【摘要】：隨著經(jīng)濟全球化的快速發(fā)展，信息化管理成為了銀行提升競爭力的一項重要手段，而客戶價值作為銀行的重要組成部分，必然成為了競爭的焦點。由于當前的客戶信息軟件開發(fā)激烈競爭和分析需求多樣化的特點，對信息的挖掘帶來了極大的挑戰(zhàn)，尤其是在客戶關系管理中，其客戶價值預測標準高、需求變化頻繁和限制因素多等客觀條件，使客戶信息挖掘的難度越來越高。 一種基于客戶價值指標體系與數(shù)據(jù)挖掘方法為解決日益增多的信息資源挖掘帶來了新的思路，其中模糊數(shù)據(jù)挖掘方法作為最為一種更精準的挖掘方法以其高度的靈活性得到了極大的關注。本文基于模糊數(shù)據(jù)挖據(jù)的實用性對銀行客戶價值進行分析與設計。 首先，研究銀行客戶現(xiàn)有問題和特點，分析影響銀行客戶的因素，分析客戶價值的重要性，客戶價值指標體系相對于傳統(tǒng)方法需求獲取的優(yōu)勢。并引入客戶細分思想，探求客戶價值。 然后，依據(jù)評價指標的依據(jù)與原則，引入專家打分法，構件客戶價值指標設計總體框架。分析體系設計步驟，設計客戶價值指標體系。 最后，分析模糊數(shù)據(jù)挖掘模式原理與目的，設計數(shù)據(jù)挖掘模式總體框架，利用模糊數(shù)據(jù)對客戶價值數(shù)據(jù)的分析，，得到實際劃分的結果與預測。針對需求進行模塊化設計，并結合X銀行客戶信息進行實證研究。
[Abstract]:With the rapid development of economic globalization, information management has become an important means for banks to enhance their competitiveness, and customer value, as an important part of banks, must become the focus of competition. Because of the fierce competition of the current customer information software development and the diversification of the analysis demand, it brings great challenge to the information mining, especially in the customer relationship management, its customer value prediction standard is high. It is more and more difficult to mine customer information because of the objective conditions such as frequent change of demand and many restrictive factors. A new method based on customer value index system and data mining has brought new ideas to solve the increasing number of information resources mining. As the most accurate mining method, fuzzy data mining method has been paid great attention to because of its high flexibility. This paper analyzes and designs the customer value of bank based on the practicability of fuzzy data mining. First of all, we study the existing problems and characteristics of bank customers, analyze the factors that affect bank customers, analyze the importance of customer value, and compare the advantages of customer value index system with traditional methods to obtain requirements. And introduce the idea of customer segmentation, explore customer value. Then, according to the basis and principle of evaluation index, the expert scoring method is introduced to design the overall frame of component customer value index. Analyze system design steps and design customer value index system. Finally, the principle and purpose of fuzzy data mining model are analyzed, the general framework of data mining pattern is designed, and the actual partition result and prediction are obtained by using fuzzy data to analyze customer value data. According to the demand of modular design, combined with X bank customer information for empirical research.
【學位授予單位】：哈爾濱理工大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：TP311.13;F832.3

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