上海移动公司客户投诉管理研究及应用

发布时间：2018-08-22 15:15

【摘要】：企业之间的竞争归根到底是对客户资源的竞争。中国移动要在全业务竞争中领先，需要将客户规模优势转化为客户关系优势，需不断提升客户满意度。上海移动在客户快速增长同时客户投诉量也在快速增长，如何能对客户投诉内容进行深入分析，快速聚焦重点投诉，发现产品、服务短板并快速落实优化，提升客户对移动服务的感知，这对提升移动服务品质，提高客户满意度，巩固移动品牌具有重要且深远的意义。投诉管理是服务品质管理中的重要组成部分，，上海移动在投诉管理中积累了大量的文本数据，一方面这些数据蕴含了对用户诉求的直接描述，另一方面如何快速从这些数据中获取知识并予以应用成为问题。文本挖掘作为知识挖掘的组成，从非结构化、异构的文本集合中发现有效的、新颖的、可用的以及能被理解的知识,是解决上述问题的一种方法。但是，如何将文本挖掘技术的理论和工具有效应用到实际的工作中满足投诉文本处理和分析的需要，成为一项挑战。本次论文从投诉系统支撑、流程管理、知识积累三方面研究了上海移动客户投诉管理现状，总结了存在的问题。针对这些问题，本次研究主要开展了三方面的工作：一、研究了文本数据挖掘的相关理论及分类方法。提出了上海移动实际应用文本挖掘理论构建文本挖掘模型的原理及过程；二、研究了支持向量机和KNN等文本分类算法的基本原理。在实际应用中，提出了一种改进的基于统计的模糊分类算法，提高了算法查准率；三、将改进的模型算法有效地应用在实际服务品质管理过程中，详细阐述了模型的应用以及平台的物理和技术架构。 CCR模型在支撑投诉管理应用后，有效降低了客户投诉量，提升了客户满意度，节支增效降低人工成本，带来较好的示范效应。
[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.
【学位授予单位】：上海交通大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：F274;F626

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