基于K-Means聚类算法的客户体验管理优化策略研究
Research on Optimization strategy of Customer Experience Management based on K-Means clustering algorithm
张蕊 1张丽红1
作者信息
- 1. 中国移动通信集团云南有限公司,云南昆明 650228
- 折叠
摘要
近年来,中国市场进入存量博弈时代,人口红利向人心红利转变,共同推动产业的迭代升级的迫切性日益凸显,对千人千面服务的要求也越来越高.为了解决这一问题,提出了结合K-Means聚类算法实现客户分群来优化客户体验管理.其中,K-Means聚类算法可以寻找出K个不同组别的簇,并将该组别所包含数值的均值作为各组别的核心.聚类结果可为后续各类客户提供的精细化服务和优化客户体验管理提供重要依据,实验表明,使用K-Means聚类的客户分群比使用其他聚类算法精准度更高,花费时间更短.
Abstract
In recent years,the Chinese market has entered the era of stock game,and the demo-graphic dividend has turned into the popular dividend.The urgency of jointly promoting the it-erative upgrading of industries has become increasingly prominent,and the requirement for the service of thousands of people has become increasingly high.In order to solve this problem,a combination of K-Means clustering algorithm is proposed to achieve customer clustering to opti-mize customer experience management.Among them,k-Means clustering method can find out K clusters of different groups,and take the mean value contained in this group as the core of each group,also known as the K-Mean value.The clustering results can provide important basis for providing refined services and optimizing customer experience management for various types of customers in the future.Experiments have shown that customer clustering using K-Means clus-tering has higher accuracy and shorter time consumption than using other clustering algorithms
关键词
客户体验管理/聚类/K-MeansKey words
Customer experience management/clustering/K-Means引用本文复制引用
出版年
2024
NETL
NSTL
万方数据