数据挖掘技术在保险公司客户关系管理中的应用研究

发布时间：2018-10-17 12:00

【摘要】：研究客户关系管理在保险公司中应用，对与保险公司自身竞争力的提升是十分重要的。大量学者用不同的方法、从不同角度研究了客户关系管理在保险业中的应用，但是并没有形成绝对的共识。值得注意的是前人的研究大多是从定性的角度对客户关系管理应用进行分析，而从定量的角度分析还比较少。随着数据挖掘技术的发展，，人们逐渐意识到数据挖掘技术应用到保险公司客户关系管理的重要性。本文将数据挖掘技术中决策树算法和传统的客户关系管理相结合来研究两者在保险公司中的应用。本文第1章主要介绍选题背景和意义，国内外文献综述及论文的结构安排和研究方法。第2章本章是本文的理论基础，本章论述了客户关系管理理论，分析客户关系管理应用到保险公司的必要性，并结合我国实际情况，分析了我国保险公司目前应用客户关系管理系统的现状。第3章为本文的模型构建及方法介绍部分，阐述了数据挖掘技术的相关理论，并对决策树算法进行了重点阐述，综合比较了决策树技术的几种算法。根据第2章及第3章的相关理论与方法，本文第4章进行了实证分析，首先选取了一个保险公司样本的大量数据，然后按照数据挖掘技术的过程，对数据中隐含的信息进行了实证分析，分析结果显示保费是影响保险公司客户流失的最主要因素。过于理想的准确率是由于所选择数据的属性值较少，但从另一方面也说明了保费的重要性。第5章为政策建议部分，根据实证分析结果，提出了一些相对应的政策措施。本文采用的决策树算法能够定量的分析影响企业客户流失的因素，定量分析与定性分析相结合，具有很强的理论及现实意义，本文结论具有一定参考作用。
[Abstract]:It is very important to study the application of CRM in insurance companies. A large number of scholars have studied the application of CRM in the insurance industry from different angles with different methods, but there is no absolute consensus. It is worth noting that most of the previous studies are qualitative analysis of the application of customer relationship management, but from the point of view of quantitative analysis is relatively small. With the development of data mining technology, people gradually realize the importance of applying data mining technology to customer relationship management of insurance companies. In this paper, the decision tree algorithm in data mining technology and the traditional customer relationship management (CRM) are combined to study their application in insurance companies. The first chapter mainly introduces the background and significance of the topic, literature review at home and abroad, the structure of the paper and research methods. Chapter 2 is the theoretical basis of this paper. This chapter discusses the theory of customer relationship management, analyzes the necessity of the application of customer relationship management to insurance companies, and combines the actual situation of our country. This paper analyzes the current situation of the application of customer relationship management system in Chinese insurance companies. Chapter 3 is the part of model construction and method introduction in this paper. The related theory of data mining technology is expounded, and the algorithm of decision tree is expounded emphatically, and several algorithms of decision tree technology are compared synthetically. According to the relevant theories and methods in Chapter 2 and Chapter 3, the fourth chapter of this paper carries on the empirical analysis, first selects a large number of data of the insurance company sample, then according to the data mining technology process, The results show that the premium is the most important factor affecting the customer turnover of insurance companies. The over-ideal accuracy is due to the fact that the selected data has fewer attribute values, but on the other hand, it also shows the importance of the premium. The fifth chapter is the policy suggestion part, according to the empirical analysis result, has proposed some corresponding policy measures. The decision tree algorithm used in this paper can quantitatively analyze the factors that affect the customer turnover of enterprises. The combination of quantitative analysis and qualitative analysis has a strong theoretical and practical significance. The conclusion of this paper has a certain reference role.
【学位授予单位】：湖南大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：TP311.13

【参考文献】