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.     

改进的k-means聚类算法在供电企业CRM中的应用

针对k-means算法存在的不足,提出了一种改进算法。针对目前供电企业CRM系统的特点提出了用聚类分析方法进行客户群细分模型设计,通过实验验证了本文提出的k-means改进算法的高效性。实验结果证明聚类分析算法在CRM中实施类分析方法的客户群细分过程模型是行之有效的。     

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.

     

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.     

Toward a hybrid data mining model for customer retention

The prevention of subscriber churn through customer retention is a core issue of Customer Relationship Management (CRM). By minimizing customer churn a company maximizes its profit. This paper proposes a hybridized architecture to deal with customer retention problems. It does so not only through predicting churn probability but also by proposing retention policies. The architecture works in two modes: learning and usage.

In the learning mode, the churn model learner seeks potential associations from the subscriber database. This historical information is used to form a churn model. This mode also calls for a policy model constructor to use the attributes identified in the churn model to divide all ‘churners’ into distinct groups. The policy model constructor is also responsible for developing a policy model for each churner group. In the usage mode, a churn predictor uses the churn model to predict the churn probability of a given subscriber. When the churn model finds that the subscriber has a high churn probability the policy model is used to suggest specific retention policies.

This study’s experiments show that the churn model has an evaluation accuracy of approximately eighty-five percent. This suggests that policy model construction represents an interesting and important technique in investigating the characteristics of churner groups. Furthermore, this study indicates that understanding the relationships between churns is essential in creating effective retention policy models for dealing with ‘churners’.     

一种大数据环境下的新聚类算法

提出了一种新的聚类算法NGKCA,该算法克服了经典聚类算法检测率和稳定性的不足,适用于解决大数据环境下的聚类问题。NGKCA聚类算法包括4个阶段:首先利用谱聚类NJW算法对大数据集进行列降维和数据归一化处理,其次引入对初始值不敏感的粒子群算法对数据集进行行降维从而选出临时的聚类中心集,接着通过全局Kmeans算法对最佳聚类中心集进行聚类以获取聚类中心点,最后使用粒子群算法对聚类中心点进行调整进而获取最终的聚类划分。在一些著名的机器学习数据集和国际标准的网络安全数据集KDDCUP99上进行实验,结果表明:提出的算法比谱聚类、Kmeans、粒子群、全局Kmeans等常见算法具有更好的稳定性和更高的检测率,与全局Kmeans算法相比具有更优的时间复杂度。     

基于并行C4.5的铁路零散白货客户流失预测研究

为了提高铁路零散白货客户流失预测的准确性和高效性,根据铁路零散白货客户的流失特征,提出了基于CDL模型的客户流失识别方法,在此基础上,针对数据量大的问题,提出了基于Hadoop并行框架的C4.5决策树客户流失预测模型。通过仿真实验,证明该模型具有较好的准确性和预测能力,并且随着样本数量的增加,Hadoop并行框架的效率得到了明显的提升,且不影响客户流失预测模型的准确性和预测能力。     

Customer segmentation based on survival character

Customer Segmentation is an increasingly pressing issue in today’s over-competitive commercial area. More and more literatures have researched the application of data mining technology in customer segmentation, and achieved sound effectives. But most of them segment customer only by single data mining technology from a special view, rather than from systematical framework. Furthermore, one of the key purposes of customer segmentation is customer retention. Although previous segment methods may identify which group needs more care, it is unable to identify customer churn trend for taking different actions. This paper focus on proposing a customer segmentation framework based on data mining and constructs a new customer segmentation method based on survival character. The new customer segmentation method consists of two steps. Firstly, with K-means clustering arithmetic, customers are clustered into different segments in which customers have the similar survival characters (churn trend). Secondly, each cluster’s survival/hazard function is predicted by survival analyzing, the validity of clustering is tested and customer churn trend is identified. The method mentioned above has been applied to a dataset from China Telecom, which acquired some useful management measures and suggestions. Some propositions for further research is also suggested.     

聚类问题的自适应杂交差分演化模拟退火算法

针对K-均值聚类算法对初始值敏感和易陷入局部最优的缺点,提出了一个基于自适应杂交差分演化模拟退火的K-均值聚类算法。该算法以差分演化算法为基础,通过模拟退火算法的更新策略来增强全局搜索能力,并运用自适应技术来选择学习策略、确定算法的关键参数。实验结果表明,该算法能较好地克服传统K-均值聚类算法的缺点,具有较好的全局收敛能力,且算法稳定性强、收敛速度快,将新算法与传统的K-均值聚类算法以及最近提出的几个同类聚类算法进行了比较。     

基于决策树的证券客户流失模型

描述了证券业客户流失分析的重要性,客户流失的定义,提出了影响客户流失的各种特征因素.然后根据CRM中的RFM模型,加入客户收益率指标,提出了证券行业客户流失分析的RFM-ROI模型.用决策树方法构建了客户流失分析模型,并提出了解决决策树剪枝问题的停止阈值方法.结果表明该模型能达到80.7%的准确率,具有较强的实用性.     

一种基于改进k-means的RBF神经网络学习方法

针对传统RBF神经网络学习算法构造的网络分类精度不高,传统的k-means算法对初始聚类中心的敏感,聚类结果随不同的初始输入而波动。为了解决以上问题,提出一种基于改进k-means的RBF神经网络学习算法。先用减聚类算法优化k-means算法,消除聚类的敏感性,再用优化后的k-means算法构造RBF神经网络。仿真结果表明了该学习算法的实用性和有效性。     

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.     

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.     

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。     

Calling communities analysis and identification using machine learning techniques

The analysis of social communities related logs has recently received considerable attention for its importance in shedding light on social concerns by identifying different groups, and hence helps in resolving issues like predicting terrorist groups. In the customer analysis domain, identifying calling communities can be used for determining a particular customer’s value according to the general pattern behavior of the community that the customer belongs to; this helps the effective targeted marketing design, which is significantly important for increasing profitability. In telecommunication industry, machine learning techniques have been applied to the Call Detail Record (CDR) for predicting customer behavior such as churn prediction. In this paper, we pursue identifying the calling communities and demonstrate how cluster analysis can be used to effectively identify communities using information derived from the CDR data. We use the information extracted from the cluster analysis to identify customer calling patterns. Customers calling patterns are then given to a classification algorithm to generate a classifier model for predicting the calling communities of a customer. We apply different machine learning techniques to build classifier models and compare them in terms of classification accuracy and computational performance. The reported test results demonstrate the applicability and effectiveness of the proposed approach.     

基于AP聚类与随机森林的客户流失预测研究

利率市场化、大数据迅速发展,银行业均表现出明显的"二八定律"现象,20％的优质客户占据了银行的大部分资产.那么,如何防止银行客户流失,尤其是优质客户的流失,已经成为银行越来越关注的问题.因此,建立优质客户流失预警模型就显得尤为重要.以某商业银行为例,重新对客户流失进行定义,重点关注银行优质客户的流失预警,首先使用AP聚...     

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.     

Improving customer attrition prediction by integrating emotions from client/company interaction emails and evaluating multiple classifiers

Predicting customer churn with the purpose of retaining customers is a hot topic in academy as well as in today’s business environment. Targeting the right customers for a specific retention campaign carries a high priority. This study focuses on two aspects in which churn prediction models could be improved by (i) relying on customer information type diversity and (ii) choosing the best performing classification technique. (i) With the upcoming interest in new media (e.g. blogs, emails, ...), client/company interactions are facilitated. Consequently, new types of information are available which generate new opportunities to increase the prediction power of a churn model. This study contributes to the literature by finding evidence that adding emotions expressed in client/company emails increases the predictive performance of an extended RFM churn model. As a substantive contribution, an in-depth study of the impact of the emotionality indicators on churn behavior is done. (ii) This study compares three classification techniques – i.e. Logistic Regression, Support Vector Machines and Random Forests – to distinguish churners from non-churners. This paper shows that Random Forests is a viable opportunity to improve predictive performance compared to Support Vector Machines and Logistic Regression which both exhibit an equal performance.     

客服专员业务能力体系与培训规划研究

为了解决客服专员业务能力不一、培训工作复杂难度高的问题,通过对超过4000名客服专员、涉及超过300类业务需求的业务行为、个人特征、质检工单等近2年数据的初步处理与分析,从超过亿条工单数据中统计出21个业务能力评价维度,建立决策树与神经网络混合模型,以决策树模型筛选出的业务能力影响因子应用于 LMBP 神经网络预测模型得出较准确的业务能力评价客服专员分群。为了规划客服专员培训体系,通过模型的分群结果分析,对不擅长处理某业务的客服专员针对最细业务划分进行定制化培训,准确高效提升客服专员业务短板能力。同时反馈培训结果到模型分析,完善业务能力评价维度,优化分群结果。与客服中心平均业务培训时间对比,应用此分类结果进行定制化培训可以有效节约工作时间,且培训业务范围更广。