k-means算法在电信客户关系管理中的应用

采用数据挖掘分析了现有的客户关系,但k-means算法聚类的结果对初始值具有敏感性,为了解决这个问题,本文讨论了不同参数的设置,寻找出合理的初始值。通过对客户关系的分析,对不足的部分进行了改进,保持企业的竞争优势,优化管理客户关系。     

数据挖掘在电信行业客户关系管理中的应用

章介绍了数据挖掘技术的相关知识及其在电信行业客户关系管理中的应用，并以客户流失分析作为实例，详细描述了数据挖掘的整个应用过程，最后对国内数据挖掘应用的现状进行了分析。     

数据挖掘技术--模糊聚类分析在客户关系管理中的应用研究

企业的竞争重点正在经历着从以产品为中心向以客户为中心的转移，客户关系管理作为一种全新的管理、经营理念，越来越引起商家的重视。在数据仓库中进行数据挖掘正逐渐成为CRM中最核心的部分。本文从CRM中数据分析的不确定性，提出采用模糊聚类的分析方法来分析、预测客户行为。     

数据挖掘在电信客户关系管理中的应用

当今,电信企业面临激烈竞争,客户成为取胜的关键之一,基于数据挖掘的客户关系管理应运而生。本文数据挖掘技术在电信客户关系管理中的应用进行探讨。     

数据挖掘在电信客户关系管理中的应用研究

本文主要研究了数据挖掘在电信顾客关系管理中的应用,构建了基于数据挖掘的电信CRM框架体系,将数据挖掘技术应用到客户细分、流失预测以及交叉营销等方面,实现客户全生命周期的科学化管理。最后,以XX电信公司的彩铃业务为例,采用Apriori算法来挖掘与分析其交叉销售的策略。     

聚类分析在客户关系管理中的研究与应用

客户关系管理是以客户为中心，保持企业与客户互动的过程，把聚类分析的数据挖掘技术运用于客户关系管理可以改善客户关系，并对将来的趋势和行为进行预测，从而支持决策。采用最小方差法的谱系聚类对样本数据进行聚类，挖掘和分析客户群中所存在的不同特征的组群，得到直观的聚类过程和较合理的分组结果。     

数据挖掘在客户关系管理中的应用研究

客户关系管理是企业在当今的电子商务环境中,利用数据仓库和数据挖掘等先进的智能化信息处理技术,把企业收集和存储的大量业务数据和有关客户资料加工成信息和知识,用来辅助决策及规划相关的企业运营活动的过程,是一个复杂的现代系统工程。结合计量机构现状,选择了这一课题开展相关工作,对在计量领域市场运作中引入CRM进行探讨。     

数据挖掘在客户关系管理中的应用研究

为了从企业客户数据库中挖掘出用户的商业行为和规律,研究了数据挖掘的体系结构和客户关系管理框架,以某电器行业客户关系管理系统为例,设计了系统的功能模块。针对系统的实施,构建了其数据仓库模型,设计了基于数据挖掘的客户关系挖掘算法。     

数据挖掘在银行客户关系管理中的应用分析

客户关系管理是一种改善企业与客户关系,以客户服务为中心的新型管理机制。数据挖掘技术能够对大量的数据信息进行分析处理,提取出有潜在价值的规则和信息,利用获取的模式对客户需求进行预测,为企业决策提供指导。数据挖掘是客户关系管理成功的关键和基础。     

数据挖掘在客户关系管理中的应用研究

为了从企业客户数据库中挖掘出用户的商业行为和规律,研究了数据挖掘的体系结构和客户关系管理框架,以某电器行业客户关系管理系统为例．设计了系统的功能模块。针对系统的实施,构建了其数据仓库模型,设计了基于数据挖掘的客户关系挖掘算法。     

系统聚类在客户关系管理中的研究与应用

提出了一种基于粗糙集联系度的聚类分析新方法,首先用粗糙集和集对分析理论对信息系统进行知识约简,然后用聚类的方法对约简后的信息系统进行聚类分析,论述了该方法的聚类过程,并给出了聚类分析的实例。     

Determinant elements of customer relationship management in e-business

This study investigates the composition of customer relationship management (CRM) in e-business by examining the possible elements that determine different aspects of the relationship between customers and e-businesses. A web-based CRM survey of 38 items, constructed from SERVQUAL (service quality instrument), SITEQUAL (website service quality instrument) and literature findings, was completed by 200 customer contact professionals. Results of a factor analysis indicated three main customer relationship attributes of e-business, which are: general CRM (accounting for 51% of the total variance); personalization (accounting for 9% of the total variance); and privacy (accounting for 7% of the total variance). Results of a stepwise regression indicated that these customer relationship attributes significantly predict customer attitude (83% of the explained variance). Within the general CRM dimension, website content organization correlated highly with customer attitude (65% of the explained variance). The results of the study indicate that customers perceive three main dimensions of relationship attributes of e-business (general CRM, personalization and privacy) and that all three significantly contribute to customer attitude. These findings support the importance of including relational-type e-business attributes when investigating interactions between customers and e-business. The study concludes with related implications and design guidelines to enhancing customer perception of e-business.     

四种聚类方法之比较

介绍了较为常见的k-means、层次聚类、SOM、FCM等四种聚类算法,阐述了各自的原理和使用步骤,利用国际通用测试数据集IRIS对这些算法进行了验证和比较.结果显示对该测试类型数据,FCM和k-means都具有较高的准确度,层次聚类准确度最差,而SOM则耗时最长.     

IT alignment strategies for customer relationship management

Customer relationship management (CRM) is the overall process of building and maintaining profitable customer relationships by delivering superior customer value and satisfaction. A CRM strategy involves the entire enterprise and is employed on an ongoing basis. Despite the fact that CRM projects incur huge expenditures, a large percentage fails to achieve the stated objectives. Failure in CRM initiatives could be avoided if a firm's CRM strategies are intelligently linked with its employees, customers, channels, and IT infrastructure. In this paper, we focus on those linkages, particularly on the linkages between an organization's CRM strategies and its IT infrastructure. Even though the relationships between IT and business strategies have been extensively explored in the IT alignment literature, prior research has not addressed how a firm's CRM strategies are aligned with its IT infrastructure. In this paper, we investigate the issues relating to CRM-IT alignment based on an in-depth case study of a large, well-known Internet travel agency.     

The priority factor model for customer relationship management system success

As the market competition becomes keen, constructing a customer relationship management system is coming to the front for winning over new customers, developing service and products for customer satisfaction and retaining existing customers. However, decisions for CRM implementation have been hampered by inconsistency between information technology and marketing strategies, and the lack of conceptual bases necessary to develop the success measures. Using a structural equation analysis, this study explores the CRM system success model that consists of CRM initiatives: process fit, customer information quality, and system support; intrinsic success: efficiency and customer satisfaction; and extrinsic success: profitability. These constructs underlie much of the existing literature on information system success and customer satisfaction perspectives. We found the empirical support for CRM implementation decision-making from 253 respondents of 14 companies which have implemented the CRM system. These findings should be of great interest to both researchers and practitioners.     

SQL Server 2008在煤炭企业智能客户关系管理中的应用研究

针对煤炭企业客户关系管理系统的信息化、智能化需求,提出了一种基于SQL Server 2008的煤炭企业智能客户关系管理系统设计方案;给出了智能客户关系管理系统的数据挖掘流程,详细介绍了SQL Server 2008中的商业智能工具在煤炭企业智能客户关系管理系统中的应用,包括煤炭企业智能客户关系管理系统的集群部署结构及其软件架构。实例分析结果验证了该系统的可行性。     

SQL Server 2008在煤炭企业智能客户关系管理中的应用研究

针对煤炭企业客户关系管理系统的信息化、智能化需求,提出了一种基于SQL Server 2008的煤炭企业智能客户关系管理系统的设计方案;给出了智能客户关系管理系统的数据挖掘流程,详细介绍了SQL Server 2008中的商业智能工具在煤炭企业智能客户关系管理系统中的应用,包括煤炭企业智能客户关系管理系统的集群部署结构及其软件架构。实例分析结果验证了该系统的可行性。     

双聚类算法在电信高价值客户细分的应用

针对传统客户价值细分方法在高价值客户细分时不够精细化的问题,引入了大均值子矩阵(LAS)双聚类算法。该方法在客户样本和消费属性两个维度上对消费记录进行双向聚类,可以挖掘出高消费、高价值的客户群体。以某电信公司的高价值客户细分为实例,通过定义一个价值尺度和构建一个PA指标,将所提算法与K均值(K-means)算法进行性能比较,实验结果表明,所提算法能挖掘出更多的高价值客户群体,且能够对客户属性进行更加精细的划分,因此它更适合应用于高价值客户市场的识别和细分。     

Why build a customer relationship management capability?

The market enthusiasm generated around investment in CRM technology is in stark contrast to the naysaying by many academic and business commentators. This raises an important research question concerning the extent to which companies should continue to invest in building a CRM capability. Drawing on field interviews and a survey of senior executives, the results reveal that a superior CRM capability can create positional advantage and subsequent improved performance. Further, it is shown that to be most successful, CRM programs should focus on latent or unarticulated customer needs that underpin a proactive market orientation.