基于大数据的电信消费者细分研究

基于大数据的电信消费者细分研究(任务书,开题报告,论文12000字)
摘要
伴随着移动互联网技术的发展，技术的变化可以说是日新月异，导致传统的通信业也开始发生了翻天覆地的变化。网络的不断升级、不同产业间的融合、以及运营商之间的竞争日益激烈等等这一系列的挑战，促使着老牌基本运营商中国电信也必须跟上时代的脚步，逐渐的改变它原有的传统经营模式，更好地为用户创造更加优质快捷的个性化还有多样化服务。伴随着大数据时代的到来，消费市场改变了传统意义上的差异性区分，通过大数据对消费者进行相关性细分，借用 SPSS Statistic 软件对收集的数据进行了相关性和共线性分析，并进行了数据清洗；期间利用 Kohonen 聚类分析对 K-means 聚类结果进行了优化，提高了结果的可靠性和准确性，本文将消费者细分与大数据相结合的目的是通过有效的消费者细分，做出针对性的营销策略，实现企业收益最大化。

关键词:大数据 消费者 细分 市场
Research on consumer segmentation in the context of big data
Abstract
     With the development of mobile Internet technology, technological changes can be said to be changing, leading to the traditional communications industry has begun to undergo earth-shaking changes. The continuous upgrading of the network, the integration between different industries, as well as the increasingly fierce competition between operators and so on this series of challenges, prompting the old basic operators China Telecom must also keep up with the pace of the times, gradually change its original Of the traditional business model, and better for users to create more quality and efficient personalized and diversified services. With the arrival of the large data age, the consumer market has changed the traditional sense of the difference between the distinction, through the large data on the consumer correlation segmentation, using SPSS Statistic software to collect the data were related and collinear analysis, and The results of K-means clustering analysis are optimized by Kohonen clustering analysis, which improves the reliability and accuracy of the results. In this paper, the purpose of combining consumer segmentation with large data is through effective consumption To break down, to make targeted marketing strategies to achieve maximum business benefits
Key words: big data; consumer; segmentation; market
 
目录
摘要    I
Abstract    2
第一章 绪论    3
1.1研究背景    3
1.2研究内容    4
1.3研究方法    4
第二章 文献综述    5
2.1大数据研究    5
2.1.1大数据的概念界定    5
2.1.2大数据的基本特点    5
2.1.3大数据的使用价值    5
2.2消费者细分研究    6
2.2.1消费者细分定义    6
2.2.2消费者细分方法比较    7
2.2.3细分结果的评估    8
第三章 电信消费者数据的聚类方法    8
3.1 K-means 算法及其优化 ——Kohonen 网络聚类    8
3.2 Kohonen 网络聚类的实行    9
第四章 基于kohonen网络聚类的电信消费者细分    14
4.1 电信消费者细分    14
4.1.1 商业理解    14
4.1.2 数据理解    15
4.1.3 数据准备与预处理    16
4.2 电信客户细分的结果检测与研究分析    21
4.2.1 电信客户细分结果检测    21
结束语    24
参考文献    25
致谢    28