基于客户资产离群数据分析的客户分类

Customer Classification Based on Discrete Data Mining of Customer Assets

大数据背景下选择有用信息能帮助企业更准确地分类客户,离群数据同样包含重要的客户信息。为了研究了基于客户资产离群数据的客户分类问题,构建了基于客户资产离群数据分析的客户分类模型,该模型根据交易频率、交易的产品或服务的种类、交易金额以及客户年龄这4个维度的变量,采用先聚类再分类的方法,最终把25类离群客户数据按照客户资产分为4大类,针对不同离群客户分类提出相应的营销策略,并应用某公司的客户数据进行了实证分析。

Selecting useful information can help enterprise to accurately classify customers at the background of big data.Discrete data include important customers'information.This paper builds customer classification model based on discrete data of customer assets.This model judges the contributions of customers to enterprise based on four variables which is trading frequency,types of products or services,sales and the customer's age.This paper classifies first and then uses clustering analysis to the data.The customers will be divided into 25 kinds of subcategories and aggregated into four categories based on customers asserts.This paper puts forward corresponding marketing strategies according to different customer classification.At the end of this paper,this model is used into an empirical analysis.