提高客户价值的可拓策略生成系统研究

发布时间：2018-03-23 20:26

本文选题：可拓学　切入点：策略生成　出处：《广东工业大学》2013年硕士论文

【摘要】：随着社会经济全球化发展,企业竞争由产品竞争转向市场竞争,市场竞争的关键在于争夺客户资源,如今,客户已经成为企业生存发展的重要资源。为了维持企业的长期发展,增加企业的核心竞争力和提高企业的利润,很多企业管理者都越来越重视客户关系的管理和客户的价值的提高。此外,客户关系理论也越来越受到研究者的重视,许多国内外学者对客户关系和客户价值进行了长期的研究,并将研究的成果成功应用到商业领域。本文通过对客户价值理论的了解、利用决策树技术和可拓学方法理论分析设计了提高客户价值的可拓策略生成系统,从理论上和实践上探索了决策树技术在策略生成系统的应用问题,为以后的研究提供了基础和方向。在目前的可拓策略生成系统的方法中,一般都是先建立矛盾问题的可拓模型,然后通过关联函数分析,建立问题库和策略库,在策略生成阶段,采用菱形思维方法生成策略,即先用发散思维对矛盾问题的相关树进行可拓变换,然后采用收敛的思维方法对生成的策略进行评价,推荐优度高的策略供决策者选择。这种方法难以对矛盾问题的核心问题进行分析,生成的策略针对性不强。本文探讨性地利用本体知识和决策树技术共同建立本体知识拓展分析树,通过本体知识拓展分析树挖掘解决矛盾问题的可拓知识和矛盾问题的核心问题,采用对核心问题可拓变换的方法生成策略,这种方法提高了可拓策略生成系统的智能性和策略生成的准确性。本文首先介绍了客户价值理论、决策树、本体知识和可拓策略生成的相关知识；其次,探讨了提高客户价值可拓模型的建立、本体知识拓展分析树模型的建立过程,研究了本体知识拓展分析树在可拓策略生成中的应用问题,即通过分析本体知识拓展分析树的特征,挖掘客户价值的可拓知识和矛盾问题的核心问题；再次,介绍了提高客户价值的可拓策略生成步骤和生成的结果；最后,总结了本文的创新点和未来努力的方向。本文的创新之处： (1)本文结合本体知识、决策树技术和客户价值的理论,提出了本体知识拓展分析树概念,给出了本体知识拓展分析树模型的构建过程,以及通过本体知识拓展分析树建立目标与条件的核问题的过程。 (2)本文探讨了本体知识拓展分析树在可拓策略生成系统的应用研究。通过对本体知识拓展分析树分析,获得相关领域的可拓知识,有效找到矛盾问题的核心问题,并且对相关条件进行可拓变换,生成有效的策略供决策者选择。 (3)本文根据客户价值的可拓知识和核心问题,提出了提高客户价值的可拓策略生成步骤,探索了策略生成的新模式。本文是广东省自然科学基金资助项目(批准号：10151009001000044)—“基于可拓数据挖掘的客户价值研究”的研究成果。
[Abstract]:With the development of social and economic globalization, enterprise competition changes from product competition to market competition. The key of market competition is to compete for customer resources. Nowadays, customer has become an important resource for enterprise survival and development. To increase the core competitiveness of enterprises and improve the profits of enterprises, many enterprise managers pay more and more attention to the management of customer relationship and the improvement of customer value. In addition, the theory of customer relationship is paid more and more attention by researchers. Many scholars at home and abroad have carried out long-term research on customer relationship and customer value, and successfully applied the research results to the field of business. Based on the theory of decision tree technology and extension method, an extension strategy generation system is designed to improve customer value. The application of decision tree technology in policy generation system is explored theoretically and practically. It provides the basis and direction for the future research. In the present extension strategy generation system, the extension model of contradiction problem is established first, and then the problem base and strategy database are established by the correlation function analysis. In the strategy generation stage, the rhombic thinking method is used to generate the strategy. That is, using divergent thinking to transform the related tree of the contradiction problem, and then using the convergent thinking method to evaluate the generated strategy. Recommend strategies with high degree of excellence for decision makers. This method is difficult to analyze the core problems of contradictory problems. In this paper, ontology knowledge and decision tree technology are used to build ontology knowledge extension and analysis tree. The extension tree of ontology knowledge is used to mine the extension knowledge and the core problem of the contradiction problem, and the method of extension transformation of the core problem is adopted to generate the strategy. This method improves the intelligence and accuracy of extension policy generation system. This paper first introduces the theory of customer value, decision tree, ontology knowledge and extension strategy generation related knowledge, secondly, discusses the establishment of extension model for improving customer value, the process of building ontology knowledge extension analysis tree model. This paper studies the application of ontology knowledge extension analysis tree in extension strategy generation, that is, mining the core problem of customer value extension knowledge and contradiction by analyzing the characteristics of ontology knowledge expansion analysis tree. This paper introduces the generating steps and results of the extension strategy to improve customer value, and finally, summarizes the innovation points and the direction of future efforts in this paper. The innovations of this paper are as follows:. 1) based on the theory of ontology knowledge, decision tree technology and customer value, the concept of ontology knowledge extension analysis tree is put forward, and the process of constructing ontology knowledge expansion analysis tree model is given. And the process of establishing the kernel problem of target and condition through ontology knowledge extension and analysis tree. In this paper, the application of ontology knowledge extension analysis tree in extension strategy generation system is discussed. Through the analysis of ontology knowledge extension analysis tree, the extension knowledge in related fields is obtained, and the core problem of contradiction problem is found effectively. The extension transformation of the relevant conditions is carried out to generate effective strategies for decision makers to choose. 3) according to the extension knowledge and core problems of customer value, this paper puts forward the steps of generating extension strategy to improve customer value, and probes into a new model of policy generation. This paper is the research result of the project supported by Guangdong Natural Science Foundation (Grant No.: 10151009001000044- "customer value Research based on Extensible data Mining").
【学位授予单位】：广东工业大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：TP391.1

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