网上银行CRM的客户信息发掘研究
发布时间：2018-10-17 17:07
【摘要】： 网上银行的兴起，为传统银行提供了一个良好的与客户进行交互的平台，加强和完善网上银行的客户关系管理，为客户提供优质服务，并且提高客户服务效率，使银行能在快速变化的市场竞争中把握客户的需求，赢得客户及客户的信任，从整体上降低银行的运营成本，就必须完善网上银行的CRM体系，这对银行的发展是具有重大意义。 网上银行的客户关系管理的原则是以客户为中心，把客户看作是网上银行的一种无形资产，这种无形资产的增值也就意味着银行价值的增加。客户关系管理是集中对有价值客户的认识、保留和发展进行动态的管理，个性化的客户服务成为客户关系管理的核心。由于客户关系管理关注的是长期的价值关系，因此，客户的价值、客户的需求对于网上银行来说就特别重要，，而客户价值的细分就成为客户关系管理的关键。 本文主要阐述了网上银行客户关系管理中的客户需求信息的重要性和意义，依据网上银行的个性化服务的原则和目标，提出了客户需求信息发掘的模型，给出了各种客户需求信息的定义、数据结构及其收集和处理的过程模型，和相应的处理方法。采用相关的算法模拟客户需求的动向。
[Abstract]:The rise of online banking provides a good platform for traditional banks to interact with customers, strengthen and improve the customer relationship management of online banking, provide high quality services for customers, and improve customer service efficiency. So that the bank can grasp the customer's demand in the rapidly changing market competition, win the trust of the customer and customer, and reduce the operation cost of the bank as a whole, it is necessary to perfect the CRM system of the online bank, which is of great significance to the development of the bank. The principle of customer relationship management of online banking is to take the customer as the center and regard the customer as an intangible asset of the online bank. The increment of this intangible asset also means the increase of the bank value. Customer relationship management (CRM) is a dynamic management that focuses on the understanding, retention and development of valuable customers, and personalized customer service becomes the core of CRM. Because customer relationship management is concerned with long-term value relationship, customer value and customer demand are especially important for online banking, and customer value segmentation becomes the key of customer relationship management. This paper mainly expounds the importance and significance of customer demand information in customer relationship management of online banking, and puts forward a model of customer demand information mining according to the principles and objectives of personalized service of online banking. The definition, data structure, the process model of collecting and processing, and the corresponding processing methods are given. Related algorithms are used to simulate the trend of customer demand.
【学位授予单位】：山东科技大学
【学位级别】：硕士
【学位授予年份】：2006
【分类号】：TP311.13
[Abstract]:The rise of online banking provides a good platform for traditional banks to interact with customers, strengthen and improve the customer relationship management of online banking, provide high quality services for customers, and improve customer service efficiency. So that the bank can grasp the customer's demand in the rapidly changing market competition, win the trust of the customer and customer, and reduce the operation cost of the bank as a whole, it is necessary to perfect the CRM system of the online bank, which is of great significance to the development of the bank. The principle of customer relationship management of online banking is to take the customer as the center and regard the customer as an intangible asset of the online bank. The increment of this intangible asset also means the increase of the bank value. Customer relationship management (CRM) is a dynamic management that focuses on the understanding, retention and development of valuable customers, and personalized customer service becomes the core of CRM. Because customer relationship management is concerned with long-term value relationship, customer value and customer demand are especially important for online banking, and customer value segmentation becomes the key of customer relationship management. This paper mainly expounds the importance and significance of customer demand information in customer relationship management of online banking, and puts forward a model of customer demand information mining according to the principles and objectives of personalized service of online banking. The definition, data structure, the process model of collecting and processing, and the corresponding processing methods are given. Related algorithms are used to simulate the trend of customer demand.
【学位授予单位】：山东科技大学
【学位级别】：硕士
【学位授予年份】：2006
【分类号】：TP311.13
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