数据挖掘技术在中国联通公司CRM系统中的实现

发布时间：2018-06-19 08:09

本文选题：数据挖掘 + 客户关系管理　；参考：《电子科技大学》2013年硕士论文

【摘要】：近几年来，国内通信行业的不断发展，通信业务总量逐年上涨。据中华人民共和国工业和信息化部最新数据显示，截至2011年11月底，中国电信业务主营业务收入累计完成9011.5亿元，和2010年同期相比增长9.6%，其中移动通信业务收入累计完成6519.1亿元，和2010年同期相比增长13.6%，占总电信业务收入的72.34%，移动通信业已成为电信业中发展最快、利润最高的一个行业。移动通信业业务量的不断激增，致使各移动运营商所掌握的客户信息量也在飞速增加。随着2009年1月中国完成了多家电信运营的重组，最终形成了中国电信、中国联通、中国移动三家电信运营商鼎立的局面，三家运营商之间的相互竞争也日趋白日化。通信运营商们面对海量的客户数据及客户信息，如何在这些数据中获取对自身有用的信息，来为公司发展、保留客户的决策提供有力的支持，已成为各移动运营商目前关注的首要问题。本文主要对数据挖掘理论和技术在中国联通公司客户关系管理（CRM）中的应用进行研究。文本首先介绍研究的背景、CRM系统的概念CRM与ERP的关系，其次综合阐述了数据挖掘的相关理论知识、数据挖掘的过程、国内外企业数据挖掘在CRM上运用的典型案例及数据仓库的概念，然后通过对通信行业客户特点的分析，介绍了CRM系统在中国联通公司的实施的必要性，并对联通CRM系统结构进行了介绍，接着提出将数据挖掘技术运用于中国联通公司的客户关系管理中去，，利用数据挖掘技术对公司客户信息进行客户群体分类、客户盈利分析、客户获取和客户保存、客户满意度分析等。最后通过中国联通公司对移动客户细分模型、移动通信客户满意度模型和客户离网预测模型的需求分析、模型设计及运行结果分析，说明在EPR上客户数据挖掘系统的成功构建并顺利运行。最后本文得出结论，通过对数据挖掘技术的运营，中国联通公司能从海量的客户信息及客户数据中提取出对公司决策有用的信息，来维持公司良好的客户关系，开发公司潜在客户，提高中国联通公司在通信行业的竞争力。
[Abstract]:In recent years, with the continuous development of domestic communication industry, the total amount of communication services has increased year by year. According to the latest data from the Ministry of Industry and Information Technology of the people's Republic of China, by the end of November 2011, the total revenue of China Telecom's main business had been 901.15 billion yuan. Compared with the same period in 2010, the total revenue of mobile communication business has increased by 651.91 billion yuan, and compared with the same period of 2010, it has increased by 13.6 yuan, accounting for 72.34 percent of the total telecom business income. Mobile communication has become the fastest growing and most profitable industry in the telecommunications industry. With the rapid growth of mobile communication business, the amount of customer information held by mobile operators is increasing rapidly. With the reorganization of many telecom operators in China in January 2009, China Telecom, China Unicom and China Mobile have finally formed the situation of the three telecom operators, and the competition among the three telecom operators has become increasingly white day by day. Facing the huge amount of customer data and customer information, how to obtain useful information from these data to provide strong support for the company's development and customer decision-making. Has become the first issue that each mobile operator pays close attention to at present. In this paper, the application of data mining theory and technology in China Unicom customer relationship Management (CRM) is studied. Firstly, the paper introduces the relationship between CRM and ERP in the background of the research, and then expounds the related theoretical knowledge of data mining and the process of data mining. The typical cases of enterprise data mining and the concept of data warehouse used in CRM at home and abroad. Then the necessity of implementing CRM system in China Unicom is introduced by analyzing the characteristics of customers in communication industry. The CRM system structure of China Unicom is introduced, and then the application of data mining technology to customer relationship management of China Unicom Company is put forward, and the customer group classification and customer profit analysis are carried out by using data mining technology. Customer acquisition and customer preservation, customer satisfaction analysis, etc. Finally, through the China Unicom to the mobile customer subdivision model, the mobile communication customer satisfaction model and the customer off-net forecast model needs analysis, the model design and the operation result analysis. The successful construction and smooth operation of customer data mining system on EPR are illustrated. Finally, this paper draws a conclusion that, through the operation of data mining technology, China Unicom can extract the useful information from the massive customer information and customer data to maintain a good customer relationship. Develop potential customers to enhance China Unicom's competitiveness in the communications industry.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：TP311.13

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