基于数据挖掘的期货公司客户细分管理系统的分析与设计

发布时间：2018-01-09 02:08

本文关键词：基于数据挖掘的期货公司客户细分管理系统的分析与设计　出处：《山东大学》2017年硕士论文　论文类型：学位论文

【摘要】：近年来,我国期货行业快速发展,期货公司不断开展多样化的公司经营模式随着市场规模不断扩大,期货公司在争夺客户的过程中,一方面在于增加现有客户量,另一方面在于维护现有客户的价值,期货公司的战略目标也由"以产品为中心"转变为"以客户为中心"。如何管理客户、如何提高客户满意度、如何将客户资源效用发挥到最大化,并转化为期货公司的利润成为了期货公司的重要目标。在此背景下,越来越多的期货公司相继上线CRM系统,以便用更好的服务现有客户、发掘潜在客户、对客户实现定制化的服务。在应用客户关系管理系统后,期货公司已经开始尽力地区分客户,把最好的服务提供给更有价值的客户,从而发挥出客户服务的最大效率。然而在期货市场气候的变化下,公司往往依照简单的价值分类对客户进行分类,使用一成不变的客户服务策略面临着客户流失的风险。数据挖掘技术将协助期货公司在客户细分问题上寻找更佳的途径,针对不同的客户提供不同的营销策略,将客户价值最大化。本文在前人研究的基础上,结合A期货公司的业务实际,以数据挖掘技术为基础,实现了期货公司客户细分管理,为公司提供个性化服务提供了技术支持。本文从以下几个部分进行阐述:首先,介绍了论文研究的市场背景和框架,进而对系统设计需要的CRM理论、数据挖掘、客户细分理论等进行阐述,然后对A期货公司业务现状进行了业务调查,通过对期货公司主要业务流程分析,明确了各部室的业务需求,第三部分通过业务分析使用平台进行客户细分管理系统的设计,最后进行了客户细分管理系统的实现工作,并且进行了测试验证和系统评价,实现了客户管理的细分,为部室提供了有效的决策支持。本文的研究希望为期货公司做好期货客户维护工作提供部分思路,为公司改善服务质量、决策支持提供帮助。通过对A公司实际应用情况,本文提出两个方面的创新点:(1)在客户基本信息管理上增加了多维分析和个体分析,包含了当前时间点的多项指标的分析和多个时间维度的指标分析,并强化了个体分析,即对某个客户进行详细分析的模块,如分析该客户的投资偏好、操作偏好、服务历史记录,并可以根据持仓情况生成简单的投资分析报告。(2)增加了客户分群分级的功能,根据实际的业务指标对客户进行分群分级,为客户提供更有针对性的服务。
[Abstract]:In recent years, with the rapid development of futures industry in China, futures companies continue to carry out a variety of business models with the expansion of market size, futures companies in the process of competing for customers. On the one hand, it is to increase the number of existing customers, on the other hand, to maintain the value of existing customers. The strategic goal of futures companies has also changed from "product-centered" to "customer-centric". How to manage customers. How to improve customer satisfaction, how to maximize the utility of customer resources, and how to convert the profits of futures companies has become an important target of futures companies. More and more futures companies go online CRM system one after another, in order to better serve existing customers, explore potential customers, achieve customized services to customers. After the application of customer relationship management system. Futures companies have begun to do their best to distinguish customers and provide the best services to more valuable customers, thus maximizing the efficiency of customer service. However, in the futures market climate changes. Companies tend to classify customers according to simple value classifications. Data mining technology will help futures companies to find a better way to solve customer segmentation problems and provide different marketing strategies for different customers. On the basis of previous research, this paper realizes customer segmentation management of futures company based on data mining technology, combined with the actual business of A futures company. It provides technical support for the company to provide personalized service. This paper describes the following parts: first, the paper introduces the market background and framework of the research, and then the system design needs the CRM theory. Data mining, customer segmentation theory and so on are elaborated, and then the business situation of A futures company is investigated. Through the analysis of the main business process of the futures company, the business needs of each department are clarified. The third part uses the platform to design the customer segmentation management system through the business analysis. Finally, the implementation of the customer segmentation management system is carried out, and the test verification and system evaluation are carried out. This paper hopes to provide some ideas for futures companies to do a good job in the maintenance of futures customers and improve the quality of service for the company. Decision support provides help. Through the practical application of company A, this paper puts forward two innovative points: 1) adding multidimensional analysis and individual analysis to customer basic information management. It includes the analysis of multiple indicators of the current time point and the analysis of multiple time dimensions, and strengthens the individual analysis, that is, the module of detailed analysis of a customer, such as the analysis of the customer's investment preference. Operation preference, service history record, and can generate a simple investment analysis report according to the position situation.) it adds the function of customer grouping and classifying customers according to the actual business indicators. To provide customers with more targeted services.
【学位授予单位】：山东大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：F274;F832.39

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