Improved churn prediction in telecommunication industry by analyzing a large network

Customer retention in telecommunication companies is one of the most important issues in customer relationship management, and customer churn prediction is a major instrument in customer retention. Churn prediction aims at identifying potential churning customers. Traditional approaches for determining potential churning customers are based only on customer personal information without considering the relationship among customers. However, the subscribers of telecommunication companies are connected with other customers, and network properties among people may affect the churn. For this reason, we proposed a new procedure of the churn prediction by examining the communication patterns among subscribers and considering a propagation process in a network based on call detail records which transfers churning information from churners to non-churners. A fast and effective propagation process is possible through community detection and through setting the initial energy of churners (the amount of information transferred) differently in churn date or centrality. The proposed procedure was evaluated based on the performance of the prediction model trained with a social network feature and traditional personal features.     

Combined rough set theory and flow network graph to predict customer churn in credit card accounts

Customer churn has become a critical issue, especially in the competitive and mature credit card industry. From an economic and risk management perspective, it is important to understand customer characteristics in order to retain customers and differentiate high-quality credit customers from bad ones. However, studies have not yet adequately introduced rules based on customer characteristics and churn forms of original data. This study uses rough set theory, a rule-based decision-making technique, to extract rules related to customer churn; then uses a flow network graph, a path-dependent approach, to infer decision rules and variables; and finally presents the relationships between rules and different kinds of churn. An empirical case of credit card customer churn is also illustrated. In this study, we collect 21,000 customer samples, equally divided into three classes: survival, voluntary churn and involuntary churn. The data from these samples includes demographic, psychographic and transactional variables for analyzing and segmenting customer characteristics. The results show that this combined model can fully predict customer churn and provide useful information for decision-makers in devising marketing strategy.     

Predicting customer behavior in telecommunications

The wireless service subscriber calls a customer service representative to complain about dropped calls. During the conversation with the customer, the CSR views a display that shows this customer's probability of churn-switching from this service provider to another-as well as the most probable reasons to churn and the best strategy to retain this customer. The CSR then quickly responds to the subscriber according to the system's recommendation. This is an intelligent customer-care system designed to predict customer behavior. Predicting customer churn is a component in the decision framework for retaining customers and maximizing profitability. Companies can use these probability and revenue estimates in a decision-theoretic framework to determine a churn intervention strategy and a profitability optimization strategy. Predicting customer behavior helps service providers build customer loyalty and maximize profitability. For the success of a project, data preparation is often a critical part of the predictive algorithm.     

Social network analytics for churn prediction in telco: Model building,evaluation and network architecture

Social network analytics methods are being used in the telecommunication industry to predict customer churn with great success. In particular it has been shown that relational learners adapted to this specific problem enhance the performance of predictive models. In the current study we benchmark different strategies for constructing a relational learner by applying them to a total of eight distinct call-detail record datasets, originating from telecommunication organizations across the world. We statistically evaluate the effect of relational classifiers and collective inference methods on the predictive power of relational learners, as well as the performance of models where relational learners are combined with traditional methods of predicting customer churn in the telecommunication industry. Finally we investigate the effect of network construction on model performance; our findings imply that the definition of edges and weights in the network does have an impact on the results of the predictive models. As a result of the study, the best configuration is a non-relational learner enriched with network variables, without collective inference, using binary weights and undirected networks. In addition, we provide guidelines on how to apply social networks analytics for churn prediction in the telecommunication industry in an optimal way, ranging from network architecture to model building and evaluation.     

服务号码捆绑特征在离网预测系统中的应用

用户流失问题是电信运营商面临的亟待解决的问题,针对不同的场景,业界研究开发了多个用户离网预测系统。服务号码捆绑指用户在使用运营商服务期间,与银行、电商、便利店等第三方服务提供商通过绑定手机号产生联系。通过研究发现用户在服务存续期间普遍会绑定多种第三方服务提供商,这些商家会不定时给用户推送短信,当用户即将流失时,多数用户会逐渐取消这类服务的绑定。因此,服务号码捆绑特征对于离网用户的甄别起到了重要的作用。采用随机森林算法构建离网预测模型,利用逻辑回归算法对服务号码捆绑特征进行降维,并加入模型,进行离网用户分析,从而辅助决策者制订相应的客户维挽策略,降低客户离网率。实验结果表明,服务号码软捆绑特征能够提高系统的分析预测能力。     

基于贝叶斯网络的电信客户流失预测分析

电信客户流失分析常用的数据挖掘方法有自动聚类、决策树和人工神经网络,它们是采用数据本身来训练模型的,没有利用先验知识。电信客户流失是由客户心理、服务质量和对手竞争等诸多复杂的因素造成的,利用这些已有的先验知识,可以提高预测的精度。该文根据先验知识选取分析变量,采集样本数据,通过贝叶斯网络的结构学习和参数学习,建立客户流失模型并进行客户流失趋势预测,取得了比标准数据集更准确的结果,该结果和决策树方法的预测结果相比还具有较大的优势,说明贝叶斯网络是分析客户流失等不确定性问题的有效工具。     

Particle classification optimization-based BP network for telecommunication customer churn prediction

Customer churn prediction is critical for telecommunication companies to retain users and provide customized services. In this paper, a particle classification optimization-based BP network for telecommunication customer churn prediction (PBCCP) algorithm is proposed, which iteratively executes the particle classification optimization (PCO) and the particle fitness calculation (PFC). PCO classifies the particles into three categories according to their fitness values, and updates the velocity of different category particles using distinct equations. PFC calculates the fitness value of a particle in each forward training process of a BP neural network. PBCCP optimizes the initial weights and thresholds of the BP neural network, and brings remarkable improvement on customer churn prediction accuracy.

     

A credit assessment mechanism for wireless telecommunication debt collection: an empirical study

The wireless telecommunication market in Taiwan is now saturated. As the competition between wireless service carriers intensifies, retaining customers becomes more difficult. To prevent companies from increasing bad debt and experiencing customer churn, this study aims to apply data mining method to build a credit assessment mechanism that can effectively evaluate customer credit risks and help wireless service carriers to enhance the quality of debt collection processes by customizing collection strategies for various customer groups. The application of the proposed mechanism to related problems in a wireless telecommunication company in Taiwan has shown satisfactory effectiveness, accounting for a savings of $2 million of a total $500 million annual revenue. A mere 0.4 % savings is significant, given that wireless service carriers in Taiwan typically allocate 2–4 % of their revenue to uncollectible debts.     

C4.5算法在保险客户流失分析中的应用

保持客户和吸引客户是保险公司提高竞争力的关键,目前保险公司对客户流失的分析是粗略的或根据经验来判断。论文利用面向属性归纳和决策树C4.5算法对保险客户基本信息进行分析,找出客户流失的特征,帮助保险公司有针对性地改善客户关系。     

电子商务客户流失的DBN预测模型研究

在电子商务迅速发展,企业快速抢占市场的背景下,客户成为企业竞争的核心因素。现有相关研究多致力于采用全数据输入模式解析客户流失现象,不同类型客户造成的差异性还有待进一步探讨。鉴于传统RFM模型不能精确解释电子商务客户流失原因,该研究将客户分为活跃与非活跃两个集群,提出一种优化的RFM理论模型与深度信念网络实证模型对电子商务客户流失进行预测。结果表明,不同类型客户流失因素的影响强度不同。对活跃用户而言,客户购买总金额是影响客户流失的主要因素;对非活跃用户而言,客户进入店铺的时间越长越可能留住客户。通过剖析非活跃用户不流失和活跃用户流失的原因,可帮助企业制定有效的客户管理策略,以最大程度地吸引潜在客户及保留现有客户,获取最多的市场利益。     

数据挖掘方法在用户流失预测分析中的应用

移动通信在高速发展的同时,出现了大量用户离网的现象,基于客户信息、消费行为等历史数据,进行客户离网预测分析成为各个运营商普遍关注的问题。文章基于客户的历史数据和短期偶发数据,提出了链型数据挖掘方法,并结合决策树,形成了一个综合的链型树分类器(Chain Tree Classifier,CTC)和用户行为预测模型,实验结果显示,该分类器对移动通信运营商感兴趣的单个事件发生具有良好的预测能力,可被应用到客户离网预测中,从而帮助运营商提前发现具有离网倾向的用户,进而获得更高的利润。     

CRM at a pay-TV company: Using analytical models to reduce customer attrition by targeted marketing for subscription services

The early detection of potential churners enables companies to target these customers using specific retention actions, and subsequently increase profits. This analytical CRM (Customer Relationship Management) approach is illustrated using real-life data of a European pay-TV company. Their very high churn rate has had a devastating effect on their customer base. This paper first develops different churn-prediction models: the introduction of Markov chains in churn prediction, and a random forest model are benchmarked to a basic logistic model.The most appropriate model is subsequently used to target those customers with a high churn probability in a field experiment. Three alternative courses of marketing action are applied: giving free incentives, organizing special customer events, obtaining feedback on customer satisfaction through questionnaires. The results of this field experiment show that profits can be doubled using our churn-prediction model. Moreover, profits vary enormously with respect to the selected retention action, indicating that a customer satisfaction questionnaire yields the best results, a phenomenon known in the psychological literature as the ‘mere-measurement effect’.     

Customer loyalty programs to sustain consumer fidelity in mobile telecommunication market

Companies realized the importance of well-managing their relationships with their customers. Customer Relationship Management (CRM) allows companies to manage their marketing strategies and deliver specific services to clients with different values. The mobile telecommunication market is a very competitive market where the customers are tended to move from one company to another easily. Mobile telecommunication companies should carry on specific programs and services to their customers in order to keep them satisfied and thus ensure their fidelity with the company. In this article our objective is to provide companies a model that facilitates to decide what kind of customer loyalty programs they should address to their clients from different segments. In order to do that we present a fuzzy based Hungarian method that allow assigning different loyalty programs to customers with different characteristics.     

Churn prediction in telecom using Random Forest and PSO based data balancing in combination with various feature selection strategies

The telecommunication industry faces fierce competition to retain customers, and therefore requires an efficient churn prediction model to monitor the customer’s churn. Enormous size, high dimensionality and imbalanced nature of telecommunication datasets are main hurdles in attaining the desired performance for churn prediction. In this study, we investigate the significance of a Particle Swarm Optimization (PSO) based undersampling method to handle the imbalance data distribution in collaboration with different feature reduction techniques such as Principle Component Analysis (PCA), Fisher’s ratio, F-score and Minimum Redundancy and Maximum Relevance (mRMR). Whereas Random Forest (RF) and K Nearest Neighbour (KNN) classifiers are employed to evaluate the performance on optimally sampled and reduced features dataset. Prediction performance is evaluated using sensitivity, specificity and Area under the curve (AUC) based measures. Finally, it is observed through simulations that our proposed approach based on PSO, mRMR, and RF termed as Chr-PmRF, performs quite well for predicting churners and therefore can be beneficial for highly competitive telecommunication industry.     

A recommender system to avoid customer churn: A case study

A major concern for modern enterprises is to promote customer value, loyalty and contribution through services such as can help establish a long-term, honest relationship with customers. For purposes of better customer relationship management, data mining technology is commonly used to analyze large quantities of data about customer bargains, purchase preferences, customer churn, etc. This paper aims to propose a recommender system for wireless network companies to understand and avoid customer churn. To ensure the accuracy of the analysis, we use the decision tree algorithm to analyze data of over 60,000 transactions and of more than 4000 members, over a period of three months. The data of the first nine weeks is used as the training data, and that of the last month as the testing data. The results of the experiment are found to be very useful for making strategy recommendations to avoid customer churn.     

基于信息融合的电信客户流失预测研究

针对数据挖掘方法在电信客户流失预测中的局限性,提出将信息融合与数据挖掘相结合,分别从数据层、特征层、决策层构建客户流失预测模型。确定客户流失预测指标;根据客户样本在特征空间分布的差异性对客户进行划分,得到不同特征的客户群;不同客户群采用不同算法构建客户流失预测模型,再通过人工蚁群算法求得模型融合权重,将各模型的预测结果加权得到预测最终结果。实验结果表明,基于信息融合的客户流失预测模型确实比传统模型更优。     

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。     

基于谱回归特征降维的客户流失预测

针对于大样本数据的客户流失预测,从特征有效表达的角度,提出了一种基于谱回归特征约简的预测模型.模型在原始客户特征基础上,利用基于谱回归的流形降维,建立可区分性的低维特征空间,在此之上采用支持向量机实现客户流失的二分类.通过在网络客户和传统电信客户两种不同数据集上的大样本实验,并与不同分类器、不同特征约简或选择方法的对比,证明了该方法的有效性.     

Churn prediction and social neighbour influences for different types of user groups in virtual worlds

In virtual worlds (VWs), users have more VW games alternatives, whereas VW companies consequently suffer from high customer turnover rate and low customer loyalty. Therefore, building a churn prediction model to facilitate subsequent churn management and customer retention is important. The churn behaviours and the impact of social neighbour influences to customer churn may be different for different types of users. Accordingly, we segment users into stable, unstable, and solitary user groups according to their social contact behaviours in VWs. Novel segmentation‐based churn prediction approaches are proposed for churn prediction in VWs by building prediction models for each type of user groups and considering the effect of social neighbour influences for different user groups. The proposed approaches are evaluated by conducting experiments with a dataset collected from a VW platform. The experimental results show different churn prediction performances under different user groups. The segmentation‐based churn prediction approaches perform better than do general approaches without considering user groups. Moreover, the results also reveal that social neighbour influences have a positive impact on stable and unstable users. The proposed work contributes to investigating the social neighbour influences on churn prediction for different types of user groups in VWs.