基于数据挖掘的移动通信业客户流失预警系统设计

借助SASEM平台。对移动通信业务数据使用数据挖掘算法建立客户细分模型,能够刻画移动通信客户的行为特征,并以此建立客户流失预测模型。从而建立一个移动通信业客户流失预警系统。实践证明,该方法实用、可操作性强,对支持企业客户关系管理产生了积极的影响。     

C4.5算法在移动通信行业客户流失分析中的应用

稳定客户和吸引客户是移动通信企业提高竞争力的关键.基于大量实验数据将数据挖掘的决策树方法引入移动通信行业客户流失分析中,通过对数据的预处理,利用C4.5算法创建决策树,通过测试流失的与未流失的客户,平均正确识别率为91.6%.决策树体现的规则与经验基本一致,为移动通信企业建立客户流失的预警机制提供了决策支持.     

移动通信业客户流失行为预测技术的研究

为了科学合理地制定有效的营销和服务策略,以最大程度地降低客户的流失率,结合目前移动通信行业激烈竞争的现状,运用先进的数据挖掘和机器学习相关技术建立预测模型,对移动通信业中客户流失行为进行预测并分析其流失的原因,以便采取相应措施去减小客户的流失率,从而实现其利润最大化。实践证明,该方案在运营商提高客户保持力和实现利润最大化方面已做出积极的贡献。     

基于PMML的移动通信客户流失模型交换

移动通信领域迫切需要在地理分布的经营分析系统之间交换标准的数据挖掘模型。尽管预测模型标记语言已经成为数据挖掘模型交换格式的业界标准，但并没形成可用的框架来指导标准交换模型的生产过程。该文提出了支持挖掘模型交换和移动通信客户流失分析的决策树算法框架。利用该框架构建了流失预警系统，并使用模拟客户数据验证了其有效性。对标准交换模型进行了适当扩展，以支持对移动通信数据更加有效的流失分析。     

基于数据仓库的客户挽留系统

随着移动通信运营市场的竞争日趋激烈,移动电话客户"大进大出",导致客户离网率居高不下,造成营销资源的大量浪费。企业为了保持市场份额和运营效益,通过系统支撑手段加强客户流失挽留工作。锁定中高端客户,通过聚焦客户关怀、业务维系、流失预警挽留等重要手段,有针对性地开展服务营销工作,有效延长客户生命周期,保留存量市场,从而节约营销成本。     

一种基于电信客户流失预警的解决方案

本文通过对电信运营商海量数据的统一整合,并对客户的基本属性、呼叫行为、缴费情况、客户服务投诉情况等数据深入研究,分析出已流失或有流失趋势客户的行为特征,建立了客户流失模型,预测具有流失倾向的客户,进行预警.并分析流失原因,针对可能流失的客户设计挽留方案,对挽留方案进行实施、跟踪和评估,形成流失分析和管理的闭环流.     

数据挖掘在电信客户流失中的应用

将数据挖掘技术应用到防止电信客户流失中,以某电信运营商的历史资料为对象,建立客户流失预测模型。并对高价值高流失概率的客户进行K-means聚类分析,同时对不同流失客户群提供相应的营销策略。     

SVM在移动通信客户流失预测中的应用研究

使用支持向量机(SVM,Support Vector Machine)数据挖掘方法对移动通信行业客户流失倾向进行预测,对支持向量机同决策树算法预测的结果进行对比,结果表明支持向量机对本文所选取的属性数据具有更强的分类能力,而且在不同训练数据规模情况下预测模型有较好的稳定性。实验证实,运用本研究模型选取全体客户的22.31%,可以预测出50.07%流失的客户,表明本研究中提出的预测模型具有实际应用价值。     

基于RFM模型的随机森林算法对民航客户的流失分析

近几年,随着航空市场的快速发展,对于航空公司而言,如何在增加市场占有率的同时,对客户的流失进行有效的控制也刻不容缓。基于随机森林算法,根据航空客户数据,建立流失预测模型,对客户是否已流失进行预测研究,将传统的RFM客户价值模型进行改进,结合随机森林算法对客户流失进行预测。实验结果表明,基于RFM模型的随机森林算法构建的客户流失模型拥有更具有说服力的指标选取,AUC值达到0.92,且准确率较高。利用该模型可对航空公司客户流失进行较为准确的预测,对流失客户进行分类,为民航企业提供营销策略。     

基于神经网络的电信客户流失预测主题建模及实现

客户流失管理是电信运营商通过对客户需求满意度调查进行有针对性挽留客户的一个重要方法，其中最关键的就是对客户流失行为做出预测。提出了一种基于神经网络的客户流失预测模型。根据行业专家经验值选取分析变量，通过神经网络计算分析变量的权值，建立客户流失预测模型并对客户流失趋势进行预测。该方法与决策树和贝叶斯网络等算法相比，通过使用两次神经网络，从原始数据上千个属性中提炼出与客户流失度相关性较大的属性，分析出的影响流失属性更利于下一步的客户挽留工作。     

Increasing trust in mobile commerce through design aesthetics

The growth of mobile commerce (m-commerce) has motivated a better understanding of how trust can be built on a mobile device. Researchers have previously examined design aesthetics (or visual aesthetics) of mobile website and incorporated a hedonic component of enjoyment in m-commerce domain, but the relationship between design aesthetics of mobile website design and customer trust in m-commerce has been rarely investigated. In this study, design aesthetics was enhanced to include a website characteristics component as important to trust development on the mobile Internet. This model was examined through an empirical study involving 200 subjects using structural equation modeling techniques. Our research found that design aesthetics did significantly impact website characteristics component, especially customization, perceived usefulness and ease of use, all of which were ultimately shown to have significant explanatory power in affecting customer trust.     

基于WEKA的客户分类信息系统研究

本文采用决策树方法,对客户交易数据和客户基本信息进行数据挖掘分析,降低了数据冗余度,提高了数据集准确率。在RFM模型基础上,从客户交易信息中选取了购买频率和平均每次购买金额作为分类评估指标的补充,得到一组客户交易数据训练集。结合J48算法使用WEKA算法对客户交易数据训练集进行训练、测试和验证,构建了客户分类决策模型,从而有利于客户分类原型系统的系统分析和系统设计。     

Matrix based proactive resource provisioning in mobile cloud environment

Mobile cloud computing is a dynamic, virtually scalable and network based computing environment where mobile device acts as a thin client and applications run on remote cloud servers. Mobile cloud computing resources required by different users depend on their respective personalized applications. Therefore, efficient resource provisioning in mobile clouds is an important aspect that needs special attention in order to make the mobile cloud computing a highly optimized entity. This paper proposes an adaptive model for efficient resource provisioning in mobile clouds by predicting and storing resource usages in a two dimensional matrix termed as resource provisioning matrix. These resource provisioning matrices are further used by an independent authority to predict future required resources using artificial neural network. Independent authority also checks and verifies resource usage bill computed by cloud service provider using resource provisioning matrices. It provides cost computation reliability for mobile customers in mobile cloud environment. Proposed model is implemented on Hadoop using three different applications. Results indicate that proposed model provides better mobile cloud resources utilization as well as maintains quality of service for mobile customer. Proposed model increases battery life of mobile device and decreases data usage cost for mobile customer.     

基于支持向量机的移动电话顾客满意度评价系统

移动电话顾客满意度的评价对于移动电话生产商和经销商的经营管理有着重要的意义.利用支持向量机的全局收敛性和良好的推广能力,提出了一种新的基于支持向量机的移动电话顾客满意度评价系统.归纳了移动电话顾客满意度评价指标的设计原则,给出了具体的评价指标体系,采用支持向量机的1-v-1分类策略建立了顾客满意度的评价模型.仿真结果表明,基于支持向量机的顾客满意度评价模型能够有效地实现顾客满意度的评估.该模型的建立为移动电话供应商提供了一个有力的顾客满意度评价工具.     

An examination of the determinants of customer loyalty in mobile commerce contexts

While the importance of customer loyalty has been recognized in marketing literature for at least three decades, the development and empirical validation of a customer loyalty model in a mobile commerce (m-commerce) context had not been addressed. The purpose of our study was to develop and validate such a customer loyalty model. Based on IS and marketing literature, a comprehensive set of constructs and hypotheses were compiled with a methodology for testing them. A questionnaire was constructed and data were collected from 255 users of m-commerce systems in Taiwan. Structural modeling techniques were then applied to analyze the data. The results indicated that customer loyalty was affected by perceived value, trust, habit, and customer satisfaction, with customer satisfaction playing a crucial intervening role in the relationship of perceived value and trust to loyalty. Based on the findings, its implications and limitations are discussed.     

贝叶斯决策树在客户流失预测中的应用

针对电信企业客户流失问题,提出采用贝叶斯决策树算法的预测模型,将贝叶斯分类的先验信息方法与决策树分类的信息熵增益方法相结合,应用到电信行业客户流失分析中,分别将移动公司的客户数据以及UCI数据纳入到模型中得出相应的结果。加入贝叶斯节点弥补决策树不能处理缺失值以及二义性数据的缺点。检验结果表明,基于贝叶斯推理的决策树算法在牺牲了较小的训练时间与分类时间的情况下,得到了比仅基于决策树算法更高的覆盖率与命中率。     

Are customer satisfaction and customer loyalty drivers of customer lifetime value in mobile data services: a comparative cross-country study

Many research papers have been published on the effect of customer satisfaction and customer loyalty on customer profitability which is related to customer lifetime value (CLV). However, there is limited research on the impact of cross-cultural factors on the effect of customer satisfaction and customer loyalty on CLV. This study aims to fill this gap. Focusing on the usage of mobile data services, 846 samples from China and 689 from the US are obtained. Data analysis suggests that customer loyalty is a driver of CLV, while customer satisfaction is not. This research has important implications for firms about how to enhance CLV in mobile data services.     

Facilitating cross-selling in a mobile telecom market to develop customer classification model based on hybrid data mining techniques

As the competition between mobile telecom operators becomes severe, it becomes critical for operators to diversify their business areas. Especially, the mobile operators are turning from traditional voice communication to mobile value-added services (VAS), which are new services to generate more average revenue per user (ARPU). That is, cross-selling is critical for mobile telecom operators to expand their revenues and profits. In this study, we propose a customer classification model, which may be used for facilitating cross-selling in a mobile telecom market. Our model uses the cumulated data on the existing customers including their demographic data and the patterns for using old products or services to find new products and services with high sales potential. The various data mining techniques are applied to our proposed model in two steps. In the first step, several classification techniques such as logistic regression, artificial neural networks, and decision trees are applied independently to predict the purchase of new products, and each model produces the results of their prediction as a form of probabilities. In the second step, our model compromises all these probabilities by using genetic algorithm (GA), and makes the final decision for a target customer whether he or she would purchase a new product. To validate the usefulness of our model, we applied it to a real-world mobile telecom company’s case in Korea. As a result, we found that our model produced high-quality information for cross-selling, and that GA in the second step contributed to significantly improve the performance.