电子商务客户行为特征和分析指标—以服装类天猫店为例

电子商务客户行为特征和分析指标—以服装类天猫店为例(任务书,开题报告,论文15000字)
摘  要
随着互联网的飞速发展，企业的经营战略已经从“产品为中心”渐渐变为“客户为中心”，本文的目的是建立电商客户指标体系，方便企业辨别客户，识别客户的需求，在商务营销中改善客户体验，为客户提供个性化服务。
本文采用主要电子商务网站调研的方法，通过学校图书馆中外文数据库等途径阅读电子商务相关文献，对电商客户行为以及分析指标进行分析、研究、归纳、总结。从电子商务的流程出发，介绍并分析了电子商务模式和电子商务客户的主要特点、服装行业的发展状况、企业竞争格局。并且通过对电子商务模式下客户行为、客户分类原则、客户分类指标等相关理论的研究，分析了电子商务环境下客户分析指标存在的必要性。在了解电子商务、客户关系管理的基本原理和业务流程的基础上，分析电子商务客户的行为，以服装类天猫店为例，搜集资料分析B2C服装市场的特征，和选择服装类天猫店的原因。最后归纳总结传统客户分析指标与电商环境下分析指标的优缺点，建立电子商务环境下客户分析指标体系，方便企业识别客户，了解客户的需求，在商务营销中改善客户体验。
在借鉴了前人的研究基础之上，同时参考主流电子商务客户分类指标，运用客户管理、消费者行为学的理论，根据服装类B2C电商客户的特点，建立了基于客户价值、客户行为的电子商务客户分类的三级指标体系。选择了以客户当前价值、客户潜在价值两个维度作为指标体系的一级指标，结合服装类天猫店客户的行为特征，将客户基本信息、用户行为、店铺行为、消费行为作为指标体系的二级指标，并对这四个二级指标进行更深入的分解，最终得到全新的电子商务环境下客户的分析指标体系。
关键词:电子商务 B2C 客户关系管理 客户行为  指标体系
 
E-commerce Customer Behavior Characteristics and Analysis Indicators ——Tmall Online Dress Store as Example
ABSTRACT
With the rapid development of the Internet, The enterprises' core philosophy has gradually transformed from the "product as the center into "take the customer as the center". The purpose of this paper is to establish index system of business customers, convenient enterprise to identify customer, understand customer needs, improve the customer experience in the marketing business, providing customers with personalized service.
The author studied the key e-commerce sites, reviewed related electronic literature resources in and abroad, studied theoretically electricity customer behavior and analysis of indicators systematically. Setting out from the concept of B2C e-commerce, we introduce and analyze the main features of B2C e-commerce mode, industry development status, enterprise competition pattern. And commenced to do a detailed study and analysis of the customer classification index of the presence under e-commerce environment surrounding from customer behavior, index classification, research methods and research status under the e-commerce environment and other related theories. On the basis of understanding of basic principles and business processes of e-commerce, customer relationship management, analyses the e-commerce customer behavior. Then take apparel Tmall online store as example, collect data to analyze features of B2C apparel market and the reasons of selecting apparel Tmall online store. Finally, we summarize the advantages and disadvantages of analysis index between traditional customers and electricity customers, establish index system under the e-commerce environment, convenient enterprise to identify customer, understand customer needs, and improve the customer experience in the marketing business, providing customers with personalized service.
On the basis of drawing previous studies, also refer to mainstream E-commerce customer classification index, use theory of customer management, consumer behavior, in view of the characteristics of B2C e-commerce enterprise customers, established based on customer value, customer behavior B2C three e-business customers index System classification. Select the 
customer current value , the customer potential value as the first level index , combined with apparel Tmall online store customer behavior characteristics, take the basic customer information, user behavior, store behavior, consumer behavior as II level index of index system, and analyze four secondary indicators deeply, finally get a new electricity supplier customer analysis index system.
Key Words：E-commerce；business to customer；customer relationship management；customer behavior；index system
 
目录
摘  要    I
ABSTRACT    II
第一章 引言    1
1.1研究背景和目的    1
1.2研究方法和技术路线    1
第二章 文献综述    3
2.1国内外对电商客户行为分析及研究成果    3
2.2文献述评    5
第三章 电子商务模式下客户行为分析    7
3.1 电子商务的特点    7
3.2 电子商务客户的特点    7
3.3 电子商务客户的行为    8
第四章 以服装类天猫店为例研究客户分析指标    11
4.1服装类电子商务概述    11
4.1.1服装消费者市场的特征    11
4.1.2以服装类天猫店为例的原因    12
4.1.3 服装类电商企业设立客户分析指标体系的重要性    12
4.2建立电子商务客户指标体系的影响因素    13
4.3客户分析指标的特征    14
4.3.1传统商务客户分类指标的特征    14
4.3.2电子商务中客户分析指标的特征    14
4.4电子商务中客户分析指标的选择    15
4.4.1建立电商客户分析指标体系的原则    15
4.4.2参考的指标体系    15
4.4.3电子商务中客户分析指标体系的确定    16
结语    20
参考文献    21
致谢    24