本文選題：客戶關(guān)系 + 云模型��； 參考：《安徽農(nóng)業(yè)大學》2013年碩士論文

【摘要】：當代社會企業(yè)快速發(fā)展，對客戶資源的爭奪日趨激烈。如何改善客戶關(guān)系，獲取客戶評價，合理聚類客戶，提高客戶滿意度是現(xiàn)代企業(yè)必須去深入思考、研究、投入財力人力的重要研究課題�；谠u價模型的客戶聚類方法研究，是當前該領(lǐng)域的研究熱點，并金融財務(wù)，企業(yè)建設(shè)，客戶管理等方面得到了廣泛的應(yīng)用。在評價模型的建模過程中，涉及到大量客戶反饋的處理，這些知識的來源都是自然語言，往往都存在不確定性，因此，為了能構(gòu)建出更加合理的、客觀的評價模型，研究如何處理這些不確定性知識的理論與方法就顯得十分必要。在此基礎(chǔ)之上，對于不同滿意度的客戶如何聚類，按照何種指標以何種算法聚類也是本文所探討的問題。 針對在傳統(tǒng)聚類以及評定方法失真或無效的情況下，本文較為深入的探討了如何處理不確定性問題，重點如何解決客戶評價建模中的關(guān)鍵問題——客戶等級的云化問題。在客戶價值等級模型建立的基礎(chǔ)上，對各種客戶聚類方法進行了深入的分析，設(shè)計并實現(xiàn)了一種使用云模型對客戶評價指標聚類分析的方法。論文研究的主要內(nèi)容及取得的成果如下： ①客戶價值模型研究。通過處理海量的統(tǒng)計數(shù)據(jù)，對客戶價值進行區(qū)分，并確定客戶價值體系，利用MatlAB以及Sas進行分析，最后完成對客戶聚類的工作。 ②提出了客戶綜合評價的云化方法。通過正向云化、逆向云化的方法從客戶各個指標入手建立相應(yīng)的評價模型，并進行主觀模糊統(tǒng)計，最后將得到的統(tǒng)計數(shù)據(jù)通過云發(fā)生器得到不同客戶的不同等級排名結(jié)果。 ③將聚類分析的結(jié)果與云化等級結(jié)果進行對比參照，互相驗證，對企業(yè)客戶的認識進一步深化，為企業(yè)決策提供依據(jù)和支持。論文研究成果對于客戶評價建模理論與方法的進一步深入研究，，構(gòu)建更加精確、更加客觀的客戶關(guān)系管理系統(tǒng)，進一步建立基于云模型客戶聚類模型，實現(xiàn)客戶價值的充分共享和協(xié)同服務(wù)，具有一定研究借鑒價值和實際應(yīng)用意義。
[Abstract]:With the rapid development of modern social enterprises, the competition for customer resources is becoming increasingly fierce. How to improve customer relationship, obtain customer evaluation, reasonably cluster customers and improve customer satisfaction is an important research topic for modern enterprises to think deeply, study and invest in financial manpower. The research on customer clustering based on Evaluation Model is the current leader The research focus of the domain, and the financial finance, the enterprise construction, the customer management and so on, has been widely applied. In the modeling process of the evaluation model, it involves the treatment of a large number of customer feedback. The sources of these knowledge are natural language and often have uncertainty. Therefore, in order to build a more reasonable and objective evaluation model, It is very necessary to study the theory and method of dealing with these uncertain knowledge. On this basis, how to cluster the customers with different satisfaction and what kind of algorithm to cluster according to the index is also a problem discussed in this paper.
In the case of the distortion or ineffectiveness of traditional clustering and evaluation methods, this paper deeply discusses how to deal with the uncertainty problem and how to solve the key problem in customer evaluation modeling, the cloud problem of customer level. On the basis of the establishment of the customer value hierarchy model, various customer clustering methods are carried out. In depth analysis, a method of clustering analysis of customer evaluation indexes using cloud model is designed and implemented. The main contents and achievements of this paper are as follows:
(1) customer value model research. By processing massive statistical data, the customer value is distinguished, and the customer value system is determined. MatlAB and Sas are used to analyze the customer value. Finally, the customer clustering work is completed.
Secondly, a cloud based method of customer comprehensive evaluation is proposed. Through the forward cloud and reverse cloud method, the corresponding evaluation model is set up from each index of the customer, and the subjective fuzzy statistics are carried out. Finally, the results are obtained through the cloud generator to get the different ranking results of different customers.
Thirdly, the results of cluster analysis are compared with the results of cloud classification, mutual validation, further deepening of the understanding of enterprise customers, and providing the basis and support for enterprise decision-making. The research results of the paper further study the theory and methods of customer evaluation modeling, and build more accurate and more objective customer relationship management system. Further establish customer clustering model based on cloud model to achieve full value sharing and collaborative services, which has certain reference value and practical application significance.
【學位授予單位】：安徽農(nóng)業(yè)大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：TP311.13;F323.3

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