CHAMP: A Prototype for Automated Cellular Churn Prediction

We describe CHAMP (CHurn Analysis, Modeling, and Prediction), an automated system for modeling cellular customer behavior on a large scale. Using historical data from GTE's data warehouse for cellular phone customers, every month CHAMP identifies churn factors for several geographic regions and updates models to generate churn scores predicting who is likely to churn within the near future. CHAMP is capable of developing customized monthly models and churn scores for over one hundred GTE cellular phone markets totaling over 5 million customers.     

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。     

基于代价敏感SVM的电信客户流失预测研究*

针对客户流失数据集的非平衡性问题和错分代价的差异性问题,将代价敏感学习应用于Veropoulos提出的采用不同惩罚系数的支持向量机,建立客户流失预测模型,对实际的电信客户流失数据进行验证。通过与传统SVM、C4.5和ANN对比研究,结果显示此方法在精确度、命中率、覆盖率和提升度均有所改善,表明此方法有效地解决了数据集的非平衡性和错分代价问题,是进行客户流失预测的有效方法。     

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.     

An extended support vector machine forecasting framework for customer churn in e-commerce

In order to accurately forecast and prevent customer churn in e-commerce, a customer churn forecasting framework is established through four steps. First, customer behavior data is collected and converted into data warehouse by extract transform load (ETL). Second, the subject of data warehouse is established and some samples are extracted as train objects. Third, alternative predication algorithms are chosen to train selected samples. Finally, selected predication algorithm with extension is used to forecast other customers. For the imbalance and nonlinear of customer churn, an extended support vector machine (ESVM) is proposed by introducing parameters to tell the impact of churner, non-churner and nonlinear. Artificial neural network (ANN), decision tree, SVM and ESVM are considered as alternative predication algorithms to forecast customer churn with the innovative framework. Result shows that ESVM performs best among them in the aspect of accuracy, hit rate, coverage rate, lift coefficient and treatment time. This novel ESVM can process large scale and imbalanced data effectively based on the framework.     

Employee churn prediction

Customer churn is a notorious problem for most industries, as loss of a customer affects revenues and brand image and acquiring new customers is difficult. Reliable predictive models for customer churn could be useful in devising customer retention plans. We survey and compare some major machine learning techniques that have been used to build predictive customer churn models. Employee churn (or attrition) closely related but not identical to customer churn is similarly painful for an organization, leading to disruptions, customer dissatisfaction and time and efforts lost in finding and training replacement. We present a case study that we carried out for building and comparing predictive employee churn models. We also propose a simple value model for employees that can be used to identify how many of the churned employees were “valuable”. This work has the potential for designing better employee retention plans and improving employee satisfaction.     

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。     

Reconciling performance and interpretability in customer churn prediction using ensemble learning based on generalized additive models

To build a successful customer churn prediction model, a classification algorithm should be chosen that fulfills two requirements: strong classification performance and a high level of model interpretability. In recent literature, ensemble classifiers have demonstrated superior performance in a multitude of applications and data mining contests. However, due to an increased complexity they result in models that are often difficult to interpret. In this study, GAMensPlus, an ensemble classifier based upon generalized additive models (GAMs), in which both performance and interpretability are reconciled, is presented and evaluated in a context of churn prediction modeling. The recently proposed GAMens, based upon Bagging, the Random Subspace Method and semi-parametric GAMs as constituent classifiers, is extended to include two instruments for model interpretability: generalized feature importance scores, and bootstrap confidence bands for smoothing splines. In an experimental comparison on data sets of six real-life churn prediction projects, the competitive performance of the proposed algorithm over a set of well-known benchmark algorithms is demonstrated in terms of four evaluation metrics. Further, the ability of the technique to deliver valuable insight into the drivers of customer churn is illustrated in a case study on data from a European bank. Firstly, it is shown how the generalized feature importance scores allow the analyst to identify the relative importance of churn predictors in function of the criterion that is used to measure the quality of the model predictions. Secondly, the ability of GAMensPlus to identify nonlinear relationships between predictors and churn probabilities is demonstrated.     

Applying Bayesian Belief Network approach to customer churn analysis: A case study on the telecom industry of Turkey

In telecommunication industry, for many organizations, it is really important to take place in the market. As competition increases between companies, customer churn becomes a great issue to deal with by the telecommunication providers. For an effective churn management, companies try to retain their existing customers, instead of acquiring new ones. Previous researches focus on predicting the customers with a propensity to churn in telecommunication industry. In this study, a model is constructed by Bayesian Belief Network to identify the behaviors of customers with a propensity to churn. The data used are collected from one of the telecommunication providers in Turkey. First, as only discrete variables are used in Bayesian Belief Networks, CHAID (Chi-squared Automatic Interaction Detector) algorithm is applied to discretize continuous variables. Then, a causal map as a base of Bayesian Belief Network is brought out via the results of correlation analysis, multicollinearity test and experts’ opinions. According to the results of Bayesian Belief Network, average minutes of calls, average billing amount, the frequency of calls to people from different providers and tariff type are the most important variables that explain customer churn. At the end of the study, three different scenarios that examine the characteristics of the churners are analyzed and promotions are suggested to reduce the churn rate.     

流失预测模型的设计与实现

随着市场竞争的日益加剧,客户流失问题是电信运营商都面临并急需解决的问题。要解决这个问题,首先就要对客户进行分析和预测。本文就是介绍利用挖掘软件SPASS Modeler对电信客户进行数据探测与分析,掌握老客户的流失动向,并对流失客户的特征进行归类,为以后电信运营提供有用的数据。     

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。     

基于决策树的保险客户流失分析

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。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,能帮助保险公司有针对性地改善客户关系。     

Estimating the effect of word of mouth on churn and cross-buying in the mobile phone market with Markov logic networks

Much has been written about word of mouth and customer behavior. Telephone call detail records provide a novel way to understand the strength of the relationship between individuals. In this paper, we predict using call detail records the impact that the behavior of one customer has on another customer's decisions. We study this in the context of churn (a decision to leave a communication service provider) and cross-buying decisions based on an anonymized data set from a telecommunications provider. Call detail records are represented as a weighted graph and a novel statistical learning technique, Markov logic networks, is used in conjunction with logit models based on lagged neighborhood variables to develop the predictive model. In addition, we propose an approach to propositionalization tailored to predictive modeling with social network data. The results show that information on the churn of network neighbors has a significant positive impact on the predictive accuracy and in particular the sensitivity of churn models. The results provide evidence that word of mouth has a considerable impact on customers' churn decisions and also on the purchase decisions, leading to a 19.5% and 8.4% increase in sensitivity of predictive models.     

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.     

An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction

Several studies have demonstrated the superior performance of ensemble classification algorithms, whereby multiple member classifiers are combined into one aggregated and powerful classification model, over single models. In this paper, two rotation-based ensemble classifiers are proposed as modeling techniques for customer churn prediction. In Rotation Forests, feature extraction is applied to feature subsets in order to rotate the input data for training base classifiers, while RotBoost combines Rotation Forest with AdaBoost. In an experimental validation based on data sets from four real-life customer churn prediction projects, Rotation Forest and RotBoost are compared to a set of well-known benchmark classifiers. Moreover, variations of Rotation Forest and RotBoost are compared, implementing three alternative feature extraction algorithms: principal component analysis (PCA), independent component analysis (ICA) and sparse random projections (SRP). The performance of rotation-based ensemble classifier is found to depend upon: (i) the performance criterion used to measure classification performance, and (ii) the implemented feature extraction algorithm. In terms of accuracy, RotBoost outperforms Rotation Forest, but none of the considered variations offers a clear advantage over the benchmark algorithms. However, in terms of AUC and top-decile lift, results clearly demonstrate the competitive performance of Rotation Forests compared to the benchmark algorithms. Moreover, ICA-based Rotation Forests outperform all other considered classifiers and are therefore recommended as a well-suited alternative classification technique for the prediction of customer churn that allows for improved marketing decision making.     

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.     

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.     

基于核主成分分析特征提取的客户流失预测

将核主成分分析(KPCA)引入到客户流失预测中,提出了相应的特征提取算法。将KPCA与Logistic回归结合,设计了预测模型。通过对某电信公司客户流失预测的试验结果表明：该方法获得的命中率、覆盖率、准确率和提升系数高于原始属性集和主成分分析(PCA)特征提取法。这表明KPCA能提取客户数据的非线性特征,是研究客户流失预测问题的有效方法。     

A BI‐LEVEL DECISION MODEL FOR CUSTOMER CHURN ANALYSIS

This paper develops a bi‐level decision model and a solution approach to optimizing service features for a company to reduce its customer churn rate. First, a bi‐level decision model, together with its modeling approach, are developed to describe the gaming relationship between decision makers in a company (service provider) and its customers. Then, a practical solution approach to reaching solutions for the bi‐level‐modeled customer churn problem is developed. Finally, experiments and case studies are conducted to illustrate the bi‐level decision model and the solution approach.