Zhaojing Zhang, R. Wang, Weihong Zheng, Shizhan Lan, D. Liang, Hao Jin
{"title":"基于数据挖掘和指数保留模型假设的客户流失问题利润最大化分析","authors":"Zhaojing Zhang, R. Wang, Weihong Zheng, Shizhan Lan, D. Liang, Hao Jin","doi":"10.1109/ICDMW.2015.84","DOIUrl":null,"url":null,"abstract":"Confronted with fierce competition, an increasing number of telecommunication companies in China realize that they can increase proflts by reducing the rate of customer churn rather than attracting the same number of new customers. Recently, the availability of big data has increased, which has stimulated the development of data mining techniques. Identifying methods by which to maximize proflts is vital for operators based on big data. Novelly, this paper studies three key factors of the customer churn problem, namely, churn rate, prediction performance, and retention capability. We propose a proflt function that maximizes proflts under different conditions and obtain favorable results in applying it to sample data from China Mobile Communications Corporation. Theoretically, about 7.72 million Chinese Yuan per month can be obtained by applying proposed model to China Mobile Group Guangxi Company Limited, making our research of great economic value.","PeriodicalId":192888,"journal":{"name":"2015 IEEE International Conference on Data Mining Workshop (ICDMW)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":"{\"title\":\"Profit Maximization Analysis Based on Data Mining and the Exponential Retention Model Assumption with Respect to Customer Churn Problems\",\"authors\":\"Zhaojing Zhang, R. Wang, Weihong Zheng, Shizhan Lan, D. Liang, Hao Jin\",\"doi\":\"10.1109/ICDMW.2015.84\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Confronted with fierce competition, an increasing number of telecommunication companies in China realize that they can increase proflts by reducing the rate of customer churn rather than attracting the same number of new customers. Recently, the availability of big data has increased, which has stimulated the development of data mining techniques. Identifying methods by which to maximize proflts is vital for operators based on big data. Novelly, this paper studies three key factors of the customer churn problem, namely, churn rate, prediction performance, and retention capability. We propose a proflt function that maximizes proflts under different conditions and obtain favorable results in applying it to sample data from China Mobile Communications Corporation. Theoretically, about 7.72 million Chinese Yuan per month can be obtained by applying proposed model to China Mobile Group Guangxi Company Limited, making our research of great economic value.\",\"PeriodicalId\":192888,\"journal\":{\"name\":\"2015 IEEE International Conference on Data Mining Workshop (ICDMW)\",\"volume\":\"98 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-11-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"13\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE International Conference on Data Mining Workshop (ICDMW)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDMW.2015.84\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE International Conference on Data Mining Workshop (ICDMW)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDMW.2015.84","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Profit Maximization Analysis Based on Data Mining and the Exponential Retention Model Assumption with Respect to Customer Churn Problems
Confronted with fierce competition, an increasing number of telecommunication companies in China realize that they can increase proflts by reducing the rate of customer churn rather than attracting the same number of new customers. Recently, the availability of big data has increased, which has stimulated the development of data mining techniques. Identifying methods by which to maximize proflts is vital for operators based on big data. Novelly, this paper studies three key factors of the customer churn problem, namely, churn rate, prediction performance, and retention capability. We propose a proflt function that maximizes proflts under different conditions and obtain favorable results in applying it to sample data from China Mobile Communications Corporation. Theoretically, about 7.72 million Chinese Yuan per month can be obtained by applying proposed model to China Mobile Group Guangxi Company Limited, making our research of great economic value.