摘要
针对客户流失预测不准确、客户流失风险识别不及时等问题,结合企业知识流动热点数据的变化特征,建立石油资源零售客户流失优化模型。根据用户的购买行为特征,对成品油销售企业零售客户进行划分,收集成品油销售企业零售知识流动热点数据,分析不同的客户流失影响因素,设置目标变量、影响变量和输入变量,定义月流失率作为衡量指标,通过客户流失类型确定和预测成品油销售企业零售客户流失量,得出成品油销售企业零售客户流失模型的输出结果。实例验证结果表明,该模型可以获得更精准的客户流失预测结果,为成品油销售企业勘探企业开展客户维护工作提供参考。
Aiming at the problems of inaccurate prediction of customer loss and untimely identification of customer loss risk, combined with the changing characteristics of hot data of knowledge flow, an optimization model of retail customer loss of refined oil enterprises is established. According to the purchase behavior characteristics of users, the retail customers of refined oil enterprises are divided, the hot data of retail knowledge flow of refined oil enterprises are collected, different factors affecting customer loss are analyzed, target variables, influence variables and input variables are set, and the monthly loss rate is defined as a metric indicator. The loss of retail customers of refined oil enterprises is determined and predicted by the type of customer loss, The output results of the model are obtained. The case verification results show that the model can obtain more accurate customer churn prediction results, and provide reference for the maintenance of exploration customers of product oil enterprises.
作者
朱萸
何治呈
ZHU Yu;HE Zhicheng(Sichuan Sales Branch,PetroChina,Chengdu 610000,China)
出处
《资源与产业》
2022年第5期117-123,共7页
Resources & Industries
关键词
知识流动
热点数据
成品油销售
客户流程
knowledge flow
hotspot data
refined oil enterprises
customer flowchart