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。     

基于数据挖掘的航空公司客户信息分析

通过对航空公司现有数据仓库中客户信息的分析,针对客户关系管理中忠诚度这一问题,提出了一种基于数据挖掘技术的客户忠诚度计算模型;并通过实验对客户群进行了聚类分析,从而为航空公司针对不同客户群制定实施客户关怀,完善客户关系管理系统提供一定的理论依据。     

改进的K-means算法在电信客户细分中的应用

在K-means算法中,选择不同的初始聚类中心会产生不同的聚类结果且有不同的准确率,并且其迭代过程在时间上不是高效的。针对K-means算法的这两点不足做了一定程度上的改进,理论分析表明,改进后的算法具有较高的准确度和较低的时间复杂度。采用改进后K-means聚类算法对电信客户数据进行聚类分析,得到具有不同特征的客户群组,通过与统计分析的对比,聚类结果分析更合理清晰,更便于对不同群组采取不同的经营策略,为管理者提供了合理的决策支持。     

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。     

K-means算法在电信CRM客户分类中的应用

面对电信市场竞争的日益加剧和信息技术的迅猛发展,电信运营商必须建立以“客户为中心”的管理模式。将客户进行分类,针对不同的客户,研究出相应的营销策略。数据挖掘中的K—means聚类算法能对大型数据集进行高效分类。对K—means算法进行改进,使其能够应用于复杂的电信客户关系管理,实现更加准确和全面的客户分类。     

改进的K-means算法在电信客户细分中的应用

在K-means算法中,选择不同的初始聚类中心会产生不同的聚类结果且有不同的准确率,并且其迭代过程在时间上不是高效的.针对K-means算法的这两点不足做了一定程度上的改进,理论分析表明,改进后的算法具有较高的准确度和较低的时间复杂度.采用改进后K-means聚类算法对电信客户数据进行聚类分析,得到具有不同特征的客户群组,通过与统计分析的对比,聚类结果分析更合理清晰,更便于对不同群组采取不同的经营策略,为管理者提供了合理的决策支持.     

商业智能在客户关系管理中的应用研究

以在电信行业中如何运用客户行为对客户进行细分为例阐述了如何在客户关系管理(CRM)中实现客户细分智能。     

基于大数据的电信客户管理技术研究

随着经济社会的发展,国内通信产业结构不断完善,业务覆盖面日趋完整,通信业务规模不断壮大,与此引发的基础电信运营商间的竞争愈发激烈。借助信息化手段在当今激烈的竞争环境中立于不败之地,面对海量的客户群体以及用户行为数据进行数据的处理,并利用数据挖掘技术从中获取有利的需求预测,为运营商建立客户信息基础数据库、制定精准产品以及提供正确的方案决策,是运营商今后关注的方向。使用大数据技术开展对电信客户管理的相关研究,从客户关系管理的现状入手,进行电信客户关系数据挖掘的方案及算法设计,在大数据分析基础上建立了电信客户关系细分模型。     

基于动态聚类的证券业客户细分实证研究

在客户关系管理理论基础上,建立了一个包含13个行业特色指标的证券业客户多维细分模型,并利用聚类分析对国内某知名券商的具体客户信息和交易数据进行了实证研究,有效识别出了具有不同特征以及偏好的客户群,并在此基础上提出了相应的营销策略。     

决策树算法在电信客户细分中的应用

电信客户细分在电信客户关系管理中占据着重要的地位．正确的客户细分能够指导企业寻找目标市场，产品定位等一系列市场营销活动，使企业经营活动曼为有效。时决策树算法在客户细分。中的应用作了一些研究，关于如何流住高价值客户，发掘潜力客户进行了阐述，以指导电信企业更好决策。     

数据挖掘技术在CRM中的应用

CRM（客户关系管理）是一种旨在改善企业与客户之间关系的新型管理机制。数据挖掘技术应用于CRM中，能够加强和改善客户关系管理，从而为企业带来更多的利润。该文对数据挖掘技术在CRM中的应用内容和过程进行了研究。     

Study of the wireless/wire integration CRM Gateway for the effective application of Event CRM for small and medium sized enterprises

The CRM System, which maximizes business profits by pursuing a continuous relationship with customers, is based on an analytical CRM that sets up a marketing strategy by analyzing customer information. However, thanks to the technological development of the Internet and mobile phones, customer contact is being carried out through a variety of channels. Yet currently, the needs of customers are not being addressed in a timely manner due to a weak system that cannot immediately deal with customer requests and because of which customer information is not administered in a systematic manner.Therefore, rather than focusing on off-line-focused analytical CRM, it is necessary to concentrate on real time CRM that reflects the aspects of the operative or collaborative CRM. This study also develops an Event CRM solution that can bring satisfaction to customers when they want it by systematizing the contact points of customers, which constitute various institutions and channels.The CRM model that this study presents is the support of Event CRM services for all business types against the backdrop of a wireless/wire environment, and the support of small and medium sized companies, which are burdened by information management costs, to meet the demand for CRM.In order to present the wireless/wire integration CRM Gateway Model, the study focuses on insurance CRM services.When a customer event arises through various channels such as the Internet, SFA or CTI, the event data are transferred through a standardized form. Based on these data, the campaign and service is then extracted and analyzed with the Event CRM Gateway Engine. A match is immediately made between the saved rules and the campaign and services that best fit the customer. Finally, the information is provided to the customer via a mobile phone or website.     

基于关系图提取算法的烟草销售客户关系管理的应用研究

展望客户关系管理,数据挖掘技术在其中起到了至关重要的作用,它可以挖掘出千万数据中的有用信息,企业才能对客户进行各类价值分类,然后预测客户的行为,从而做出正确有效的决策。在CRM应用中,基于数据挖掘技术的基础上,结合CRM系统的不足,为避免或减少客户管理系统因管理不到位而导致客户的流失,造成企业的损失,提出了客户关系图的提取算法,很好地分析了数据挖掘技术在CRM中的应用。     

Extracting Actionable Knowledge from Decision Trees

Most data mining algorithms and tools stop at discovered customer models, producing distribution information on customer profiles. Such techniques, when applied to industrial problems such as customer relationship management (CRM), are useful in pointing out customers who are likely attritors and customers who are loyal, but they require human experts to postprocess the discovered knowledge manually. Most of the postprocessing techniques have been limited to producing visualization results and interestingness ranking, but they do not directly suggest actions that would lead to an increase in the objective function such as profit. In this paper, we present novel algorithms that suggest actions to change customers from an undesired status (such as attritors) to a desired one (such as loyal) while maximizing an objective function: the expected net profit. These algorithms can discover cost-effective actions to transform customers from undesirable classes to desirable ones. The approach we take integrates data mining and decision making tightly by formulating the decision making problems directly on top of the data mining results in a postprocessing step. To improve the effectiveness of the approach, we also present an ensemble of decision trees which is shown to be more robust when the training data changes. Empirical tests are conducted on both a realistic insurance application domain and UCI benchmark data     

Web数据挖掘在企业CRM中的应用

CRM的本质在于通过WWW的渠道,在营销、销售、服务和支持四个方面与客户建立良好的关系,从而提高企业收益。在电子商务中,提高客户忠诚度保持住客户,实现交叉销售等成为电子商务成败的一个关键问题。而Web数据挖掘能在电子商务中更好地运作CRM,建立良好客户关系的一种解决方法。该文研究了Web数据挖掘技术在CRM中的应用。     

Including spatial interdependence in customer acquisition models: A cross-category comparison

Within analytical customer relationship management (CRM), customer acquisition models suffer the most from a lack of data quality because the information of potential customers is mostly limited to socio-demographic and lifestyle variables obtained from external data vendors. Particularly in this situation, taking advantage of the spatial correlation between customers can improve the predictive performance of these models. This study compares an autoregressive and hierarchical technique that both are able to incorporate spatial information in a model that can be applied on large datasets, which is typical for CRM. Predictive performances of these models are compared in an application that identifies potential new customers for 25 products and brands. The results show that when a discrete spatial variable is used to group customers into mutually exclusive neighborhoods, a multilevel model performs at least as well as, and for a large number of durable goods even significantly better than a frequently used autologistic model. Further, this application provides interesting insights for marketing decision makers. It indicates that especially for publicly consumed durable goods neighborhood effects can be identified. However, for more exclusive brands, incorporating spatial information will not always result in major predictive improvements. For these luxury products, the high spatial interdependence is mainly caused by homophily in which the spatial variable is a substitute for absent socio-demographic and lifestyle variables. As a result, these neighborhood variables lose a lot of predictive value on top of a traditional acquisition model that typically is based on such non-transactional variables.     

基于数据仓库技术的CRM决策支持系统模型设计

针对目前企业决策支持系统面临的新问题，介绍了CRM相关知识和决策支持系统的前沿技术——数据仓库及数据挖掘和联机分析处理，并在此基础上，提出了以数据仓库为中心、数据挖掘和联机分析处理为手段的面向客户关系管理的决策支持系统模型框架，描述了CRM数据运作流程和数据仓库等技术在其中所起的重要作用。     

Exploring the role of customer relationship management (CRM) systems in customer knowledge creation

This study explores how customer relationship management (CRM) systems support customer knowledge creation processes [48], including socialization, externalization, combination and internalization. CRM systems are categorized as collaborative, operational and analytical. An analysis of CRM applications in three organizations reveals that analytical systems strongly support the combination process. Collaborative systems provide the greatest support for externalization. Operational systems facilitate socialization with customers, while collaborative systems are used for socialization within an organization. Collaborative and analytical systems both support the internalization process by providing learning opportunities. Three-way interactions among CRM systems, types of customer knowledge, and knowledge creation processes are explored.