基于数据挖掘的银行客户流失模型分析研究

发布时间：2019-03-15 07:03

【摘要】： 经济全球化以及电子商务的实施,使商业银行面临更加激烈的竞争,特别是由于对客户资源的竞争全球化以及争夺高价值客户竞争的日趋激烈,使得客户扰动加剧、客户流失严重、客户获得成本增加,银行经营风险增加、竞争力受到巨大的冲击。研究发现在银行行业,客户保持是CRM策略成功的关键,只有当客户随着时间得到保持,这一策略才是有利可图的,并且成功的客户保持能够降低银行寻求新的、具有潜在风险客户的需求,并使银行将注意力集中在建立关系和满足现有客户的需求上。所以面对当前的市场状况,商业银行必须在发展新客户的同时,着手进行客户保持的研究。商业银行维系现有客户可以增加资金的汇聚量,同时节省诱导客户进入银行所必需的广告和介绍成本,从而产生更多的现金流和利润。银行客户保持的成功主要依靠对银行客户流失情况的分析和评估,从而提前预知某些客户是否有流失的可能性,进而采取市场策略。而空前巨大的个体水平客户数据量使得银行的数据库变得更加巨大和复杂,数据挖掘技术能够胜任对海量数据的处理,必将在银行行业的客户流失分析中发挥巨大的作用,从海量的普通业务数据中发掘出关于客户流失的关键信息,帮助银行留住最宝贵的资源——客户。本文正是在这样的背景下,运用市场营销、管理决策理论与方法、数据挖掘技术和统计技术,围绕具有广泛的实际背景和发展前景的商业银行客户关系管理核心部分客户流失问题进行了系统的研究。首先在考察和分析其它相关研究使用的变量基础上,得出与客户流失密切相关的因素:在银行服务时间长度,年龄,与银行接触的主要渠道,是否购买银行的某些产品,拥有银行各种业务的数量等13个与银行客户流失密切相关的静态客户资料;同时,引入了时间序列的因素,即考察期前一年银行客户的各种交易行为,最终把这两部分因素都做为客户流失模型的输入变量,最终输入模型的解释变量达到了200多个。使用Weka和SAS Enterprise Miner两种数据挖掘软件分别建立了某商业银行客户流失的决策树预测模型和Logistic回归预测模型,最后对所建立的客户流失预测模型的预测效果进行了比较和分析,识别了商业银行将要流失客户的特征。研究结果对于商业银行设计银行客户保持规划,维系有价值客户,提高商业银行基于事实的决策制定能力,通过与有价值的客户保持长期稳定的关系增加客户对银行利润的贡献,和帮助银行获得真正的竞争优势具有十分重要的理论价值和现实意义。
[Abstract]:Economic globalization and the implementation of e-commerce make commercial banks face more fierce competition, especially due to the globalization of competition for customer resources and the increasingly fierce competition for high-value customers, which aggravates the customer disturbance. The loss of customers is serious, the cost of customer acquisition is increased, the risk of bank operation is increased, and the competitiveness is greatly impacted. The study found that in the banking industry, customer retention is key to the success of the CRM strategy, which is profitable only when the customer is maintained over time, and that successful customer retention lowers the bank's search for new ones. Demand from customers with potential risks and focus on building relationships and meeting the needs of existing customers. Therefore, in the face of the current market conditions, commercial banks must develop new customers, at the same time, proceed with customer maintenance research. Maintaining existing customers by commercial banks can increase the amount of capital gathered while saving the advertising and introduction costs necessary to induce customers to enter the bank, thereby generating more cash flow and profits. The success of bank customers depends mainly on the analysis and evaluation of bank customer turnover, so as to predict the possibility of some customers losing ahead of time, and then adopt the market strategy. And the unprecedented individual level of customer data makes the database of the bank more huge and complex, data mining technology can be competent to deal with massive data, will play a huge role in the analysis of customer churn in the banking industry. Discover key information about customer churn from the vast amount of ordinary business data to help banks retain the most valuable resource-customers. Under this background, this paper applies marketing, management decision theory and method, data mining technology and statistics technology, and makes use of the theory and method of marketing, management decision-making, data mining and statistics. This paper makes a systematic study on customer churn, which is the core part of customer relationship management in commercial banks, which has a wide practical background and development prospects. First of all, on the basis of investigating and analyzing the variables used in other related studies, the factors closely related to customer loss are obtained: the length of bank service, the age, the main channel of contact with the bank, and whether to purchase certain products of the bank. Has the bank various business quantity and so on the 13 static customer information which is closely related to the bank customer loss; At the same time, the factors of time series, that is, the transaction behavior of bank customers in the year before the survey period, are introduced. At last, these two factors are regarded as input variables of customer churn model, and the explanatory variables of the input model reach more than 200. Two kinds of data mining software, Weka and SAS Enterprise Miner, are used to establish the decision tree forecasting model and Logistic regression forecasting model of customer churn in a commercial bank, respectively. Finally, the forecasting effect of the established customer churn prediction model is compared and analyzed. Identify the characteristics that commercial banks are about to lose customers. The research results maintain the planning of commercial bank design bank customers, maintain valuable customers, and improve the ability of commercial banks to make decision-making based on facts. It is of great theoretical value and practical significance to increase customers' contribution to bank profits by maintaining long-term stable relationship with valuable customers and to help banks gain real competitive advantage.
【学位授予单位】：重庆大学
【学位级别】：硕士
【学位授予年份】：2008
【分类号】：F830.4;F224

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