I 摘要 随着时代的发展，数据处理技术也飞速发展，数据网络对人们生活有着十分巨大 的影响。涌现出了各式金融科技产品，其多样化、个性化、便捷性、高收益的特点不断 吸引客户，越来越多客户将资金的投入其中，影响银行的存款业务和资金收入，冲击着 银行的传统业务。所以研究客户需求，区分客户类型，采用针对性的营销策略显得十分 重要。银行的客户管理系统中记录有大量的客户数据信息，这些数据的使用成为新时 代背景下银行面临的难题。 本文首先基于相关数据挖掘理论以及客户关系管理理论，提出解决这个难题的解 决建议。其次，通过大数据技术对银行客户的信息进行分析，首先将客户信息进行分 类，随后在上述的分组的基础上构建模型，主要运用K-means聚类分析处理银行客户 信息，减少变量的冗杂性，对变量数据进行优化选择，筛选出其中相关性更高的变量进 行评估，避免建模分析中因变量过多带来的评估模型复杂度高等问题。再次，利用 Logistic回归模型建立起判断客户产品购买意愿的预测回归方程，以提高对已有数据的 利用率。随后，对模型进行检验，得出模型的检验结果，可用于实践的应用。在检验结 果基础上，提出基于数据分析的客户管理建议和针对性的营销建议，实现对客户的有 效跟踪管理，精准营销银行产品，维护客户关系。 关键词：，客户细分；客户关系管理；数据分析； K-means聚类； 广东工业大学硕士学位论文 II Abstract With the development of the times, data networks have a tremendous impact on people's lives. Among them are many various fintech products that emerged at the historic moment. Their diversified, personalized, and even high liquidity and high-yield characteristics continue to attract customer funds, which affects the bank's deposit business and capital income, and impacts banks' Traditional business. At present, it is very important to study customer needs, distinguish customer types and adopt targeted marketing strategies. There are a large number of customer data recorded in the customer management system of the bank, and the use of these data has become a problem faced by the bank in the new era. Firstly, based on the related data mining theory and customer relationship management theory, this paper puts forward some suggestions to solve this problem. Secondly, through the use of data mining methods to classify and filter bank customer information data and modeling analysis, the K-means clustering analysis is mainly used to process bank customer information, reduce the complexity of variables, optimize the selection of variable data, select variables with higher correlation for evaluation, and avoid the evaluation model caused by too many variables in modeling analysis. Thirdly, the logistic regression model is used to establish the prediction regression equation to judge the purchase intention of customers' products, so as to improve the utilization of existing data. Then, the likelihood ratio test and R2 test are carried out to get the test results of the model, which can be used in practice.On the basis of the test results, the paper puts forward customer management suggestions and targeted marketing suggestions based on data analysis to realize effective tracking management of customers, precise marketing of bank products and maintenance of customer relations. . Keywords: Customer Segmentation，CRM, Data Analysis, K-means clustering, 目 录 III 目 录 摘要 ......................................................................................................................................... I Abstract .................................................................................................................................. II 目 录 ......................................................................................................................................III Contents ................................................................................................................................. VI 第一章 绪论 ............................................................................................................................ 1 1.1研究背景 ........................................................................................................................ 1 1.2研究意义 ........................................................................................................................ 2 1.2.1实践意义 ................................................................................................................. 2 1.2.2理论意义 ................................................................................................................. 3 1.3研究目标 ........................................................................................................................ 3 1.4研究方法 ........................................................................................................................ 4 1.5研究内容与论文结构 .................................................................................................... 7 第二章 文献综述与相关研究................................................................................................................... 9 2.1 客户细分理论 ............................................................................................................... 9 2.2 客户关系管理 ............................................................................................................... 9 2.3大数据技术 .................................................................................................................. 10 2.4大数据处理技术 .......................................................................................................... 10 2.4.1数据挖掘常用方法 ............................................................................................... 11 2.4.2数据挖掘处理过程 ............................................................................................... 12 2.4.3数据挖掘在金融行业中的应用 ........................................................................... 13 2.4.4聚类与K-means算法........................................................................................... 14 2.5 客户关系管理与数据挖掘 ......................................................................................... 16 2.6本章小结 ...................................................................................................................... 17 第三章 N银行客户关系管理现状 ....................................................................................................... 19 3.1 银行概况 ..................................................................................................................... 19 3.2 银行客户关系管理现状 ............................................................................................. 19 3.2.1对于细分市场进行组织结构改革 ....................................................................... 19 3.2.2各个领域线上线下服务体系逐渐完善 ............................................................... 20 广东工业大学硕士学位论文 IV 3.3 银行客户关系管理存在的问题 ................................................................................. 21 3.3.1客户关系管理体系外包 ....................................................................................... 21 3.3.2各细分部门价值目标不一 ................................................................................... 21 3.3.3客户细分能力不足 ............................................................................................... 22 3.3.4业务管理较为分散化 ........................................................................................... 22 3.4本章小结 ...................................................................................................................... 22 第四章 基于聚类分析的客户特征描述 ............................................................................................. 23 4.1 数据准备 ..................................................................................................................... 23 4.1.1细分指标选取 ....................................................................................................... 23 4.1.2 数据与处理 .......................................................