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Customer segmentation by web content mining
Journal of Retailing and Consumer Services ( IF 10.4 ) Pub Date : 2021-04-24 , DOI: 10.1016/j.jretconser.2021.102588
Jinfeng Zhou , Jinliang Wei , Bugao Xu

This article introduces a new dimension, Interpurchase Time (T), into the existing RFM (Recency, Frequency, and Monetary) model to form an expanded RFMT model for parsing consumers' online purchase sequences in a long period to implement customer segmentation. The proposed RFMT model can track and discern changes in customer purchasing behaviors during their whole shopping cycle. Firstly, a web content retrieving system was developed to fetch publicly available customer data on a retailer's website, including demographic information (gender, age, location, etc.) and product information (name, price, date, etc.) of each purchase in a period from 2008 to 2019. The RFMT values of a customer were then computed from the retrieved data and subsequently analyzed by the hierarchical clustering to derive seven homogeneous clusters with specific customer profiles. Subsequently, demographic features and product preferences were identified for each cluster with business insights that can help the retailer to improve customer relationships and to implement targeted recommendation strategies.



中文翻译:

通过Web内容挖掘进行客户细分

本文在现有的RFM(新近度,频率和货币)模型中引入了一个新维度,即购买时间(T),以形成扩展的RFMT模型,用于长时间解析消费者的在线购买顺序以实现客户细分。提出的RFMT模型可以跟踪和识别客户在整个购物周期中的购买行为变化。首先,开发了一个Web内容检索系统,以获取零售商网站上公开可用的客户数据,包括每次购物中的人口统计信息(性别,年龄,位置等)和产品信息(名称,价格,日期等)。从2008年到2019年。RFMT然后,从检索到的数据中计算出客户的价值,然后通过层次聚类进行分析,以得出具有特定客户资料的七个同类聚类。随后,利用业务洞察力为每个集群确定了人口统计特征和产品偏好,这些洞察力可以帮助零售商改善客户关系并实施有针对性的推荐策略。

更新日期:2021-04-26
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