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

【摘要】：當(dāng)代社會(huì)企業(yè)快速發(fā)展，對(duì)客戶(hù)資源的爭(zhēng)奪日趨激烈。如何改善客戶(hù)關(guān)系，獲取客戶(hù)評(píng)價(jià)，合理聚類(lèi)客戶(hù)，提高客戶(hù)滿(mǎn)意度是現(xiàn)代企業(yè)必須去深入思考、研究、投入財(cái)力人力的重要研究課題�；谠u(píng)價(jià)模型的客戶(hù)聚類(lèi)方法研究，是當(dāng)前該領(lǐng)域的研究熱點(diǎn)，并金融財(cái)務(wù)，企業(yè)建設(shè)，客戶(hù)管理等方面得到了廣泛的應(yīng)用。在評(píng)價(jià)模型的建模過(guò)程中，涉及到大量客戶(hù)反饋的處理，這些知識(shí)的來(lái)源都是自然語(yǔ)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(píng)價(jià)模型，研究如何處理這些不確定性知識(shí)的理論與方法就顯得十分必要。在此基礎(chǔ)之上，對(duì)于不同滿(mǎn)意度的客戶(hù)如何聚類(lèi)，按照何種指標(biāo)以何種算法聚類(lèi)也是本文所探討的問(wèn)題。 針對(duì)在傳統(tǒng)聚類(lèi)以及評(píng)定方法失真或無(wú)效的情況下，本文較為深入的探討了如何處理不確定性問(wèn)題，重點(diǎn)如何解決客戶(hù)評(píng)價(jià)建模中的關(guān)鍵問(wèn)題——客戶(hù)等級(jí)的云化問(wèn)題。在客戶(hù)價(jià)值等級(jí)模型建立的基礎(chǔ)上，對(duì)各種客戶(hù)聚類(lèi)方法進(jìn)行了深入的分析，設(shè)計(jì)并實(shí)現(xiàn)了一種使用云模型對(duì)客戶(hù)評(píng)價(jià)指標(biāo)聚類(lèi)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(hù)價(jià)值模型研究。通過(guò)處理海量的統(tǒng)計(jì)數(shù)據(jù)，對(duì)客戶(hù)價(jià)值進(jìn)行區(qū)分，并確定客戶(hù)價(jià)值體系，利用MatlAB以及Sas進(jìn)行分析，最后完成對(duì)客戶(hù)聚類(lèi)的工作。 ②提出了客戶(hù)綜合評(píng)價(jià)的云化方法。通過(guò)正向云化、逆向云化的方法從客戶(hù)各個(gè)指標(biāo)入手建立相應(yīng)的評(píng)價(jià)模型，并進(jìn)行主觀模糊統(tǒng)計(jì)，最后將得到的統(tǒng)計(jì)數(shù)據(jù)通過(guò)云發(fā)生器得到不同客戶(hù)的不同等級(jí)排名結(jié)果。 ③將聚類(lèi)分析的結(jié)果與云化等級(jí)結(jié)果進(jìn)行對(duì)比參照，互相驗(yàn)證，對(duì)企業(yè)客戶(hù)的認(rèn)識(shí)進(jìn)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(duì)于客戶(hù)評(píng)價(jià)建模理論與方法的進(jìn)一步深入研究，，構(gòu)建更加精確、更加客觀的客戶(hù)關(guān)系管理系統(tǒng)，進(jìn)一步建立基于云模型客戶(hù)聚類(lèi)模型，實(shí)現(xiàn)客戶(hù)價(jià)值的充分共享和協(xié)同服務(wù)，具有一定研究借鑒價(jià)值和實(shí)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(xué)位授予單位】：安徽農(nóng)業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類(lèi)號(hào)】：TP311.13;F323.3

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