【摘要】：企業(yè)之間的競爭歸根到底是對客戶資源的競爭。中國移動要在全業(yè)務競爭中領先，需要將客戶規(guī)模優(yōu)勢轉化為客戶關系優(yōu)勢，需不斷提升客戶滿意度。上海移動在客戶快速增長同時客戶投訴量也在快速增長，如何能對客戶投訴內容進行深入分析，快速聚焦重點投訴，發(fā)現產品、服務短板并快速落實優(yōu)化，提升客戶對移動服務的感知，這對提升移動服務品質，提高客戶滿意度，鞏固移動品牌具有重要且深遠的意義。 投訴管理是服務品質管理中的重要組成部分，，上海移動在投訴管理中積累了大量的文本數據，一方面這些數據蘊含了對用戶訴求的直接描述，另一方面如何快速從這些數據中獲取知識并予以應用成為問題。文本挖掘作為知識挖掘的組成，從非結構化、異構的文本集合中發(fā)現有效的、新穎的、可用的以及能被理解的知識,是解決上述問題的一種方法。但是，如何將文本挖掘技術的理論和工具有效應用到實際的工作中滿足投訴文本處理和分析的需要，成為一項挑戰(zhàn)。 本次論文從投訴系統(tǒng)支撐、流程管理、知識積累三方面研究了上海移動客戶投訴管理現狀，總結了存在的問題。針對這些問題，本次研究主要開展了三方面的工作：一、研究了文本數據挖掘的相關理論及分類方法。提出了上海移動實際應用文本挖掘理論構建文本挖掘模型的原理及過程；二、研究了支持向量機和KNN等文本分類算法的基本原理。在實際應用中，提出了一種改進的基于統(tǒng)計的模糊分類算法，提高了算法查準率；三、將改進的模型算法有效地應用在實際服務品質管理過程中，詳細闡述了模型的應用以及平臺的物理和技術架構。 CCR模型在支撐投訴管理應用后，有效降低了客戶投訴量，提升了客戶滿意度，節(jié)支增效降低人工成本，帶來較好的示范效應。
[Abstract]:The competition between enterprises is the competition of customer resources in the final analysis. China Mobile wants to lead in the whole business competition, needs to transform the customer scale superiority into the customer relations superiority, needs to enhance the customer satisfaction unceasingly. Shanghai Mobile is growing rapidly and the number of customer complaints is also growing rapidly. How can we conduct in-depth analysis of customer complaint content, quickly focus on key complaints, find products and service boards, and quickly implement optimization, It is of great significance to improve the quality of mobile services, improve customer satisfaction and consolidate mobile brands. Complaints management is an important part of service quality management. Shanghai Mobile has accumulated a lot of text data in complaint management. On the one hand, these data contain a direct description of users' demands. On the other hand, how to quickly acquire knowledge from these data and apply them becomes a problem. As a component of knowledge mining, text mining is a method to solve the above problems by finding effective, novel, usable and understandable knowledge from unstructured and heterogeneous text sets. However, how to effectively apply the theory and tools of text mining to the practical work to meet the needs of complaint text processing and analysis has become a challenge. This paper studies the current situation of Shanghai Mobile customer complaint management from three aspects: complaint system support, process management and knowledge accumulation, and summarizes the existing problems. In order to solve these problems, this study mainly carried out three aspects of work: first, the text data mining theory and classification methods. The principle and process of building text mining model by using text mining theory in Shanghai Mobile are put forward. Secondly, the basic principles of text classification algorithms such as support vector machine and KNN are studied. In practical application, an improved fuzzy classification algorithm based on statistics is proposed to improve the precision of the algorithm. Thirdly, the improved model algorithm is effectively applied in the process of practical service quality management. The application of the model and the physical and technical framework of the platform are described in detail. After supporting the application of complaint management, the CCR model effectively reduces the amount of customer complaints, improves customer satisfaction, saves expenses and increases efficiency and reduces labor costs. Bring good demonstration effect.
【學位授予單位】：上海交通大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：F274;F626

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