發(fā)布時(shí)間：2018-08-22 15:15

【摘要】：企業(yè)之間的競(jìng)爭(zhēng)歸根到底是對(duì)客戶資源的競(jìng)爭(zhēng)。中國(guó)移動(dòng)要在全業(yè)務(wù)競(jìng)爭(zhēng)中領(lǐng)先，需要將客戶規(guī)模優(yōu)勢(shì)轉(zhuǎn)化為客戶關(guān)系優(yōu)勢(shì)，需不斷提升客戶滿意度。上海移動(dòng)在客戶快速增長(zhǎng)同時(shí)客戶投訴量也在快速增長(zhǎng)，如何能對(duì)客戶投訴內(nèi)容進(jìn)行深入分析，快速聚焦重點(diǎn)投訴，發(fā)現(xiàn)產(chǎn)品、服務(wù)短板并快速落實(shí)優(yōu)化，提升客戶對(duì)移動(dòng)服務(wù)的感知，這對(duì)提升移動(dòng)服務(wù)品質(zhì)，提高客戶滿意度，鞏固移動(dòng)品牌具有重要且深遠(yuǎn)的意義。 投訴管理是服務(wù)品質(zhì)管理中的重要組成部分，，上海移動(dòng)在投訴管理中積累了大量的文本數(shù)據(jù)，一方面這些數(shù)據(jù)蘊(yùn)含了對(duì)用戶訴求的直接描述，另一方面如何快速?gòu)倪@些數(shù)據(jù)中獲取知識(shí)并予以應(yīng)用成為問題。文本挖掘作為知識(shí)挖掘的組成，從非結(jié)構(gòu)化、異構(gòu)的文本集合中發(fā)現(xiàn)有效的、新穎的、可用的以及能被理解的知識(shí),是解決上述問題的一種方法。但是，如何將文本挖掘技術(shù)的理論和工具有效應(yīng)用到實(shí)際的工作中滿足投訴文本處理和分析的需要，成為一項(xiàng)挑戰(zhàn)。 本次論文從投訴系統(tǒng)支撐、流程管理、知識(shí)積累三方面研究了上海移動(dòng)客戶投訴管理現(xiàn)狀，總結(jié)了存在的問題。針對(duì)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(shù)據(jù)挖掘的相關(guān)理論及分類方法。提出了上海移動(dòng)實(shí)際應(yīng)用文本挖掘理論構(gòu)建文本挖掘模型的原理及過程；二、研究了支持向量機(jī)和KNN等文本分類算法的基本原理。在實(shí)際應(yīng)用中，提出了一種改進(jìn)的基于統(tǒng)計(jì)的模糊分類算法，提高了算法查準(zhǔn)率；三、將改進(jìn)的模型算法有效地應(yīng)用在實(shí)際服務(wù)品質(zhì)管理過程中，詳細(xì)闡述了模型的應(yīng)用以及平臺(tái)的物理和技術(shù)架構(gòu)。 CCR模型在支撐投訴管理應(yīng)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(yīng)。
[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(xué)位授予單位】：上海交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：F274;F626

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