摘要
针对基于在线客户评论数据进行客户细分分析的问题,设计了二阶段客户细分分析框架。在客户偏好建模阶段,设计了同义属性识别方法和属性偏好换算方法,基于产品属性树结构,构建粒度统一的客户偏好向量;在客户聚类阶段,设计了包含最优聚类数识别的聚类流程,基于模糊C均值聚类方法,对客户进行聚类。
Traditional customer segmentation methods predominately rely on high-cost market survey,in which data is subjective.As a real expression of customers,online customer reviews（OCR）contain valuable information for customer segmentation analysis.To solve OCR-based customer segmentation problem,this research proposes a two-stage customer segmentation framework.In the user modeling stage,an synonymous attribute recognition method and an attribute utility conversion method are designed,combining with tree structure of product attributes,to build a granularity unified user preference vector;in user clustering stage,Fuzzy C-Means（FCM）-based clustering process including an optimal cluster number identification method is developed for user clustering.Empirical studies justify the effectiveness of the proposed method.
出处
《管理学报》
CSSCI
北大核心
2015年第7期1059-1063,共5页
Chinese Journal of Management
基金
国家自然科学基金资助项目(71371081)
教育部博士点(博导类)基金资助项目(20130142110044)
华中科技大学创新研究院技术创新基金资助项目(CXY13Q033)
关键词
在线客户评论
客户细分
客户偏好建模
模糊C均值算法
online customer review
customer segment analysis
customer preference modeling
fuzzy C-Means
