基于数据挖掘的娄底市移动公司客户流失预警研究

发布时间：2018-04-21 11:10

本文选题：娄底移动 + 客户流失　；参考：《湖南大学》2016年硕士论文

【摘要】：数据挖掘能够吸取隐藏在大数据后面的有用知识,并把这些隐性知识利用起来,从海量数据中提取人们感兴趣的知识。近十几年来,出现了许多数据挖掘的新方法,如神经网络、文本挖掘、支持向量机等,特别是最近几年,数据挖掘基本概念和方法都已成型,并逐渐得到人们的认可。数据挖掘研究正在向更深层次的方向发展。随着电信改革的不断深入,近几年通信行业在我国蓬勃发展,其产业结构链变得越来越复杂,很多环节都影响了客户行为,从而也赋予了客户流失新的内涵,使得客户挽留与客户保有难度加大。于是,国内很多电信运营商开始寻找新的方法,预测电信客户的流失问题。基于数据挖掘技术的电信客户流失预测研究便开始在国内发展起来。本文根据数据挖掘技术及理论,借助娄底市移动公司的业务数据,懫用了决策树的数据挖掘算法,遵循标准数据建模准则,逐步按照商业理解、数据理解、数据准备、模型构建、模型评估的步骤,对移动客户流失问题做了预测研究,并为移动客户的流失管理提供了战略性策略。首先对本文的研究背景、研究现状、主要研究内容、研究方法和创新点进行了描述。其次,对娄底市移动公司客户流失现状及分析,通过查阅资料和实地调查,运用PEST分析法和行业分析法,对公司所处的宏观环境、行业结构、市场竞争态势进行归纳,分析出其面临的机会与威胁。从实际因素的角度出发,对公司离网数据进行分析,分别进行评价。再次,通过数据抽取,进行数据选择分析,建立模型。最后,提出娄底市移动公司客户流失管理策略。从实际出发,尤其是从总公司未来的发展趋势出发,提出保障公司客户维系的具体策略。本文的研究在前人的研究基础上对电信行业客户的流失管理进行更加细化的分析,为数据挖掘技术在电信行业的客户行为分析和客户关系管理的应用提供了有益参考,并且对电信行业发展和维护与客户的良好关系,增强企业的竞争力也有较大的现实意义。
[Abstract]:Data mining can absorb the useful knowledge hidden behind big data and make use of the tacit knowledge to extract the knowledge that people are interested in from the massive data. In recent years, many new methods of data mining have emerged, such as neural network, text mining, support vector machine and so on. Especially in recent years, the basic concepts and methods of data mining have been formed and gradually accepted by people. The research of data mining is developing to a deeper level. With the deepening of telecommunication reform, in recent years, the telecommunications industry has developed vigorously in our country, and its industrial structure chain has become more and more complex. Many links have affected customer behavior, thus giving new meaning to customer churn. Customer retention and customer retention more difficult. As a result, many domestic telecom operators began to look for new ways to predict the loss of telecom customers. The research of telecom customer churn prediction based on data mining technology began to develop in China. According to the technology and theory of data mining, with the help of the business data of Loudi City Mobile Company, this paper uses the data mining algorithm of decision tree, follows the standard data modeling criterion, and gradually according to the commercial understanding, data understanding, data preparation. The model construction and the steps of model evaluation are used to predict the problem of mobile customer churn and provide a strategic strategy for mobile customer churn management. First of all, the research background, research status, main research content, research methods and innovation points are described. Secondly, through consulting information and field investigation, using PEST analysis method and industry analysis method, the author summarizes the macro environment, industry structure and market competition situation of Loudi mobile company. Analyze the opportunities and threats they face. From the point of view of actual factors, the company off-net data are analyzed and evaluated separately. Thirdly, through data extraction, data selection analysis, the establishment of the model. Finally, the Loudi mobile company customer turnover management strategy. Starting from the actual situation, especially from the future development trend of the head office, this paper puts forward the specific strategies to ensure the company's customer maintenance. On the basis of previous studies, this paper makes a more detailed analysis of customer churn management in telecom industry, which provides a useful reference for the application of data mining technology in customer behavior analysis and customer relationship management in telecom industry. It is also of great practical significance to the development and maintenance of telecommunication industry and the maintenance of good relationship with customers and the enhancement of the competitiveness of enterprises.
【学位授予单位】：湖南大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：F626;F274

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