关键绩效指标（例如活跃客户的增长，总购买量和总消费量）的改善结果的支持。