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发布时间：2026年03月15日作者:aiycxz.cn

基于数据挖掘的电力营销客户细分研究重庆大学硕士学位论文（专业学位）学生姓名：王婷指导教师：陈玲副教授专业学位类别：工程（软件工程）研究方向：数据挖掘答辩委员会主席：文俊浩教授授位时间：2018年12月# Research on Customer Segmentation of Electric Power Marketing Based on Data MiningA Thesis Submitted to Chongqing University in Partial Fulfillment of the Requirement for the Professional Degree## By Wang Ting### Supervised by Ass. Prof. Chen Ling#### December, 2018中文摘要摘要随着电力体制改革的不断深入，电力市场逐步由卖方市场转变为买方市场，电力企业之间的竞争日益激烈，电力营销工作成为电力企业的核心业务。电力营销客户细分是电力营销工作的基础，通过客户细分，电力企业可以更好地了解客户需求，针对不同客户群体制定差异化的营销策略，从而增强客户粘性、提升企业效益。本文以电力营销客户为研究对象，以数据挖掘技术为支撑，对电力营销客户细分问题进行研究。主要研究内容如下：① 分析电力营销客户细分的研究背景及意义，对国内外研究现状进行综述，总结现有研究的不足，提出本文的研究内容及技术路线。② 对电力营销客户细分相关理论进行阐述，包括电力营销客户细分的内涵、作用、原则及流程，分析电力营销客户细分的影响因素，并介绍数据挖掘相关技术。③ 构建电力营销客户细分指标体系。从客户价值、客户信用、客户行为三个维度出发，分析电力营销客户细分指标，构建电力营销客户细分指标体系。在此基础上，利用主成分分析法对指标体系进行降维，得到电力营销客户细分主成分指标。④ 构建电力营销客户细分模型。针对传统 K-means 算法存在的不足，提出一种改进的 K-means 算法，该算法利用信息熵确定初始聚类中心，并采用轮廓系数法确定最佳聚类数目。在此基础上，构建基于改进 K-means 算法的电力营销客户细分模型。⑤ 实证分析。以某市电力企业客户数据为例，对本文构建的电力营销客户细分模型进行实证分析，验证模型的有效性。根据实证分析结果，将电力营销客户划分为四类，并针对不同客户群体提出相应的营销策略。关键词：电力营销；客户细分；数据挖掘；K-means 算法1英文摘要AbstractWith the deepening of power system reform, the power market has gradually changed from a seller's market to a buyer's market. The competition among power enterprises is becoming increasingly fierce. Power marketing has become the core business of power enterprises. Customer segmentation of power marketing is the basis of power marketing work. Through customer segmentation, power enterprises can better understand customer needs and formulate differentiated marketing strategies for different customer groups, so as to enhance customer stickiness and improve enterprise efficiency.This paper takes power marketing customers as the research object, supports by data mining technology, and studies the customer segmentation of power marketing. The main research contents are as follows:① Analyze the research background and significance of customer segmentation in power marketing, summarize the research status at home and abroad, summarize the shortcomings of existing research, and put forward the research content and technical route of this paper.② This paper expounds the related theories of customer segmentation in power marketing, including the connotation, function, principle and process of customer segmentation in power marketing, analyzes the influencing factors of customer segmentation in power marketing, and introduces the related technologies of data mining.③ Construct the index system of customer segmentation in power marketing. From the three dimensions of customer value, customer credit and customer behavior, this paper analyzes the customer segmentation index of power marketing and constructs the index system of customer segmentation of power marketing. On this basis, principal component analysis is used to reduce the dimension of the index system, and the principal component index of customer segmentation in power marketing is obtained.④ Construct a customer segmentation model for power marketing. Aiming at the shortcomings of traditional K-means algorithm, an improved K-means algorithm is proposed. The algorithm uses information entropy to determine the initial clustering center and uses silhouette coefficient method to determine the optimal clustering number. On this basis, a customer segmentation model of power marketing based on improved K-means algorithm is constructed.III重庆大学硕士学位论文⑤ Empirical analysis. Taking the customer data of a city's power enterprise as an example, this paper makes an empirical analysis of the customer segmentation model of power marketing constructed in this paper, and verifies the validity of the model. According to the results of empirical analysis, power marketing customers are divided into four categories, and corresponding marketing strategies are proposed for different customer groups.Keywords: Power marketing; Customer segmentation; Data mining; K-means algorithmIV目录目录中文摘要......I英文摘要......III1 绪论......11.1 研究背景及意义

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