基于数据挖掘的移动通讯消费者行为分析.doc

摘要

随着信息化时代的来临，移动通讯市场的竞争越来越激烈，抢占市场份额、提高客户与企业之间的黏度是移动通讯企业一直的目标。消费行为分析是客户关系管理的重要组成部分, 传统的分析都是借助于经济学的基本理论进行的,没有进行定量的研究,结果存在一定的局限性。在新技术不断发展的今天,数据挖掘技术作为一项强大的数据分析技术, 在客户关系管理中的应用正得到越来越多人的关注。在以客户为中心的竞争环境中，如果既能拥有大量的信息，就能在激烈的竞争中取得优势。数据挖掘是从大量数据中提取或挖掘知识进行数据分析，

从而发现潜在信息的技术。对客户进行细分能够帮助企业从更加深入全面的角度洞察客户、

了解客户价值取向，基于这种洞察在合适的时间通过合适的渠道向合适的客户提供量身定做

的产品套餐。基于此背景提出了该课题。

如何从大量的消费者消费记录中发现消费者的消费行为，对移动通讯企业提高客户的满意度

等有着重要的战略意义。本文基于数据挖掘的移动通信消费者消费行为的研究以数据进行驱

动，对移动通讯消费者消费行为进行了相关分析，基于已处理的数据，进行消费者细分。通过 K-Means、Two-Step 和 Kohonen 聚类方法，分别进行聚类，最终选择了 K-Means 的细分结果作为消费者细分准则，得到五类消费者，即重要保持客户、重要发展客户、重要挽留客户、

一般价值客户和低价值客户。本文第一章首先阐述了数据挖掘的相关理论，并对消费者行为分析

进行分析，第二章阐述了数据挖掘理论，介绍了数据挖掘的特点和数据挖掘的一般过程

以及数据挖掘的特点。第三章进行了消费者行为分析，包括客户关系的管理、CRM流程、消费者行为分析和消费者细分的方法，以及移动通讯企业的消费者细分问题。第四章描述了移动通讯消费者细分的案例，进行了数据预处理消费者聚类，以及细分客户消费行为分析。第五章进行了移动通讯消费者的相关性分析，包括消费者购买的相关性消费者消费行为的分

析，在第六章进行了总结与展望。本文在移动通讯消费者购买倾向上共进行了CART算

法、

CHAID算法和 C5.0 算法，这三种算法进行处理，最终的二道重要保持客户和年龄关系较大，

重要挽留客户和消费频率关系较大，重要发展客户则和最近一次消费时间相关性高，一般价值客户和消费频率与消费金额有关，低价值客户则和性别有一定关系。针对此，在展开营销策划时，可以针对性进行营销。j6j7f6o1k3 。

关键词： RFM、客户细分、数据挖掘、CART算法、消费者行为

ABSTRACT

With the advent of the information age, competition in the mobile communications market more competitive, market share, enhance viscosity between customers and

business mobile communications business has been the goal. At the same time, the

use of mobile communication more and more consumers, how to find consumer behavior from a large number of consumer spending recorded in the mobile communications

business has important strategic significance to improve customer satisfaction. j6j7f6o1k3 。

This paper is the study of consumer behavior mobile communication about data

mining, first elaborated the theory of data mining, analysis and consumer behavior analysis, and the resulting data into the sample, perform RFManalysis, consumption records from the consumer, That c onsumer ID, spending time and amount of consumption to its R, F, M value, the next consumer to provide the data base segmentation,

analysis of their value by the consumer, is more straightforward. j6j7f6o1k3 。

Based on the processed data, conduct consumer segmentation. By K-Means, Two-Step and Kohonen clustering methods, were clustering, chose K-Means segments results as consumer segmentation criteria to give consumers five categories, namely important to keep customers, an important development client it is important to retain customers, the general value customers and low-value customers. On the basis of

consumer segmentation based on different types were consumer behavior analysis more meaningful. j6j7f6o1k3 。

Since this data has 24785 data, but consumers only 10085, data distribution may not satisfy some algorithms, this mobile consumers to buy CARTalgorithm were carried out on the tendency, CHAID algorithm and C5.0 algorithms, these three algorithms

processing, final important to maintain a large customer and their age, the larger retain customers and important relationship between frequency of consumption, important developments and recent customers are spending time correlation is high, the general value customers and consumption frequency and amount of consumption

related, low-value customers are and gender have a certain relationship. For this, in the expanded marketing plan, you can carry out targeted marketing. j6j7f6o1k3 。

In this paper, data-driven, mobile communications consumer spending behavior

correlation analysis, corporate marketing planning for the future development of