text:
title = {Profit optimizing customer churn prediction with Bayesian network classifiers},
  abstract = {Customer churn prediction is becoming an increasingly important business analytics problem for telecom operators. In order to increase the efficiency of customer retention campaigns, churn prediction models need to be accurate as well as compact and interpretable. Although a myriad of techniques for churn prediction has been examined, there has been little attention for the use of Bayesian Network classifiers. This paper investigates the predictive power of a number of Bayesian Network algorithms, ranging from the Naive Bayes classifier to General Bayesian Network classifiers. Furthermore, a feature selection method based on the concept of the Markov Blanket, which is genuinely related to Bayesian Networks, is tested. The performance of the classifiers is evaluated with both the Area under the Receiver Operating Characteristic Curve and the recently introduced Maximum Profit criterion. The Maximum Profit criterion performs an intelligent optimization by targeting this fraction of the customer base which would maximize the profit generated by a retention campaign. The results of the experiments are rigorously tested and indicate that most of the analyzed techniques have a comparable performance. Some methods, however, are more preferred since they lead to compact networks, which enhances the interpretability and comprehensibility of the churn prediction models. © 2014-IOS Press.},
  doi = {10.3233/IDA-130625},
  journal = {Intelligent Data Analysis},
  bibtexkey = {Verbraken20143},
  author = {Verbraken, T., Verbeke, W., Baesens, B.},
  source = {SCOPUS}
}

@{,
  category = {INPROCEEDINGS},
  year = {2014},
  title = {Applicability of machine-learning techniques in predicting customer defection},
  abstract = {Machine learning is an established method of predicting customer defection from a contractual business. However, no systematic comparison or evaluation of the different machine-learning techniques has been performed. In this study, we provide a comprehensive comparison of different machine-learning techniques with three different data sets of a software company to predict customer defection. The evaluation criteria of the techniques are understandability of the model, convenience of using the model, time efficiency in running the learning model, and performance of predicting customer defection. © 2014 IEEE.},
  doi = {10.1109/ISTMET.2014.6936498},
  journal = {ISTMET 2014 - 1st International Symposium on Technology Management and Emerging Technologies, Proceedings},
  bibtexkey = {Prasasti2014157},
  author = {Prasasti, N., Ohwada, H.},
  source = {SCOPUS}
}

@{,
  category = {INPROCEEDINGS},
  year = {2014},
  title = {Research on customers churn prediction model based on logistic},
  abstract = {At present, the competition is increasingly fierce between the securities company, whether can effectively prevent the loss of users, reducing loss rate is a difficult problem at present each securities company urgently needs to solve. The model based on the principle of data mining, proposes a prediction method based on Logistic regression algorithm. Prediction model is built based on Logistic regression algorithm and the validity and accuracy of the model is verified by experiment, provides a new method and thinking for the securities company customer churn prediction. © (2014) Trans Tech Publications, Switzerland.},
  doi = {10.4028/www.scientific.net/AMR.989-994.1517},
  journal = {Advanced Materials Research},
  bibtexkey = {Jiang20141517},
  author = {Jiang, M., Chu, N., Bi, X.M.},
  source = {SCOPUS}
}

@{,
  category = {INPROCEEDINGS},
  year = {2014},
  title = {Prediction of customer attrition of commercial banks based on SVM model},
  abstract = {Currently, Chinese commercial banks are facing triple tremendous pressure, including financial disintermediation, interest rate marketization and Internet finance. Meanwhile, increasing financial consumption demand of customers further intensifies the competition among commercial banks. To increase their profits for continuing operations and enhance the core competitiveness, commercial banks must avoid the loss of customers while acquiring new customers. This paper discusses commercial bank customer churn prediction based on SVM model, and uses random sampling method to improve SVM model, considering the imbalance characteristics of customer data sets. The results show that this method can effectively enhance the prediction accuracy of the selected model. © 2014 Published by Elsevier B.V.},
  doi = {10.1016/j.procs.2014.05.286},
  journal = {Procedia Computer Science},
  bibtexkey = {He2014423},
  author = {He, B., Shi, Y., Wan, Q., Zhao, X.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2014},
  title = {Social network analysis for customer churn prediction},
  abstract = {This study examines the use of social network information for customer churn prediction. An alternative modeling approach using relational learning algorithms is developed to incorporate social network effects within a customer churn prediction setting, in order to handle large scale networks, a time dependent class label, and a skewed class distribution. An innovative approach to incorporate non-Markovian network effects within relational classifiers and a novel parallel modeling setup to combine a relational and non-relational classification model are introduced. The results of two real life case studies on large scale telco data sets are presented, containing both networked (call detail records) and non-networked (customer related) information about millions of subscribers. A significant impact of social network effects, including non-Markovian effects, on the performance of a customer churn prediction model is found, and the parallel model setup is shown to boost the profits generated by a retention campaign. © 2013 Elsevier B.V. All rights reserved.},
  doi = {10.1016/j.asoc.2013.09.017},
  journal = {Applied Soft Computing Journal},
  bibtexkey = {Verbeke2014431},
  author = {Verbeke, W., Martens, D., Baesens, B.},
  source = {SCOPUS}
}

@{,
  category = {INPROCEEDINGS},
  year = {2013},
  title = {Providing a customer churn prediction model Using Random Forest technique},
  abstract = {In order to succeed in the global competition, organizations need to understand and monitor customers' behavior, so that they could retain them by predicting their preference and behavior before others. Recently, marketing strategies have been changed from product-oriented strategies to customer-oriented strategies and most organizations have focused on customer relationship management. In fact, more organizations have found out that retention of their present customers, as their most valuable asset, is very important. Therefore, with the aim of describing data mining abilities in churn management, and designing and implementation of a customer churn prediction model using a standard CRISP-DM (Cross Industry Standard Process for Data Mining) methodology based on RFM (Recency, Frequency, Monetary) and random forest technique, the database of one of the biggest holdings of the country, Solico food industries group, is explored. Using this model, the customers tending to turn over are identified and effective marketing strategies will be planned for this group. Customer behavior analysis indicates that length of relationship, the relative frequency and the average inter purchase time are among the best predictors. © 2013 IEEE.},
  doi = {10.1109/IKT.2013.6620065},
  journal = {IKT 2013 - 2013 5th Conference on Information and Knowledge Technology},
  bibtexkey = {Nabavi2013202},
  author = {Nabavi, S., Jafari, S.},
  source = {SCOPUS}
}

@{,
  category = {INPROCEEDINGS},
  year = {2013},
  title = {Preventing churn in telecommunications: The forgotten network},
  abstract = {This paper outlines an approach developed as a part of a company-wide churn management initiative of a major European telecom operator. We are focusing on explanatory churn model for the postpaid segment, assuming that the mobile telecom network, the key resource of operators, is also a churn driver in case it under delivers to customers' expectations. Typically, insights generated by churn models are deployed in marketing campaigns; our model's insights are used in network optimization in order to remove the key network related churn drivers and therefore prevent churn, rather than cure it. The insights generated by the model have caused a paradigm shift in managing the network with the operator where the research was conducted. © 2013 Springer-Verlag.},
  doi = {10.1007/978-3-642-41398-8_31},
  journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
  bibtexkey = {Radosavljevik2013357},
  author = {Radosavljevik, D., Van Der Putten, P.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2013},
  title = {Customer churn prediction in the online gambling industry: The beneficial effect of ensemble learning},
  abstract = {The online gambling industry is one of the most revenue generating branches of the entertainment business, resulting in fierce competition and saturated markets. Therefore it is essential to efficiently retain gamblers. Churn prediction is a promising new alternative in customer relationship management (CRM) to analyze customer retention. It is the process of identifying gamblers with a high probability to leave the company based on their past behavior. This study investigates whether churn prediction is a valuable option in the CRM palette of the online gambling companies. Using real-life data of poker players at bwin, single algorithms, CART decision trees and generalized additive models are benchmarked to their ensemble counterparts, random forests and GAMens. The results show that churn prediction is a valuable strategy to identify and profile those customers at risk. Furthermore, the performance of the ensembles is more robust and better than the single models. © 2012 Elsevier Inc.},
  doi = {10.1016/j.jbusres.2012.12.008},
  journal = {Journal of Business Research},
  bibtexkey = {Coussement20131629},
  author = {Coussement, K., De Bock, K.W.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2013},
  title = {Exploring a local linear model tree approach to car-following},
  abstract = {Because car-following (CF) models are fundamental to replicating traffic flow they have received considerable attention over the last 50 years. They are in a continuous state of improvement due to their significant role in traffic microsimulations, intelligent transportation systems, and safety engineering models. This article uses the local linear model tree (LOLIMOT) approach to model driver's CF behavior to incorporate human perceptual imperfections into a CF model. This model defines some localities in the input space. These localities are fuzzy and have overlaps with each other. Specific models for each of the localities are then defined and combined in a fuzzy manner to predict the final output. The model was developed using real world dynamic data sets. Three different data sets were used for training, testing, and validating the model. The performance of the model was compared to a number of existing CF models. The results showed very close agreement between the real data and the LOLIMOT outputs. © 2013 Computer-Aided Civil and Infrastructure Engineering.},
  doi = {10.1111/mice.12011},
  journal = {Computer-Aided Civil and Infrastructure Engineering},
  bibtexkey = {Aghabayk2013581},
  author = {Aghabayk, K., Forouzideh, N., Young, W.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2013},
  title = {Customer attrition in retailing: An application of Multivariate Adaptive Regression Splines},
  abstract = {The profit resulting from customer relationship is essential to ensure companies viability, so an improvement in customer retention is crucial for competitiveness. As such, companies have recognized the importance of customer centered strategies and consequently customer relationship management (CRM) is often at the core of their strategic plans. In this context, a priori knowledge about the risk of a given customer to mitigate or even end the relationship with the provider is valuable information that allows companies to take preventive measures to avoid defection. This paper proposes a model to predict partial defection, using two classification techniques: Logistic regression and Multivariate Adaptive Regression Splines (MARS). The main objective is to compare the performance of MARS with Logistic regression in modeling customer attrition. This paper considers the general form of Logistic regression and Logistic regression combined with a wrapper feature selection approach, such as stepwise approach. The empirical results showed that MARS performs better than Logistic regression when variable selection procedures are not used. However, MARS loses its superiority when Logistic regression is conducted with stepwise feature selection. © 2013 Elsevier B.V. All rights reserved.},
  doi = {10.1016/j.eswa.2013.05.069},
  journal = {Expert Systems with Applications},
  bibtexkey = {MiguÃ©is20136225},
  author = {MiguÃ©is, V.L., Camanho, A., FalcÃ£o E Cunha, J.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2013},
  title = {Kernel Factory: An ensemble of kernel machines},
  abstract = {We propose an ensemble method for kernel machines. The training data is randomly split into a number of mutually exclusive partitions defined by a row and column parameter. Each partition forms an input space and is transformed by an automatically selected kernel function into a kernel matrix K. Subsequently, each K is used as training data for a base binary classifier (Random Forest). This results in a number of predictions equal to the number of partitions. A weighted average combines the predictions into one final prediction. To optimize the weights, a genetic algorithm is used. This approach has the advantage of simultaneously promoting (1) diversity, (2) accuracy, and (3) computational speed. (1) Diversity is fostered because the individual K's are based on a subset of features and observations, (2) accuracy is sought by automatic kernel selection and the genetic algorithm, and (3) computational speed is obtained because the computation of each K can be parallelized. Using five times twofold cross validation we benchmark the classification performance of Kernel Factory against Random Forest and Kernel-Induced Random Forest (KIRF). We find that Kernel Factory has significantly better performance than Kernel-Induced Random Forest. When the right kernel is selected Kernel Factory is also significantly better than Random Forest. In addition, an open-source R-software package of the algorithm (kernelFactory) is available from CRAN. © 2013 Elsevier B.V. All rights reserved.},
  doi = {10.1016/j.eswa.2012.12.007},
  journal = {Expert Systems with Applications},
  bibtexkey = {Ballings20132904},
  author = {Ballings, M., Van Den Poel, D.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2013},
  title = {Churn management optimization with controllable marketing variables and associated management costs},
  abstract = {In this paper, we propose a churn management model based on a partial least square (PLS) optimization method that explicitly considers the management costs of controllable marketing variables for a successful churn management program. A PLS prediction model is first calibrated to estimate the churn probabilities of customers. Then this PLS prediction model is transformed into a control model after relative management costs of controllable marketing variables are estimated through a triangulation method. Finally, a PLS optimization model with marketing objectives and constraints are specified and solved via a sequential quadratic programming method. In our experiments, we observe that while the training and test data sets are dramatically different in terms of churner distributions (50% vs. 1.8%), four controllable variables in three marketing strategies significantly changed through optimization process while other variables only marginally changed. We also observe that the most significant variable in a PLS prediction model does not necessarily change most significantly in our PLS optimization model due to the highest management cost associated, implying differences between a prediction and an optimization model. Finally, two marketing models designed for targeting the subsets of customers based on churn probability or management costs are presented and discussed. © 2012 Elsevier Ltd. All rights reserved.},
  doi = {10.1016/j.eswa.2012.10.043},
  journal = {Expert Systems with Applications},
  bibtexkey = {Kim20132198},
  author = {Kim, Y.S., Lee, H., Johnson, J.D.},
  source = {SCOPUS}
}

@{,
  category = {INPROCEEDINGS},
  year = {2013},
  title = {Classification of emotional speech units in call centre interactions},
  abstract = {Detecting emotional traits in call centre interactions can be beneficial to the quality management of the services provided, since this reveals the positioning of both speakers, i.e. satisfaction or frustration and anger on the customers' side, and stress detection, disappointment mitigation or failure to provide the requested service on the operators' side. This paper describes a machine learning approach to classify emotional speech units occurring in a call centre dataset by employing emotion-related labels, automatically extracted acoustic features as well as additional context-related features. © 2013 IEEE.},
  doi = {10.1109/CogInfoCom.2013.6719279},
  journal = {4th IEEE International Conference on Cognitive Infocommunications, CogInfoCom 2013 - Proceedings},
  bibtexkey = {Galanis2013403},
  author = {Galanis, D., Karabetsos, S., Koutsombogera, M., Papageorgiou, H., Esposito, A., Riviello, M.-T.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2013},
  title = {A credit assessment mechanism for wireless telecommunication debt collection: An empirical study},
  abstract = {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. © 2012 Springer-Verlag.},
  doi = {10.1007/s10257-012-0192-x},
  journal = {Information Systems and e-Business Management},
  bibtexkey = {Wang2013357},
  author = {Wang, H.-Y., Liao, C., Kao, C.-H.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2012},
  title = {Customer event history for churn prediction: How long is long enough?},
  abstract = {The key question of this study is: How long should customer event history be for customer churn prediction? While most studies in predictive churn modeling aim to improve models by data augmentation or algorithm improvement, this study focuses on a another dimension: time window optimization with respect to predictive performance. This paper first presents a formalization of the time window selection strategy, along with a literature review. Next, using logistic regression, classification trees and bagging in combination with classification trees, this study analyzes the improvement in churn-model performance by extending customer event history from one to sixteen years. The results show that, after the fifth additional year, predictive performance is only marginally increased, meaning that the company in this study can discard 69% of its data with almost no decrease in predictive performance. The practical implication is that analysts can substantially decrease data-related burdens, such as data storage, preparation and analysis. This is particularly valuable in times of big data when decreasing computational complexity is paramount. © 2012 Elsevier Ltd. All rights reserved.},
  doi = {10.1016/j.eswa.2012.07.006},
  journal = {Expert Systems with Applications},
  bibtexkey = {Ballings201213517},
  author = {Ballings, M., Van Den Poel, D.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2012},
  title = {Sequential manifold learning for efficient churn prediction},
  abstract = {Nowadays, thanks to the rapid evolvement of information technology, an explosively large amount of information with very high-dimensional features for customers is being accumulated in companies. These companies, in turn, are exerting every effort to develop more efficient churn prediction models for managing customer relationships effectively. In this paper, a novel method is proposed to deal with a high-dimensional large data set for constructing better churn prediction models. The proposed method starts by partitioning a data set into small-sized data subsets, and applies sequential manifold learning to reduce high-dimensional features and give consistent results for combined data subsets. The performance of the constructed churn prediction model using the proposed method is tested using an E-commerce data set by comparing it with other existing methods. The proposed method works better and is much faster for high-dimensional large data sets without the need for retraining the original data set to reduce the dimensions of new test samples. © 2012 Elsevier Ltd. All rights reserved.},
  doi = {10.1016/j.eswa.2012.05.069},
  journal = {Expert Systems with Applications},
  bibtexkey = {Kim201213328},
  author = {Kim, K., Lee, J.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2012},
  title = {A hierarchical multiple kernel support vector machine for customer churn prediction using longitudinal behavioral data},
  abstract = {The availability of abundant data posts a challenge to integrate static customer data and longitudinal behavioral data to improve performance in customer churn prediction. Usually, longitudinal behavioral data are transformed into static data before being included in a prediction model. In this study, a framework with ensemble techniques is presented for customer churn prediction directly using longitudinal behavioral data. A novel approach called the hierarchical multiple kernel support vector machine (H-MK-SVM) is formulated. A three phase training algorithm for the H-MK-SVM is developed, implemented and tested. The H-MK-SVM constructs a classification function by estimating the coefficients of both static and longitudinal behavioral variables in the training process without transformation of the longitudinal behavioral data. The training process of the H-MK-SVM is also a feature selection and time subsequence selection process because the sparse non-zero coefficients correspond to the variables selected. Computational experiments using three real-world databases were conducted. Computational results using multiple criteria measuring performance show that the H-MK-SVM directly using longitudinal behavioral data performs better than currently available classifiers. © 2012 Elsevier B.V. All rights reserved.},
  doi = {10.1016/j.ejor.2012.06.040},
  journal = {European Journal of Operational Research},
  bibtexkey = {Chen2012461},
  author = {Chen, Z.-Y., Fan, Z.-P., Sun, M.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2012},
  title = {Improving customer retention in financial services using kinship network information},
  abstract = {This study investigates the advantage of social network mining in a customer retention context. A company that is able to identify likely churners in an early stage can take appropriate steps to prevent these potential churners from actually churning and subsequently increase profit. Academics and practitioners are constantly trying to optimize their predictive-analytics models by searching for better predictors. The aim of this study is to investigate if, in addition to the conventional sets of variables (socio-demographics, purchase history, etc.), kinship network based variables improve the predictive power of customer retention models. Results show that the predictive power of the churn model can indeed be improved by adding the social network (SNA-) based variables. Including network structure measures (i.e. degree, betweenness centrality and density) increase predictive accuracy, but contextual network based variables turn out to have the highest impact on discriminating churners from non-churners. For the majority of the latter type of network variables, the importance in the model is even higher than the individual level counterpart variable. © 2012 Elsevier Ltd. All rights reserved.},
  doi = {10.1016/j.eswa.2012.04.016},
  journal = {Expert Systems with Applications},
  bibtexkey = {Benoit201211435},
  author = {Benoit, D.F., Van Den Poel, D.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2012},
  title = {Forecasting of short-term traffic-flow based on improved neurofuzzy models via emotional temporal difference learning algorithm},
  abstract = {Bounded rationally idea, rather that optimization idea, have result and better performance in decision making theory. Bounded rationality is the idea in decision making, rationality of individuals is limited by the information they have, the cognitive limitations of their minds, and the finite amount of time they have to make decisions. The emotional theory is an important topic presented in this field. The new methods in the direction of purposeful forecasting issues, which are based on cognitive limitations, are presented in this study. The presented algorithms in this study are emphasizes to rectify the learning the peak points, to increase the forecasting accuracy, to decrease the computational time and comply the multi-object forecasting in the algorithms. The structure of the proposed algorithms is based on approximation of its current estimate according to previously learned estimates. The short term traffic flow forecasting is a real benchmark that has been studied in this area. Traffic flow is a good measure of traffic activity. The time-series data used for fitting the proposed models are obtained from a two lane street I-494 in Minnesota City, USA. The research discuss the strong points of new method based on neurofuzzy and limbic system structure such as Locally Linear Neurofuzzy network (LLNF) and Brain Emotional Learning Based Intelligent Controller (BELBIC) models against classical and other intelligent methods such as Radial Basis Function (RBF), Takagi-Sugeno (T-S) neurofuzzy, and Multi-Layer Perceptron (MLP), and the effect of noise on the performance of the models is also considered. Finally, findings confirmed the significance of structural brain modeling beyond the classical artificial neural networks. © 2011 Elsevier Ltd. All rights reserved.},
  doi = {10.1016/j.engappai.2011.09.011},
  journal = {Engineering Applications of Artificial Intelligence},
  bibtexkey = {Abdi20121022},
  author = {Abdi, J., Moshiri, B., Abdulhai, B., Sedigh, A.K.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2012},
  title = {Determinants of intangible assets value: The data mining approach},
  abstract = {It is very important for investors and creditors to understand the critical factors affecting a firm's value before making decisions about investments and loans. Since the knowledge-based economy has evolved, the method for creating firm value has transferred from traditional physical assets to intangible knowledge. Therefore, valuation of intangible assets has become a widespread topic of interest in the future of the economy. This study takes advantage of feature selection, an important data-preprocessing step in data mining, to identify important and representative factors affecting intangible assets. Particularly, five feature selection methods are considered, which include principal component analysis (PCA), stepwise regression (STEPWISE), decision trees (DT), association rules (AR), and genetic algorithms (GA). In addition, multi-layer perceptron (MLP) neural networks are used as the prediction model in order to understand which features selected from these five methods can allow the prediction model to perform best. Based on the chosen dataset containing 61 variables, the experimental result shows that combining the results from multiple feature selection methods performs the best. GA n STEPWISE, DT <U+222A> PCA, and the DT single feature selection method generate approximately 75% prediction accuracy, which select 26, 22, and 7 variables respectively. © 2012 Elsevier Ltd. All rights reserved.},
  doi = {10.1016/j.knosys.2012.02.007},
  journal = {Knowledge-Based Systems},
  bibtexkey = {Tsai201267},
  author = {Tsai, C.-F., Lu, Y.-H., Yen, D.C.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2012},
  title = {Reconciling performance and interpretability in customer churn prediction using ensemble learning based on generalized additive models},
  abstract = {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. © 2011 Elsevier Ltd. All rights reserved.},
  doi = {10.1016/j.eswa.2012.01.014},
  journal = {Expert Systems with Applications},
  bibtexkey = {DeBock20126816},
  author = {De Bock, K.W., Van Den Poel, D.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2012},
  title = {New insights into churn prediction in the telecommunication sector: A profit driven data mining approach},
  abstract = {Customer churn prediction models aim to indicate the customers with the highest propensity to attrite, allowing to improve the efficiency of customer retention campaigns and to reduce the costs associated with churn. Although cost reduction is their prime objective, churn prediction models are typically evaluated using statistically based performance measures, resulting in suboptimal model selection. Therefore, in the first part of this paper, a novel, profit centric performance measure is developed, by calculating the maximum profit that can be generated by including the optimal fraction of customers with the highest predicted probabilities to attrite in a retention campaign. The novel measure selects the optimal model and fraction of customers to include, yielding a significant increase in profits compared to statistical measures. In the second part an extensive benchmarking experiment is conducted, evaluating various classification techniques applied on eleven real-life data sets from telecom operators worldwide by using both the profit centric and statistically based performance measures. The experimental results show that a small number of variables suffices to predict churn with high accuracy, and that oversampling generally does not improve the performance significantly. Finally, a large group of classifiers is found to yield comparable performance. © 2011 Elsevier B.V. All rights reserved.},
  doi = {10.1016/j.ejor.2011.09.031},
  journal = {European Journal of Operational Research},
  bibtexkey = {Verbeke2012211},
  author = {Verbeke, W., Dejaeger, K., Martens, D., Hur, J., Baesens, B.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2012},
  title = {Analytical CRM in banking and finance using SVM: A modified active learning-based rule extraction approach},
  abstract = {This paper presents advancement to modified active learning-based approach in an eclectic framework for extracting if-then rules from support vector machine (SVM) for customer relationship management (CRM) purposes. The proposed approach comprises of three major phases: 1 feature selection using SVM-RFE (recursive feature elimination) 2 active learning for synthetic data generation 3 rule generation using decision tree (DT) and Naive Bayes tree (NBTree). Finance problems solved in this study are churn prediction in bank credit cards customers and fraud detection in insurance. Based on sensitivity measure, the empirical results suggest that the proposed modified active learning-based rule extraction approach yielded best sensitivity and, length and number of rules is reduced resulting in improved comprehensibility. Feature selection leads to the most important attributes of the customers and extracted rules serves as early warning system to the management to enforce better CRM practices and detect/avoid possible frauds. Copyright © 2012 Inderscience Enterprises Ltd.},
  doi = {10.1504/IJECRM.2012.046470},
  journal = {International Journal of Electronic Customer Relationship Management},
  bibtexkey = {Farquad201248},
  author = {Farquad, M.A.H., Ravi, V., Raju, S.B.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2012},
  title = {Predicting customer churn through interpersonal influence},
  abstract = {Preventing customer churn is an important task for many enterprises and requires customer churn prediction. This paper investigates the effects of interpersonal influence on the accuracy of customer churn predictions and proposes a novel prediction model that is based on interpersonal influence and that combines the propagation process and customers' personalized characters. Our contributions include the following: (1) the effects of interpersonal influence on prediction accuracy are evaluated while including determinants that other researchers proved effective, and several models are constructed based on machine learning and statistical methods and compared, assuring the validity of the evaluation; and (2) a novel prediction model based on interpersonal influence and information propagation is proposed. The dataset used in the empirical study was obtained from a leading mobile telecommunication service provider and contains the traditional and network attributes of over one million customers. The empirical results show that traditional classification models that incorporate interpersonal influence can greatly improve prediction accuracy, and our proposed prediction model outperforms the traditional models. © 2011 Elsevier B.V. All rights reserved.},
  doi = {10.1016/j.knosys.2011.12.005},
  journal = {Knowledge-Based Systems},
  bibtexkey = {Zhang201297},
  author = {Zhang, X., Zhu, J., Xu, S., Wan, Y.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2012},
  title = {Dynamic classifier ensemble model for customer classification with imbalanced class distribution},
  abstract = {Customer classification is widely used in customer relationship management including churn prediction, credit scoring, cross-selling and so on. In customer classification, an important yet challenging problem is the imbalance of data distribution. In this paper, we combine ensemble learning with cost-sensitive learning, and propose a dynamic classifier ensemble method for imbalanced data (DCEID). For each test customer, it can adaptively select out the more appropriate one from the two kinds of dynamic ensemble approach: dynamic classifier selection (DCS) and dynamic ensemble selection (DES). Meanwhile, new cost-sensitive selection criteria for DCS and DES are constructed respectively to improve the classification ability for imbalanced data. We apply this method to a credit scoring dataset in UCI and a real churn prediction dataset from a telecommunication company. The experimental results show that the classification performance of DCEID is not only better than some static ensemble methods such as weighted random forests and improved balanced random forests, but also better than the existing DCS and DES strategies. © 2011 Elsevier Ltd. All rights reserved.},
  doi = {10.1016/j.eswa.2011.09.059},
  journal = {Expert Systems with Applications},
  bibtexkey = {Xiao20123668},
  author = {Xiao, J., Xie, L., He, C., Jiang, X.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2012},
  title = {Predicting partial customer churn using Markov for discrimination for modeling first purchase sequences},
  abstract = {Currently, in order to remain competitive companies are adopting customer centered strategies and consequently customer relationship management is gaining increasing importance. In this context, customer retention deserves particular attention. This paper proposes a model for partial churn detection in the retail grocery sector that includes as a predictor the similarity of the products' first purchase sequence with churner and non-churner sequences. The sequence of first purchase events is modeled using Markov for discrimination. Two classification techniques are used in the empirical study: logistic regression and random forests. A real sample of approximately 95,000 new customers is analyzed taken from the data warehouse of a European retailing company. The empirical results reveal the relevance of the inclusion of a products' sequence likelihood in partial churn prediction models, as well as the supremacy of logistic regression when compared with random forests. © 2012 Springer-Verlag Berlin Heidelberg.},
  doi = {10.1007/s11634-012-0121-3},
  journal = {Advances in Data Analysis and Classification},
  bibtexkey = {MiguÃ©is2012337},
  author = {MiguÃ©is, V.L., Van den Poel, D., Camanho, A.S., FalcÃ£o e Cunha, J.},
  source = {SCOPUS}
}

@{,
  category = {INPROCEEDINGS},
  year = {2012},
  title = {Improving customer churn prediction by data augmentation using pictorial stimulus-choice data},
  abstract = {The purpose of this paper is to determine the added value of pictorial stimulus-choice data in customer churn prediction. Using Random Forests and 5 times 2 fold cross-validation, this study analyzes how much pictorial stimulus - choice data and survey data increase the AUC of a churn model over and above administrative, operational and complaints data. The finding is that pictorial-stimulus choice data significantly increases AUC of models with administrative and operational data. The practical implication of this finding is that companies should start considering mining pictorial data from social media sites (e.g. Pinterest), in order to augment their internal customer database. This study is original in that it is the first that assesses the added value of pictorial stimulus-choice data in predictive models. This is important because more and more social media websites are focusing on pictures. © 2012 Springer-Verlag.},
  doi = {10.1007/978-3-642-30864-2_21},
  journal = {Advances in Intelligent Systems and Computing},
  bibtexkey = {Ballings2012217},
  author = {Ballings, M., Van Den Poel, D., Verhagen, E.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2012},
  title = {Diagnosing assets impairment by using random forests model},
  abstract = {This study develops a diagnosing model to examine the outcomes of assets write-off in enriching the literatures of assets impairment. Prior studies employed the Logit, linear and Tobit regression models to classify the determination of assets impairment and to diagnose the magnitude of the impairment, respectively. However, the drivers of assets write-off are somewhat complicated explicitly or implicitly, these models are unlikely to provide fairly satisfactory results. To improve the diagnosis, the Random Forests model is used for the classification determining and the magnitude diagnosing of assets impairment in this study. The result reveals that the Random Forests model outperforms the Logit and linear regression models in each case with variables selected by individual wrapping approach. This study also demonstrates diagnostic checks for both models with similar selected variables. The results are robust to these various specifications. © 2012 World Scientific Publishing Company.},
  doi = {10.1142/S0219622012500046},
  journal = {International Journal of Information Technology and Decision Making},
  bibtexkey = {Chen201277},
  author = {Chen, C.-L., Wu, C.-W.},
  source = {SCOPUS}
}

@{,
  category = {INPROCEEDINGS},
  year = {2011},
  title = {Feature selection based transfer ensemble model for customer churn prediction},
  abstract = {It is difficult to get satisfactory customer churn prediction effect for the traditional model, because the class distribution of customer data is often imbalanced, and the available data in target task is little. This paper combines the transfer learning with the ensemble learning, and proposes a feature selection based transfer ensemble model (FSTE). It utilizes the customer data in both the related source domain and target domain, selects a series of feature subsets, obtains the corresponding training subsets by mapping; further, it trains a number of classifiers and gets the final customer churn prediction result by integrating the prediction results. The empirical results show that FSTE can achieve better customer churn prediction performance compared with the traditional churn prediction model, and some existing transfer learning models such as TFS, TrBagg and TrAdboost. © 2011 IEEE.},
  doi = {10.1109/ICSSEM.2011.6081258},
  journal = {2011 International Conference on System Science, Engineering Design and Manufacturing Informatization, ICSEM 2011},
  bibtexkey = {Xie2011134},
  author = {Xie, L., Li, D., Xiao, J.},
  source = {SCOPUS}
}

@{,
  category = {INPROCEEDINGS},
  year = {2011},
  title = {Transfer ensemble model for customer churn prediction with imbalanced class distribution},
  abstract = {Customer churn prediction is an important issue in customer relationship management. The class distribution of customer data is often imbalanced, which may affect the performance of churn prediction model greatly. This paper combines transfer learning and multiple classifiers ensemble, and proposes a transfer ensemble model for imbalanced data (TEMID). This method focuses on using transfer learning and sampling to enlarge the available training set and balance it respectively. What's more, it also uses multiple classifiers ensemble method to implement the classification. The performance of TEMID and some existing transfer learning algorithms are compared in two class imbalanced datasets. The results show that the TEMID methods can actually improve the performance of the customer churn prediction. © 2011 IEEE.},
  doi = {10.1109/ICM.2011.397},
  journal = {Proceedings - 2011 International Conference of Information Technology, Computer Engineering and Management Sciences, ICM 2011},
  bibtexkey = {Wang2011177},
  author = {Wang, Y., Xiao, J.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2011},
  title = {Credit card churn forecasting by logistic regression and decision tree},
  abstract = {In this paper, two data mining algorithms are applied to build a churn prediction model using credit card data collected from a real Chinese bank. The contribution of four variable categories: customer information, card information, risk information, and transaction activity information are examined. The paper analyzes a process of dealing with variables when data is obtained from a database instead of a survey. Instead of considering the all 135 variables into the model directly, it selects the certain variables from the perspective of not only correlation but also economic sense. In addition to the accuracy of analytic results, the paper designs a misclassification cost measurement by taking the two types error and the economic sense into account, which is more suitable to evaluate the credit card churn prediction model. The algorithms used in this study include logistic regression and decision tree which are proven mature and powerful classification algorithms. The test result shows that regression performs a little better than decision tree. © 2011 Elsevier Ltd. All rights reserved.},
  doi = {10.1016/j.eswa.2011.06.028},
  journal = {Expert Systems with Applications},
  bibtexkey = {Nie201115273},
  author = {Nie, G., Rowe, W., Zhang, L., Tian, Y., Shi, Y.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2011},
  title = {Tuning metaheuristics: A data mining based approach for particle swarm optimization},
  abstract = {The paper is concerned with practices for tuning the parameters of metaheuristics. Settings such as, e.g., the cooling factor in simulated annealing, may greatly affect a metaheuristic's efficiency as well as effectiveness in solving a given decision problem. However, procedures for organizing parameter calibration are scarce and commonly limited to particular metaheuristics. We argue that the parameter selection task can appropriately be addressed by means of a data mining based approach. In particular, a hybrid system is devised, which employs regression models to learn suitable parameter values from past moves of a metaheuristic in an online fashion. In order to identify a suitable regression method and, more generally, to demonstrate the feasibility of the proposed approach, a case study of particle swarm optimization is conducted. Empirical results suggest that characteristics of the decision problem as well as search history data indeed embody information that allows suitable parameter values to be determined, and that this type of information can successfully be extracted by means of nonlinear regression models. © 2010 Elsevier Ltd. All rights reserved.},
  doi = {10.1016/j.eswa.2011.04.075},
  journal = {Expert Systems with Applications},
  bibtexkey = {Lessmann201112826},
  author = {Lessmann, S., Caserta, M., Arango, I.M.},
  source = {SCOPUS}
}

@{,
  category = {INPROCEEDINGS},
  year = {2011},
  title = {Learnability of latent position network models},
  abstract = {The latent position model is a well known model for social network analysis which has also found application in other fields, such as analysis of marketing and e-commerce data. In such applications, the data sets are increasingly massive and only partially observed, giving rise to the possibility of overfitting by the model. Using tools from statistical learning theory, we bound the VC dimension of the latent position model, leading to bounds on the overfit of the model. We find that the overfit can decay to zero with increasing network size even if only a vanishing fraction of the total network is observed. However, the amount of observed data on a per-node basis should increase with the size of the graph. © 2011 IEEE.},
  doi = {10.1109/SSP.2011.5967748},
  journal = {IEEE Workshop on Statistical Signal Processing Proceedings},
  bibtexkey = {Choi2011521},
  author = {Choi, D.S., Wolfe, P.J.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2011},
  title = {Social network analysis and mining for business applications},
  abstract = {Social network analysis has gained significant attention in recent years, largely due to the success of online social networking and media-sharing sites, and the consequent availability of a wealth of social network data. In spite of the growing interest, however, there is little understanding of the potential business applications of mining social networks. While there is a large body of research on different problems and methods for social network mining, there is a gap between the techniques developed by the research community and their deployment in real-world applications. Therefore the potential business impact of these techniques is still largely unexplored. In this article we use a business process classiication framework to put the research topics in a business context and provide an overview of what we consider key problems and techniques in social network analysis and mining from the perspective of business applications. In particular, we discuss data acquisition and preparation, trust, expertise, community structure, network dynamics, and information propagation. In each case we present a brief overview of the problem, describe state-of-the art approaches, discuss business application examples, and map each of the topics to a business process classification framework. In addition, we provide insights on prospective business applications, challenges, and future research directions. The main contribution of this article is to provide a state-of-the-art overview of current techniques while providing a critical perspective on business applications of social network analysis and mining.© 2011 ACM.},
  doi = {10.1145/1961189.1961194},
  journal = {ACM Transactions on Intelligent Systems and Technology},
  bibtexkey = {Bonchi2011},
  author = {Bonchi, F., Castillo, C., Gionis, A., Jaimes, A.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2011},
  title = {Building comprehensible customer churn prediction models with advanced rule induction techniques},
  abstract = {Customer churn prediction models aim to detect customers with a high propensity to attrite. Predictive accuracy, comprehensibility, and justifiability are three key aspects of a churn prediction model. An accurate model permits to correctly target future churners in a retention marketing campaign, while a comprehensible and intuitive rule-set allows to identify the main drivers for customers to churn, and to develop an effective retention strategy in accordance with domain knowledge. This paper provides an extended overview of the literature on the use of data mining in customer churn prediction modeling. It is shown that only limited attention has been paid to the comprehensibility and the intuitiveness of churn prediction models. Therefore, two novel data mining techniques are applied to churn prediction modeling, and benchmarked to traditional rule induction techniques such as C4.5 and RIPPER. Both AntMiner+ and ALBA are shown to induce accurate as well as comprehensible classification rule-sets. AntMiner+ is a high performing data mining technique based on the principles of Ant Colony Optimization that allows to include domain knowledge by imposing monotonicity constraints on the final rule-set. ALBA on the other hand combines the high predictive accuracy of a non-linear support vector machine model with the comprehensibility of the rule-set format. The results of the benchmarking experiments show that ALBA improves learning of classification techniques, resulting in comprehensible models with increased performance. AntMiner+ results in accurate, comprehensible, but most importantly justifiable models, unlike the other modeling techniques included in this study. © 2010 Elsevier Ltd. All rights reserved.},
  doi = {10.1016/j.eswa.2010.08.023},
  journal = {Expert Systems with Applications},
  bibtexkey = {Verbeke20112354},
  author = {Verbeke, W., Martens, D., Mues, C., Baesens, B.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2011},
  title = {Mining data with random forests: A survey and results of new tests},
  abstract = {Random forests (RF) has become a popular technique for classification, prediction, studying variable importance, variable selection, and outlier detection. There are numerous application examples of RF in a variety of fields. Several large scale comparisons including RF have been performed. There are numerous articles, where variable importance evaluations based on the variable importance measures available from RF are used for data exploration and understanding. Apart from the literature survey in RF area, this paper also presents results of new tests regarding variable rankings based on RF variable importance measures. We studied experimentally the consistency and generality of such rankings. Results of the studies indicate that there is no evidence supporting the belief in generality of such rankings. A high variance of variable importance evaluations was observed in the case of small number of trees and small data sets. © 2010 Elsevier Ltd. All rights reserved.},
  doi = {10.1016/j.patcog.2010.08.011},
  journal = {Pattern Recognition},
  bibtexkey = {Verikas2011330},
  author = {Verikas, A., Gelzinis, A., Bacauskiene, M.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2011},
  title = {A hybrid under-sampling approach for mining unbalanced datasets: Applications to banking and insurance},
  abstract = {In solving unbalanced classification problems, machine learning algorithms are overwhelmed by the majority class and consequently misclassify the minority class observations. Here, we propose a hybrid under-sampling approach to improve the performance of classifiers. The proposed approach first employs k-reverse nearest neighbour (kRNN) method to detect the outliers from majority class. After removing the outliers, using K-means clustering, K-clusters are selected to further reduce the influence of the majority class. Then, we employed support vector machine (SVM), logistic regression (LR), multi layer perceptron (MLP), radial basis function network (RBF), group method of data handling (GMDH), genetic programming (GP) and decision tree (J48) for classification purpose. The effectiveness of the proposed approach was demonstrated on datasets taken from insurance fraud detection and credit card churn in banking domain. Ten-fold cross validation method was used in the study. It is observed that the proposed approach improved the performance of the classifiers. Copyright © 2011 Inderscience Enterprises Ltd.},
  doi = {10.1504/IJDMMM.2011.038812},
  journal = {International Journal of Data Mining, Modelling and Management},
  bibtexkey = {Vasu201175},
  author = {Vasu, M., Ravi, V.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2011},
  title = {A fuzzy prediction model for calling communities},
  abstract = {The analysis of logs related to social communities 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 general, identifying calling communities can be used to determine a particular customer's value according to the general pattern of behaviour of the community that the customer belongs to; this helps in creating an effective targeted marketing design, which is significantly important for increasing profitability. In the telecommunications industry, machine-learning techniques have been applied to the Call Detail Record (CDR) for predicting customer behaviour such as churn prediction. In this paper, we pursue the identification of 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. Customer calling patterns are then input to a classification algorithm to generate a classifier model for predicting the calling communities of a customer. We apply two different classification methods: the Support Vector Machine (SVM) algorithm and the fuzzy genetic classifier. The latter method is used for possibly assigning a customer to different classes with different degrees of membership. The reported test results demonstrate the applicability and effectiveness of the proposed approach. © Inderscience Enterprises Ltd.},
  doi = {10.1504/IJNVO.2011.037162},
  journal = {International Journal of Networking and Virtual Organisations},
  bibtexkey = {Kianmehr201175},
  author = {Kianmehr, K., Alhajj, R.},
  source = {SCOPUS}
}

@{,
  category = {INPROCEEDINGS},
  year = {2010},
  title = {Churn in social networks: A discussion boards case study},
  abstract = {Churn has been identified as an important issue in a wide range of industries. In social networks, churn represents a significant risk for the health and functioning of communities. However, the importance and actual meaning of churn in social networks is almost unexplored. This work provides a general view on these issues and discusses aspects that are especially relevant to discussion boards. We provide a broad literature review on "traditional" churn analysis and prediction and highlight the specialities of churn in social networks. We further present an empirical analysis of a churn definition particularly appropriate for discussion boards and propose future research directions for predicting churn in social networks, focusing on the importance of social roles, influence and influence diffusion. © 2010 IEEE.},
  doi = {10.1109/SocialCom.2010.40},
  journal = {Proceedings - SocialCom 2010: 2nd IEEE International Conference on Social Computing, PASSAT 2010: 2nd IEEE International Conference on Privacy, Security, Risk and Trust},
  bibtexkey = {Karnstedt2010233},
  author = {Karnstedt, M., Hennessy, T., Chan, J., Hayes, C.},
  source = {SCOPUS}
}

@{,
  category = {INPROCEEDINGS},
  year = {2010},
  title = {Fuzzy-rough k-nearest neighbor algorithm for imbalanced data sets learning},
  abstract = {Learning from imbalanced data sets presents a new challenge to machine learning community, as traditional methods are biased to majority classes and produce poor detection rate of minority classes. This paper presents a new approach, namely fuzzy-rough k-nearest neighbor algorithm for imbalanced data sets learning to improve the classification performance of minority class. The approach defines fuzzy membership function that is in favor of minority class and constructs fuzzy equivalent relation between the unlabeled instance and its k nearest neighbors. The approach takes the fuzziness and roughness of the nearest neighbors of an instance into consideration, and can reduce the disturbance of majority class to minority class. Experiments show that our new approach improves not only the classification performance of minority class more effectively, but also the classification performance of the whole data set comparing with other methods. ©2010 IEEE.},
  doi = {10.1109/FSKD.2010.5569116},
  journal = {Proceedings - 2010 7th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2010},
  bibtexkey = {Han20101286},
  author = {Han, H., Mao, B.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2010},
  title = {Rule extraction from support vector machine using modified active learning based approach: An application to CRM},
  abstract = {Despite superior generalization performance Support vector machines (SVMs) generate black box models. The process of converting such opaque models into transparent model is often regarded as rule extraction. This paper presents a new approach for rule extraction from SVMs using modified active learning based approach (mALBA), to predict churn in bank credit cards. The dataset is obtained from Business Intelligence Cup 2004, which is highly unbalanced with 93% loyal and 7% churned customers' data. Since identifying churner is paramount from business perspective, therefore considering sensitivity alone, the empirical results suggest that the proposed rule extraction approach using mALBA yielded the best sensitivity compared to other classifiers. © 2010 Springer-Verlag.},
  doi = {10.1007/978-3-642-15387-7_50},
  journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
  bibtexkey = {Farquad2010461},
  author = {Farquad, M.A.H., Ravi, V., Raju, S.B.},
  source = {SCOPUS}
}

@{,
  category = {INPROCEEDINGS},
  year = {2010},
  title = {Customer relationship management: Computer-assisted tools for customer lifetime value prediction},
  abstract = {A customer lifetime value (CLV) can be used as an indicator for assessing a customer's worthiness. It is often used among the medium to large organizations for predicting future revenues from their customers. Many research projects have been conducted to enhance the individual prediction models and formulae. However, there are no obvious formal approaches to ensure the consistency and customizability of a prediction process and its major elements. This paper proposes a basic set of computer-assisted tools for a CLV prediction process. The semi-automated tools could be used by the business analysts of an organization to customize the existing templates; record, monitor, and track the work done in each phase of the process. In this paper, various challenges are discussed together with their possible solutions. The tools are specifically designed and implemented for the derivation of customizable formulae and models among the different customer segments of a non-profit organization. In the near future, more research work would be conducted to enhance the capabilities of the tools to cater for possible collaborative CLV predictions among the interested parties on the variables, algorithms, and the subsystems of a model base management system and the associated knowledge base management system. © 2010 IEEE.},
  doi = {10.1109/ITSIM.2010.5561487},
  journal = {Proceedings 2010 International Symposium on Information Technology - System Development and Application and Knowledge Society, ITSim'10},
  bibtexkey = {Yean20101180},
  author = {Yean, L.C., Khoo, V.K.T.},
  source = {SCOPUS}
}

@{,
  category = {INPROCEEDINGS},
  year = {2010},
  title = {Data mining applications in customer churn management},
  abstract = {According to the lack of a comprehensive literature review in the area of application of data mining in customer churn management, which has become a central issue in customer relationship management nowadays, this paper tackles to provide a brief review of researches in that field from two perspectives: techniques used and statistical reports. From the first point of view, a taxonomy based on the models exploited in papers is provided. The latter perspective gives insight into the trend, place and frequency of publications in the mentioned area. Also some of recent papers are summarized for the sake of interested readers. It is suggested that this paper will point out the gaps and strengths in this research issue, which might be of interest for contributors and researches and business managers respectively. © 2010 IEEE.},
  doi = {10.1109/ISMS.2010.49},
  journal = {ISMS 2010 - UKSim/AMSS 1st International Conference on Intelligent Systems, Modelling and Simulation},
  bibtexkey = {KhakAbi2010220},
  author = {KhakAbi, S., Gholamian, M.R., Namvar, M.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2010},
  title = {Variable selection by association rules for customer churn prediction of multimedia on demand},
  abstract = {Multimedia on demand (MOD) is an interactive system that provides a number of value-added services in addition to traditional TV services, such as video on demand and interactive online learning. This opens a new marketing and managerial problem for the telecommunication industry to retain valuable MOD customers. Data mining techniques have been widely applied to develop customer churn prediction models, such as neural networks and decision trees in the domain of mobile telecommunication. However, much related work focuses on developing the prediction models per se. Few studies consider the pre-processing step during data mining whose aim is to filter out unrepresentative data or information. This paper presents the important processes of developing MOD customer churn prediction models by data mining techniques. They contain the pre-processing stage for selecting important variables by association rules, which have not been applied before, the model construction stage by neural networks (NN) and decision trees (DT), which are widely adapted in the literature, and four evaluation measures including prediction accuracy, precision, recall, and F-measure, all of which have not been considered to examine the model performance. The source data are based on one telecommunication company providing the MOD services in Taiwan, and the experimental results show that using association rules allows the DT and NN models to provide better prediction performances over a chosen validation dataset. In particular, the DT model performs better than the NN model. Moreover, some useful and important rules in the DT model, which show the factors affecting a high proportion of customer churn, are also discussed for the marketing and managerial purpose. © 2009 Elsevier Ltd. All rights reserved.},
  doi = {10.1016/j.eswa.2009.06.076},
  journal = {Expert Systems with Applications},
  bibtexkey = {Tsai20102006},
  author = {Tsai, C.-F., Chen, M.-Y.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2010},
  title = {Improved marketing decision making in a customer churn prediction context using generalized additive models},
  abstract = {Nowadays, companies are investing in a well-considered CRM strategy. One of the cornerstones in CRM is customer churn prediction, where one tries to predict whether or not a customer will leave the company. This study focuses on how to better support marketing decision makers in identifying risky customers by using Generalized Additive Models (GAM). Compared to Logistic Regression, GAM relaxes the linearity constraint which allows for complex non-linear fits to the data. The contributions to the literature are three-fold: (i) it is shown that GAM is able to improve marketing decision making by better identifying risky customers; (ii) it is shown that GAM increases the interpretability of the churn model by visualizing the non-linear relationships with customer churn identifying a quasi-exponential, a U, an inverted U or a complex trend and (iii) marketing managers are able to significantly increase business value by applying GAM in this churn prediction context. © 2009 Elsevier Ltd. All rights reserved.},
  doi = {10.1016/j.eswa.2009.07.029},
  journal = {Expert Systems with Applications},
  bibtexkey = {Coussement20102132},
  author = {Coussement, K., Benoit, D.F., Van den Poel, D.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2010},
  title = {Data mining techniques in customer churn prediction},
  abstract = {Customer churn prediction is one of the most important problems in customer relationship management (CRM). Its aim is to retain valuable customers to maximize the profit of a company. To predict whether a customer will be a churner or non-churner, there are a number of data mining techniques applied for churn prediction, such as artificial neural networks, decision trees, and support vector machines. This paper reviews some recent patents along with 21 related studies published from 2000 to 2009 and compares them in terms of the domain dataset used, data pre-processing and prediction techniques considered, etc. Future research issues are discussed. © 2010 Bentham Science Publishers Ltd.},
  doi = {10.2174/1874479611003010028},
  journal = {Recent Patents on Computer Science},
  bibtexkey = {Tsai201028},
  author = {Tsai, C.-F., Lu, Y.-H.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2010},
  title = {Consumer finance: Challenges for operational research},
  abstract = {Consumer finance has become one of the most important areas of banking, both because of the amount of money being lent and the impact of such credit on global economy and the realisation that the credit crunch of 2008 was partly due to incorrect modelling of the risks in such lending. This paper reviews the development of credit scoringthe way of assessing risk in consumer financeand what is meant by a credit score. It then outlines 10 challenges for Operational Research to support modelling in consumer finance. Some of these involve developing more robust risk assessment systems, whereas others are to expand the use of such modelling to deal with the current objectives of lenders and the new decisions they have to make in consumer finance. © 2010 Operational Research Society Ltd. All rights reserved.},
  doi = {10.1057/jors.2009.104},
  journal = {Journal of the Operational Research Society},
  bibtexkey = {Thomas201041},
  author = {Thomas, L.C.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2009},
  title = {Data mining using rules extracted from SVM: An application to churn prediction in bank credit cards},
  abstract = {In this work, an eclectic procedure for rule extraction from Support Vector Machine is proposed, where Tree is generated using Naïve Bayes Tree (NBTree) resulting in the SVM+NBTree hybrid. The data set analyzed in this paper is about churn prediction in bank credit cards and is obtained from Business Intelligence Cup 2004. The data set under consideration is highly unbalanced with 93.11% loyal and 6.89% churned customers. Since identifying churner is of paramount importance from business perspective, sensitivity of classification model is more critical. Using the available, original unbalanced data only, we observed that the proposed hybrid SVM+NBTree yielded the best sensitivity compared to other classifiers. © 2009 Springer-Verlag Berlin Heidelberg.},
  doi = {10.1007/978-3-642-10646-0_47},
  journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
  bibtexkey = {Farquad2009390},
  author = {Farquad, M.A.H., Ravi, V., Raju, S.B.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2009},
  title = {Benefits of quantile regression for the analysis of customer lifetime value in a contractual setting: An application in financial services},
  abstract = {The move towards a customer-centred approach to marketing, coupled with the increasing availability of customer transaction data, has led to an interest in understanding and estimating customer lifetime value (CLV). Several authors point out that, when evaluating customer profitability, profitable customers are rare compared to the unprofitable ones. In spite of this, most authors fail to recognize the implications of these skewed distributions on the performance of models they use. In this study, we propose analyzing CLV by means of quantile regression. In a financial services application, we show that this technique provides management more in-depth insights into the effects of the covariates that are missed with linear regression. Moreover, we show that in the common situation where interest is in a top-customer segment, quantile regression outperforms linear regression. The method also has the ability of constructing prediction intervals. Combining the CLV point estimate with the prediction intervals leads to a new segmentation scheme that is the first to account for uncertainty in the predictions. This segmentation is ideally suited for managing the portfolio of customers. © 2009.},
  doi = {10.1016/j.eswa.2009.01.031},
  journal = {Expert Systems with Applications},
  bibtexkey = {Benoit200910475},
  author = {Benoit, D.F., Van den Poel, D.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2009},
  title = {Modeling churn using customer lifetime value},
  abstract = {The definition and modeling of customer loyalty have been central issues in customer relationship management since many years. Recent papers propose solutions to detect customers that are becoming less loyal, also called churners. The churner status is then defined as a function of the volume of commercial transactions. In the context of a Belgian retail financial service company, our first contribution is to redefine the notion of customer loyalty by considering it from a customer-centric viewpoint instead of a product-centric one. We hereby use the customer lifetime value (CLV) defined as the discounted value of future marginal earnings, based on the customer's activity. Hence, a churner is defined as someone whose CLV, thus the related marginal profit, is decreasing. As a second contribution, the loss incurred by the CLV decrease is used to appraise the cost to misclassify a customer by introducing a new loss function. In the empirical study, we compare the accuracy of various classification techniques commonly used in the domain of churn prediction, including two cost-sensitive classifiers. Our final conclusion is that since profit is what really matters in a commercial environment, standard statistical accuracy measures for prediction need to be revised and a more profit oriented focus may be desirable. © 2008 Elsevier B.V. All rights reserved.},
  doi = {10.1016/j.ejor.2008.06.027},
  journal = {European Journal of Operational Research},
  bibtexkey = {Glady2009402},
  author = {Glady, N., Baesens, B., Croux, C.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2009},
  title = {Electronic promotion to new customers using mkNN learning},
  abstract = {In recent years, several techniques have been proposed to model electronic promotions for existing customers. However, these techniques are not applicable for new customers with no previous profile or behavior data. This study models promotions to new customers in an electronic marketplace. We introduce a multi-valued k-Nearest Neighbor (mkNN) learning capability for modeling promotions to new customers. In this modified learning algorithm, instead of a single product category, the seller sends the new customer a promotion on a variable set of m categories (where m is a variable) with the highest rank of desirability among the most similar previous customers. Previous studies consider sellers' profits in promotion and marketing models. In addition to the sellers' profits, three important factors - annoyance of customers, sellers' reputations, and customers' anonymity - are considered in this study. Without considering the customer's profile, we minimize unrelated and disliked offers to reduce the customer's annoyance and elevate the seller's reputation. The promotion models are evaluated in two separate experiments on populations with different degrees of optimism: (1) with fixed number of customers; and (2) in a fixed period of time. The evaluation is based on the parameters of customer population size and behavior as well as time interval, seller payoff, seller reputation, and the number of promotions canceled by the customers. The simulation results demonstrate that the proposed mkNN-based promotion strategies are moderately efficient with respect to all parameters for providing services in a large population. In addition, purchasing preferences of past customers, which are based on periodic promotions that a seller sends to customers, can generate future rapidly expanding demands in the market. By using these approaches, an advertising company can send acceptable promotions to customers without having specific profile information. © 2008 Elsevier Inc. All rights reserved.},
  doi = {10.1016/j.ins.2008.09.019},
  journal = {Information Sciences},
  bibtexkey = {Nassiri-Mofakham2009248},
  author = {Nassiri-Mofakham, F., Nematbakhsh, M.A., Baraani-Dastjerdi, A., Ghasem-Aghaee, N.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2009},
  title = {Customer churn prediction using improved balanced random forests},
  abstract = {Churn prediction is becoming a major focus of banks in China who wish to retain customers by satisfying their needs under resource constraints. In churn prediction, an important yet challenging problem is the imbalance in the data distribution. In this paper, we propose a novel learning method, called improved balanced random forests (IBRF), and demonstrate its application to churn prediction. We investigate the effectiveness of the standard random forests approach in predicting customer churn, while also integrating sampling techniques and cost-sensitive learning into the approach to achieve a better performance than most existing algorithms. The nature of IBRF is that the best features are iteratively learned by altering the class distribution and by putting higher penalties on misclassification of the minority class. We apply the method to a real bank customer churn data set. It is found to improve prediction accuracy significantly compared with other algorithms, such as artificial neural networks, decision trees, and class-weighted core support vector machines (CWC-SVM). Moreover, IBRF also produces better prediction results than other random forests algorithms such as balanced random forests and weighted random forests. © 2008 Elsevier Ltd. All rights reserved.},
  doi = {10.1016/j.eswa.2008.06.121},
  journal = {Expert Systems with Applications},
  bibtexkey = {Xie20095445},
  author = {Xie, Y., Li, X., Ngai, E.W.T., Ying, W.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2009},
  title = {Domain knowledge integration in data mining using decision tables: Case studies in churn prediction},
  abstract = {Companies' interest in customer relationship modelling and key issues such as customer lifetime value and churn has substantially increased over the years. However, the complexity of building, interpreting and applying these models creates obstacles for their implementation. The main contribution of this paper is to show how domain knowledge can be incorporated in the data mining process for churn prediction, viz. through the evaluation of coefficient signs in a logistic regression model, and secondly, by analysing a decision table (DT) extracted from a decision tree or rule-based classifier. An algorithm to check DTs for violations of monotonicity constraints is presented, which involves the repeated application of condition reordering and table contraction to detect counter-intuitive patterns. Both approaches are applied to two telecom data sets to empirically demonstrate how domain knowledge can be used to ensure the interpretability of the resulting models.Journal of the Operational Research Society (2009) 60, 1096-1106. doi:10.1057/jors.2008.161; published online 18 February 2009 © 2009 Operational Research Society Ltd.},
  doi = {10.1057/jors.2008.161},
  journal = {Journal of the Operational Research Society},
  bibtexkey = {Lima20091096},
  author = {Lima, E., Mues, C., Baesens, B.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2009},
  title = {Improving customer attrition prediction by integrating emotions from client/company interaction emails and evaluating multiple classifiers},
  abstract = {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. © 2008 Elsevier Ltd. All rights reserved.},
  doi = {10.1016/j.eswa.2008.07.021},
  journal = {Expert Systems with Applications},
  bibtexkey = {Coussement20096127},
  author = {Coussement, K., Poel, D.V.d.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2009},
  title = {Handling class imbalance in customer churn prediction},
  abstract = {Customer churn is often a rare event in service industries, but of great interest and great value. Until recently, however, class imbalance has not received much attention in the context of data mining [Weiss, G. M. (2004). Mining with rarity: A unifying framework. SIGKDD Explorations, 6(1), 7-19]. In this study, we investigate how we can better handle class imbalance in churn prediction. Using more appropriate evaluation metrics (AUC, lift), we investigated the increase in performance of sampling (both random and advanced under-sampling) and two specific modelling techniques (gradient boosting and weighted random forests) compared to some standard modelling techniques. AUC and lift prove to be good evaluation metrics. AUC does not depend on a threshold, and is therefore a better overall evaluation metric compared to accuracy. Lift is very much related to accuracy, but has the advantage of being well used in marketing practice [Ling, C., & Li, C. (1998). Data mining for direct marketing problems and solutions. In Proceedings of the fourth international conference on knowledge discovery and data mining (KDD-98). New York, NY: AAAI Press]. Results show that under-sampling can lead to improved prediction accuracy, especially when evaluated with AUC. Unlike Ling and Li [Ling, C., & Li, C. (1998). Data mining for direct marketing problems and solutions. In Proceedings of the fourth international conference on knowledge discovery and data mining (KDD-98). New York, NY: AAAI Press], we find that there is no need to under-sample so that there are as many churners in your training set as non churners. Results show no increase in predictive performance when using the advanced sampling technique CUBE in this study. This is in line with findings of Japkowicz [Japkowicz, N. (2000). The class imbalance problem: significance and strategies. In Proceedings of the 2000 international conference on artificial intelligence (IC-AI'2000): Special track on inductive learning, Las Vegas, Nevada], who noted that using sophisticated sampling techniques did not give any clear advantage. Weighted random forests, as a cost-sensitive learner, performs significantly better compared to random forests, and is therefore advised. It should, however always be compared to logistic regression. Boosting is a very robust classifier, but never outperforms any other technique. © 2008 Elsevier Ltd. All rights reserved.},
  doi = {10.1016/j.eswa.2008.05.027},
  journal = {Expert Systems with Applications},
  bibtexkey = {Burez20094626},
  author = {Burez, J., Van den Poel, D.},
  source = {SCOPUS}
}

@{,
  category = {INPROCEEDINGS},
  year = {2008},
  title = {Construction of Bayesian classifiers with GA for predicting customer retention},
  abstract = {In this paper, a novel Bayesian classifier model is constructed based on genetic algorithms (GA) for the prediction of customer churn. Experiment results have shown that it not only matches potential churning customers well from the large amount of validation data but also shows higher classifying precision compared with the other three Bayesian classifier models. Recently, the model has been adopted by a Japanese enterprise to successfully handle some business problems of potential churning customers prediction. © 2008 IEEE.},
  doi = {10.1109/ICNC.2008.724},
  journal = {Proceedings - 4th International Conference on Natural Computation, ICNC 2008},
  bibtexkey = {Hongmei2008181},
  author = {Hongmei, S., Gaofeng, Z., Fengxian, A.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2008},
  title = {Separating financial from commercial customer churn: A modeling step towards resolving the conflict between the sales and credit department},
  abstract = {In subscription services, customers who leave the company can be divided into two groups: customers who do not renew their fixed-term contract at the end of that contract, and others who just stop paying during their contract to which they are legally bound. Those two separate processes are often modeled together in a so-called churn-prediction model, but are actually two different processes. The first type of churn can be considered commercial churn, i.e., customers making a studied choice not to renew their subscriptions. The second phenomenon is defined as financial churn, people who stop paying because they can no longer afford the service. The so-called marketing dilemma arises, as conflicting interests exist between the sales and marketing department on the one hand, and the legal and credit department on the other hand. This paper shows that the two different processes mentioned can be separated by using information from the internal database of the company and that previous bad-payment behavior is more important as a driver for financial than for commercial churn. Finally, it is shown on real-life data that one can more accurately predict financial churn than commercial churn (increasing within period as well as out-of-period prediction performance). Conversely, when trying to persuade customers to stay with the company, the impact of 'loyalty' actions is far greater with potential commercial churners as compared to financial churners. Evidence comes from a real-life field experiment. © 2007 Elsevier Ltd. All rights reserved.},
  doi = {10.1016/j.eswa.2007.07.036},
  journal = {Expert Systems with Applications},
  bibtexkey = {Burez2008497},
  author = {Burez, J., Van den Poel, D.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2008},
  title = {Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques},
  abstract = {CRM gains increasing importance due to intensive competition and saturated markets. With the purpose of retaining customers, academics as well as practitioners find it crucial to build a churn prediction model that is as accurate as possible. This study applies support vector machines in a newspaper subscription context in order to construct a churn model with a higher predictive performance. Moreover, a comparison is made between two parameter-selection techniques, needed to implement support vector machines. Both techniques are based on grid search and cross-validation. Afterwards, the predictive performance of both kinds of support vector machine models is benchmarked to logistic regression and random forests. Our study shows that support vector machines show good generalization performance when applied to noisy marketing data. Nevertheless, the parameter optimization procedure plays an important role in the predictive performance. We show that only when the optimal parameter-selection procedure is applied, support vector machines outperform traditional logistic regression, whereas random forests outperform both kinds of support vector machines. As a substantive contribution, an overview of the most important churn drivers is given. Unlike ample research, monetary value and frequency do not play an important role in explaining churn in this subscription-services application. Even though most important churn predictors belong to the category of variables describing the subscription, the influence of several client/company-interaction variables cannot be neglected. © 2006 Elsevier Ltd. All rights reserved.},
  doi = {10.1016/j.eswa.2006.09.038},
  journal = {Expert Systems with Applications},
  bibtexkey = {Coussement2008313},
  author = {Coussement, K., Van den Poel, D.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2007},
  title = {Modeling CLV: A test of competing models in the insurance industry},
  abstract = {Customer Lifetime Value (CLV) is one of the key metrics in marketing and is considered an important segmentation base. This paper studies the capabilities of a range of models to predict CLV in the insurance industry. The simplest models can be constructed at the customer relationship level, i.e. aggregated across all services. The more complex models focus on the individual services, paying explicit attention to cross buying, but also retention. The models build on a plethora of approaches used in the existing literature and include a status quo model, a Tobit II model, univariate and multivariate choice models, and duration models. For all models, CLV for each customer is computed for a four-year time horizon. We find that the simple models perform well. The more complex models are expected to better capture the richness of relationship development. Surprisingly, this does not lead to substantially better CLV predictions. © Springer Science+Business Media, LLC 2007.},
  doi = {10.1007/s11129-006-9016-y},
  journal = {Quantitative Marketing and Economics},
  bibtexkey = {Donkers2007163},
  author = {Donkers, B., Verhoef, P.C., de Jong, M.G.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2007},
  title = {CRM at a pay-TV company: Using analytical models to reduce customer attrition by targeted marketing for subscription services},
  abstract = {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'. © 2005 Elsevier Ltd. All rights reserved.},
  doi = {10.1016/j.eswa.2005.11.037},
  journal = {Expert Systems with Applications},
  bibtexkey = {Burez2007277},
  author = {Burez, J., Van den Poel, D.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2007},
  title = {Predicting customer loyalty using the internal transactional database},
  abstract = {Loyalty and targeting are central topics in Customer Relationship Management. Yet, the information that resides in customer databases only records transactions at a single company, whereby customer loyalty is generally unavailable. In this study, we enrich the customer database with a prediction of a customer's behavioral loyalty such that it can be deployed for targeted marketing actions without the necessity to measure the loyalty of every single customer. To this end, we compare multiple linear regression with two state-of-the-art machine learning techniques (random forests and automatic relevance determination neural networks), and we show that (i) a customer's behavioral loyalty can be predicted to a reasonable degree using the transactional database, (ii) given that overfitting is controlled for by the variable-selection procedure we propose in this study, a multiple linear regression model significantly outperforms the other models, (iii) the proposed variable-selection procedure has a beneficial impact on the reduction of multicollinearity, and (iv) the most important indicator of behavioral loyalty consists of the variety of products previously purchased. © 2005 Elsevier Ltd. All rights reserved.},
  doi = {10.1016/j.eswa.2005.11.004},
  journal = {Expert Systems with Applications},
  bibtexkey = {Buckinx2007125},
  author = {Buckinx, W., Verstraeten, G., Van den Poel, D.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2006},
  title = {Bagging and boosting classification trees to predict churn},
  abstract = {In this article, the authors explore the bagging and boosting classification techniques. They apply the two techniques to a customer database of an anonymous U.S. wireless telecommunications company, and both significantly improve accuracy in predicting churn. This higher predictive performance could ultimately lead to incremental profits for companies that use these methods. Furthermore, the results recommend the use of a balanced sampling scheme when predicting a rare event from large data sets, but this requires an appropriate bias correction. © 2006, American Marketing Association.},
  doi = {10.1509/jmkr.43.2.276},
  journal = {Journal of Marketing Research},
  bibtexkey = {Lemmens2006276},
  author = {Lemmens, A., Croux, C.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2005},
  title = {Predicting customer retention and profitability by using random forests and regression forests techniques},
  abstract = {In an era of strong customer relationship management (CRM) emphasis, firms strive to build valuable relationships with their existing customer base. In this study, we attempt to better understand three important measures of customer outcome: next buy, partial-defection and customers' profitability evolution. By means of random forests techniques we investigate a broad set of explanatory variables, including past customer behavior, observed customer heterogeneity and some typical variables related to intermediaries. We analyze a real-life sample of 100,000 customers taken from the data warehouse of a large European financial services company. Two types of random forests techniques are employed to analyze the data: random forests are used for binary classification, whereas regression forests are applied for the models with linear dependent variables. Our research findings demonstrate that both random forests techniques provide better fit for the estimation and validation sample compared to ordinary linear regression and logistic regression models. Furthermore, we find evidence that the same set of variables have a different impact on buying versus defection versus profitability behavior. Our findings suggest that past customer behavior is more important to generate repeat purchasing and favorable profitability evolutions, while the intermediary's role has a greater impact on the customers' defection proneness. Finally, our results demonstrate the benefits of analyzing different customer outcome variables simultaneously, since an extended investigation of the next buy-partial-defection-customer profitability triad indicates that one cannot fully understand a particular outcome without understanding the other related behavioral outcome variables. © 2005 Elsevier Ltd. All rights reserved.},
  doi = {10.1016/j.eswa.2005.04.043},
  journal = {Expert Systems with Applications},
  bibtexkey = {LariviÃ¨re2005472},
  author = {LariviÃ¨re, B., Van Den Poel, D.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2005},
  title = {Customer base analysis: Partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting},
  abstract = {Customer relationship management (CRM) enjoys increasing attention as a countermeasure to switching behaviour of customers. Because foregone profits of (partially) defected customers can be significant, an increase of the retention rate can be very profitable. In this paper we focus on the treatment of a company's most behaviourally loyal customers in a non-contractual setting. We build a model in order to predict partial defection by behaviourally loyal clients using three classification techniques: Logistic regression, automatic relevance determination (ARD) Neural Networks and Random Forests. Focusing on partial attrition of high-frequency shoppers who exhibit a regular visit pattern may overcome the problem of unidentifiability of total defection in non-contractual settings. Classification accuracy (PCC) and area under the receiver operating characteristic curve (AUC) are used to evaluate classifier performance on a test/hold-out sample. Using real-life data from an FMCG retailer, we show that future partial defection can be successfully predicted, i.e. exceeding the benchmark hurdle of the null model. There are no significant differences in terms of performance among alternative classification techniques. Similar to direct-marketing applications we find that past behavioural variables, more specifically RFM variables (recency, frequency, and monetary value) are the best predictors of partial customer defection. This set of variables complements demographic variables confirming findings by other authors about its importance in predicting chum behaviour. Moreover, additional variables (listed in decreasing order of importance) such as the length of customer relationship, mode of payment, buying behaviour across categories, usage of promotions and brand purchase behaviour are shown to be moderately useful to incorporate in attrition models. © 2004 Elsevier B.V. All rights reserved.},
  doi = {10.1016/j.ejor.2003.12.010},
  journal = {European Journal of Operational Research},
  bibtexkey = {Buckinx2005252},
  author = {Buckinx, W., Van Den Poel, D.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2003},
  title = {A novel evolutionary data mining algorithm with applications to churn prediction},
  abstract = {Classification is an important topic in data mining research. Given a set of data records, each of which belongs to one of a number of predefined classes, the classification problem is concerned with the discovery of classification rules that can allow records with unknown class membership to be correctly classified. Many algorithms have been developed to mine large data sets for classification models and they have been shown to be very effective. However, when it comes to determining the likelihood of each classification made, many of them are not designed with such purpose in mind. For this, they are not readily applicable to such problem as church prediction. For such an application, the goal is not only to predict whether or not a subscriber would switch from one carrier to another, it is also important that the likelihood of the subscriber's doing so be predicted. The reason for this is that a carrier can then choose to provide special personalized offer and services to those subscribers who are predicted with higher likelihood to churn. Given its importance, we propose a new data mining algorithm, called data mining by evolutionary learning (DMEL), to handle classification problems of which the accuracy of each predictions made has to be estimated. In performing its tasks, DMEL searches through the possible rule space using an evolutionary approach that has the following characteristics: 1) the evolutionary process begins with the generation of an initial set of first-order rules (i.e., rules with one conjunct/condition) using a probabilistic induction technique and based on these rules, of higher order (two or more conjuncts) are obtained iteratively; 2) when identifying interesting rules, an objective interestingness measure is used; 3) the fitness of a chromosome is defined in terms of the probability that the attribute values of a record can be correctly determined using the rules it encodes; and 4) the likelihood of predictions (or classifications) made are estimated so that subscribers can be ranked according to their likelihood to churn. Experiments with different data sets showed that DMEL is able to effectively discover interesting classification rules. In particular, it is able to predict churn accurately under different churn rates when applied to real telecom subscriber data.},
  doi = {10.1109/TEVC.2003.819264},
  journal = {IEEE Transactions on Evolutionary Computation},
  bibtexkey = {Au2003532},
  author = {Au, W.-H., Chan, C.C., Yao, X.},
  source = {SCOPUS}
}

@{,
  category = {ARTICLE},
  year = {2002},
  title = {Turning telecommunications call details to churn prediction: A data mining approach},
  abstract = {As deregulation, new technologies, and new competitors open up the mobile telecommunications industry, churn prediction and management has become of great concern to mobile service providers. A mobile service provider wishing to retain its subscribers needs to be able to predict which of them may be at-risk of changing services and will make those subscribers the focus of customer retention efforts. In response to the limitations of existing churn-prediction systems and the unavailability of customer demographics in the mobile telecommunications provider investigated, we propose, design, and experimentally evaluate a churn-prediction technique that predicts churning from subscriber contractual information and call pattern changes extracted from call details. This proposed technique is capable of identifying potential churners at the contract level for a specific prediction time-period. In addition, the proposed technique incorporates the multi-classifier class-combiner approach to address the challenge of a highly skewed class distribution between churners and non-churners. The empirical evaluation results suggest that the proposed call-behavior-based churn-prediction technique exhibits satisfactory predictive effectiveness when more recent call details are employed for the churn prediction model construction. Furthermore, the proposed technique is able to demonstrate satisfactory or reasonable predictive power within the one-month interval between model construction and churn prediction. Using a previous demographics-based churn-prediction system as a reference, the lift factors attained by our proposed technique appear largely satisfactory. © 2002 Elsevier Science Ltd. All rights reserved.},
  doi = {10.1016/S0957-4174(02)00030-1},
  journal = {Expert Systems with Applications},
  bibtexkey = {Wei2002103},
  author = {Wei, C.-P., Chiu, I.-T.},
  source = {SCOPUS}
}

@{,
  category = {INCOLLECTION},
  year = {2019},
  title = {Fuzzy Clustering with Ensemble Classification Techniques to Improve the Customer Churn Prediction in Telecommunication Sector},
  abstract = {Retention of customer improves the profit growth of a marketing firm. Customer retention is the process of identifying and retaining the customers who are about to slide from one brand to another on any dissatisfaction. Customer Correlation Management (CCM) process helps the organization in this retention of customer. A company which performs the retention of customer efficiently is achieving a high market value. Nowadays, customer retention is highly required in the telecommunication systems as the sliding of customers from one provider to the other is increasing. Earlier this customer retention was made using single classification techniques which were less efficient than the hybrid models. Clustering the customers who all belong to similar characteristics into groups and then classifying each group is called the hybrid model. Further to improve the classification in our hybrid model, ensemble techniques are used, which are more efficient. In the first phase, fuzzy-based clustering methods such as Fuzzy C-Means (FCM), Possibility C-Means (PCM), and Possibility Fuzzy C-Means (PFCM) are used for clustering the customers into groups. In the second phase, those clustered groups are partitioned into training and testing data using holdout. In the third phase, the training data are given to ensemble models like bagging, boosting, and Random Subspace (RS) algorithms for building the model. The test data are predicted based on the majority voting, provided by the ensemble techniques. Through this analysis, it is found that the proposed fuzzy clustering with ensemble classification techniques provides more accuracy than single classifier and clustering with base classifier.},
  doi = {10.1007/978-981-13-1280-9_25},
  bibtexkey = {kalita_fuzzy_2019},
  author = {Vijaya, J., Sivasankar, E., Gayathri, S.},
  source = {SPRINGER}
}

@{,
  category = {INCOLLECTION},
  year = {2012},
  title = {Improving Customer Churn Prediction by Data Augmentation Using Pictorial Stimulus-Choice Data},
  abstract = {The purpose of this paper is to determine the added value of pictorial stimulus-choice data in customer churn prediction. Using Random Forests and 5 times 2 fold cross-validation, this study analyzes how much pictorial stimulus – choice data and survey data increase the AUC of a churn model over and above administrative, operational and complaints data. The finding is that pictorial-stimulus choice data significantly increases AUC of models with administrative and operational data. The practical implication of this finding is that companies should start considering mining pictorial data from social media sites (e.g. Pinterest), in order to augment their internal customer database. This study is original in that it is the first that assesses the added value of pictorial stimulus-choice data in predictive models. This is important because more and more social media websites are focusing on pictures.},
  doi = {10.1007/978-3-642-30864-2_21},
  bibtexkey = {casillas_improving_2012},
  author = {Ballings, Michel, Van den Poel, Dirk, Verhagen, Emmanuel},
  source = {SPRINGER}
}

@{,
  category = {INCOLLECTION},
  year = {2016},
  title = {Customer Churn Models: A Comparison of Probability and Data Mining Approaches},
  abstract = {As markets become increasingly saturated, astute companies acknowledge that their business strategies should focus on identifying those customers who are likely to churn (Hadden, Tiwari, Roy, & Ruta, 2007). Since net returns on investments for retention strategies are generally higher than for acquisitions, it is generally accepted that companies should concentrate their marketing resources to keep existing customers rather than to attract new ones (Colgate & Danaher, 2000). This calls for models capable of making accurate predictions about consumers’ behavior in a future time period. Such models should be able to specify which customers in a dataset have a higher probability to churn in a given future time period. Literature on churn modeling reveals that predictive models fall into one of two categories, namely probability modeling and data mining modeling. Although many studies from both of these streams have focused on developing models to predict and identify customer churn, to the best of our knowledge, none of them have compared the performance of these modeling approaches in terms of accuracy in identifying and predicting customer churn.},
  doi = {10.1007/978-3-319-24184-5_35},
  bibtexkey = {campbell_customer_2016},
  author = {Jahromi, Ali Tamaddoni, Stakhovych, Stanislav, Ewing, Michael},
  source = {SPRINGER}
}

@{,
  category = {ARTICLE},
  year = {2019},
  title = {Hybrid PPFCM-ANN model: an efficient system for customer churn prediction through probabilistic possibilistic fuzzy clustering and artificial neural network},
  abstract = {A vital issue for customer correlation management and consumer conservation in the telecommunications business is increased customer churn. The data mining approaches can aid in the prediction of churn behavior of consumers. This article aims to propose a system to predict customer churn through hybrid probabilistic possibilistic fuzzy C-means clustering (PPFCM) along with artificial neural network (PPFCM-ANN). This paper comprises of two modules: (1) proposing clustering component on the basis of PPFCM and (2) churn prediction component on the basis of ANN. The input dataset is gathered into clusters, with the help of probabilistic possibilistic fuzzy C-means clustering algorithm in the clustering module. The obtained clustered information is used in the artificial neural network, and this hybrid construction is further used in the churn prediction module. During the testing process, the clustered test data select the most accurate ANN classifier which corresponds to the closest cluster of the test data, according to minimum distance or similarity measures. Finally, to predict the churn customer the output score value is utilized. Three sets of experiments are carried out: the primary set of experiments comprises PPFCM clustering algorithm, the secondary set assesses the classification result, and the third set authenticates the proposed hybrid model presentation. The proposed hybrid PPFCM-ANN model provides maximum accuracy when compared to any single model.},
  doi = {10.1007/s00521-018-3548-4},
  journal = {Neural Computing and Applications},
  bibtexkey = {sivasankar_hybrid_2019},
  author = {Sivasankar, E., Vijaya, J.},
  source = {SPRINGER}
}

@{,
  category = {INCOLLECTION},
  year = {2010},
  title = {Ensembles of Probability Estimation Trees for Customer Churn Prediction},
  abstract = {Customer churn prediction is one of the most important elements of a company’s Customer Relationship Management (CRM) strategy. In this study, two strategies are investigated to increase the lift performance of ensemble classification models, i.e. (i) using probability estimation trees (PETs) instead of standard decision trees as base classifiers, and (ii) implementing alternative fusion rules based on lift weights for the combination of ensemble member’s outputs. Experiments are conducted for four popular ensemble strategies on five real-life churn data sets. In general, the results demonstrate how lift performance can be substantially improved by using alternative base classifiers and fusion rules. However, the effect varies for the different ensemble strategies. In particular, the results indicate an increase of lift performance of (i) Bagging by implementing C4.4 base classifiers, (ii) the Random Subspace Method (RSM) by using lift-weighted fusion rules, and (iii) AdaBoost by implementing both.},
  doi = {10.1007/978-3-642-13025-0_7},
  bibtexkey = {garcia-pedrajas_ensembles_2010},
  author = {De Bock, Koen W., Van den Poel, Dirk},
  source = {SPRINGER}
}

@{,
  category = {INCOLLECTION},
  year = {2008},
  title = {Customer Churn Time Prediction in Mobile Telecommunication Industry Using Ordinal Regression},
  abstract = {Customer churn in considered to be a core issue in telecommunication customer relationship management (CRM). Accurate prediction of churn time or customer tenure is important for developing appropriate retention strategies. In this paper, we discuss a method based on ordinal regression to predict churn time or tenure of mobile telecommunication customers. Customer tenure is treated as an ordinal outcome variable and ordinal regression is used for tenure modeling. We compare ordinal regression with the state-of-the-art methods for tenure prediction - survival analysis. We notice from our results that ordinal regression could be an alternative technique for survival analysis for churn time prediction of mobile customers. To the best knowledge of authors, the use of ordinal regression as a potential technique for modeling customer tenure has been attempted for the first time.},
  doi = {10.1007/978-3-540-68125-0_88},
  bibtexkey = {washio_customer_2008},
  author = {Gopal, Rupesh K., Meher, Saroj K.},
  source = {SPRINGER}
}

@{,
  category = {INCOLLECTION},
  year = {2020},
  title = {Rule-Based Customer Churn Prediction Model Using Artificial Neural Network Based and Rough Set Theory},
  abstract = {Customer Churn can also be your best friend to make better marketing decisions. Identifying early who is at risk to leave is the best way to segment your campaigns to re-engage these users effectively and in time because it is more expensive and time-consuming acquiring new customers than retaining old ones. In this modern age of digitization, everyone uses online services for various day-to-day activities provided by numerous E-commerce websites. The consumers write reviews which explain and help other companies to the happiness of customers. This paper proposed a rule-based customer churn prediction model using artificial neural network based and rough set theory on customer reviews dataset. The utilization of presented framework help companies in making intelligent decision support system.},
  doi = {10.1007/978-981-15-0751-9_9},
  bibtexkey = {pant_rule-based_2020},
  author = {Kumar, Harvendra, Yadav, Rakesh Kumar},
  source = {SPRINGER}
}

@{,
  category = {INCOLLECTION},
  year = {2009},
  title = {Customer Churn Prediction for Broadband Internet Services},
  abstract = {Although churn prediction has been an area of research in the voice branch of telecommunications services, more focused studies on the huge growth area of Broadband Internet services are limited. Therefore, this paper presents a new set of features for broadband Internet customer churn prediction, based on Henley segments, the broadband usage, dial types, the spend of dial-up, line-information, bill and payment information, account information. Then the four prediction techniques (Logistic Regressions, Decision Trees, Multilayer Perceptron Neural Networks and Support Vector Machines) are applied in customer churn, based on the new features. Finally, the evaluation of new features and a comparative analysis of the predictors are made for broadband customer churn prediction. The experimental results show that the new features with these four modelling techniques are efficient for customer churn prediction in the broadband service field.},
  doi = {10.1007/978-3-642-03730-6_19},
  bibtexkey = {pedersen_customer_2009},
  author = {Huang, B. Q., Kechadi, M-T., Buckley, B.},
  source = {SPRINGER}
}

@{,
  category = {ARTICLE},
  year = {2018},
  title = {In Pursuit of Enhanced Customer Retention Management: Review, Key Issues, and Future Directions},
  abstract = {In today’s turbulent business environment, customer retention presents a significant challenge for many service companies. Academics have generated a large body of research that addresses part of that challenge—with a particular focus on predicting customer churn. However, several other equally important aspects of managing retention have not received similar level of attention, leaving many managerial problems not completely solved, and a program of academic research not completely aligned with managerial needs. Therefore, our goal is to draw on previous research and current practice to provide insights on managing retention and identify areas for future research. This examination leads us to advocate a broad perspective on customer retention. We propose a definition that extends the concept beyond the traditional binary retain/not retain view of retention. We discuss a variety of metrics to measure and monitor retention. We present an integrated framework for managing retention that leverages emerging opportunities offered by new data sources and new methodologies such as machine learning. We highlight the importance of distinguishing between which customers are at risk and which should be targeted—as they are not necessarily the same customers. We identify trade-offs between reactive and proactive retention programs, between short- and long-term remedies, and between discrete campaigns and continuous processes for managing retention. We identify several areas of research where further investigation will significantly enhance retention management.},
  doi = {10.1007/s40547-017-0080-0},
  journal = {Customer Needs and Solutions},
  bibtexkey = {ascarza_pursuit_2018},
  author = {Ascarza, Eva, Neslin, Scott A., Netzer, Oded, Anderson, Zachery, Fader, Peter S., Gupta, Sunil, Hardie, Bruce G. S., Lemmens, AurÃ©lie, Libai, Barak, Neal, David, Provost, Foster, Schrift, Rom},
  source = {SPRINGER}
}

@{,
  category = {INCOLLECTION},
  year = {2008},
  title = {Support Vector Machines for Predicting Customer Activity and Future Best Customers in Non-Contractual Settings},
  abstract = {The Pareto/NBD and the BG/NBD models owe their names to their underlying distributional assumptions, which emphasizes the strong theoretical foundation of the models. Yet, the last chapter showed that they do not outperform simple management heuristics. In fact, even back in the late 1960s, Tukey (1969) has already postulated that putting too much emphasis on the mathematical theories of statistics did not help in solving the real world problems. It was his mantra that statistical work is detective work and that one should let the data speak for itself. The branch of exploratory data analysis emerged, but was dismissed by mathematical statisticians for a long period of time. Many of them proclaimed that proper statistical analysis must be based on hypothesis and distributional assumptions. Their argument was that looking at data before formulating a scientific hypothesis would bias the hypothesis towards what the data might show. The term data mining typically was used in a derogatory connotation. The argument culminated in the reproach of improper scientific use, the reproach of torturing the data until it confesses everything.},
  doi = {10.1007/978-3-8349-8121-9_5},
  bibtexkey = {wubben_support_2008},
  author = {NA},
  source = {SPRINGER}
}

@{,
  category = {INCOLLECTION},
  year = {2008},
  title = {Supervised Classification for Decision Support in Customer Relationship Management},
  abstract = {Supervised classification embraces theories and algorithms for disclosing patterns within large, heterogeneous data streams. Several empirical experiments in various domains including medical diagnosis, drug design, document and image classification as well as text recognition have proven its effectiveness to solve complex forecasting and identification tasks. This paper considers applications of classification within the scope of customer relationship management (CRM). Representative operational planning tasks are reviewed to describe the potential and limitations of classification analysis. To that end, a survey of the relevant literature is given to summarize the body of knowledge in each field and identify similarities across applications. The discussion provides a general understanding of technical and managerial challenges encountered in typical CRM applications and indicates promising areas for future research.},
  doi = {10.1007/978-3-8349-9777-7_14},
  bibtexkey = {bortfeldt_supervised_2008},
  author = {Lessmann, Stefan, VoÃŸ, Stefan},
  source = {SPRINGER}
}

@{,
  category = {INCOLLECTION},
  year = {2017},
  title = {Machine Learning and Big Data},
  abstract = {The last 10 years saw a remarkable increase of data available to marketers. What was considered 5 years ago as big data (see e.g., Vol. I, Sect. 3.5.6) was based on hundreds of thousands of observations (e.g., Reimer et al. 2014). Today this is considered by data scientists as an average sample size. Consumer handscan panels and regular brand tracking allowed us to build models combining consumer actions with mindset metrics (e.g., Hanssens et al. 2014; Van Heerde et al. 2008). And of course, click stream data inspired a brave new world of modeling (prospective) customers’ online decision journeys (Bucklin and Sismeiro 2003; Pauwels and Van Ewijk 2014). Within the last two years we see social media interaction data coming more and more into marketing research (see e.g., Borah and Tellis 2016; Ilhan et al. 2016). Through this source marketers get access to customer and user generated content. Especially service and fast mover consumer goods as entertainment brands enjoy a high volume of interactions (see e.g., Henning-Thurau et al. 2014) in different social media channels that is linked with the company’s future sales performance. Social media again boosts the number of data available to marketers. Instead of now facing hundreds of thousands of observations, researchers are very likely to encounter millions of comments, likes and shares in even short observation periods. Combining this new data with existing sources, will provide new opportunities and further develop marketing research (Sudhir 2016). Verhoef et al. (2016) provide many examples how big data (analytics) can be used to create value for customers and firms.},
  doi = {10.1007/978-3-319-53469-5_19},
  bibtexkey = {leeflang_machine_2017},
  author = {KÃ¼bler, Raoul V., Wieringa, Jaap E., Pauwels, Koen H.},
  source = {SPRINGER}
}

@{,
  category = {ARTICLE},
  year = {2017},
  title = {A churn prediction model for prepaid customers in telecom using fuzzy classifiers},
  abstract = {The incredible growth of telecom data and fierce competition among telecommunication operators for customer retention demand continues improvements, both strategically and analytically, in the current customer relationship management (CRM) systems. One of the key objectives of a typical CRM system is to classify and predict a group of potential churners form a large set of customers to devise profitable and targeted retention campaigns for keeping a long-term relationship with valued customers. For achieving the aforementioned objective, several churn prediction models have been proposed in the past for the accurate identification of the customers who are prone to churn. However, these previously proposed models suffer from a number of limitations which place strong barriers towards the direct applicability of such models for accurate prediction. Firstly, the feature selection methods adopted in majority of the past work neglected the information rich variables present in call details record for model development. Secondly, selection of important features was done through statistical methods only. Although statistical methods have been applied successfully in diverse domains, however, these methods alone without the augmentation of domain knowledge have the tendency to yield erroneous results. Thirdly, the previous models have been validated mainly with benchmark datasets which do not provide a true representation of real world telecom data consisting of noise and large number of missing values. Fourthly, the evaluation measures used in the past neglected the True Positive (TP) rate, which actually highlights the ability of a model to correctly classify the percentage of churners as compared to non-churners. Finally, the classifiers used in the previous models completely neglected the use of fuzzy classification methods which perform reasonably well for data sets with noise. In this paper, a fuzzy based churn prediction model has been proposed and validated using a real data from a telecom company in South Asia. A number of predominant classifiers namely, Neural Network, Linear regression, C4.5, SVM, AdaBoost, Gradient Boosting and Random Forest have been compared with fuzzy classifiers to highlight the superiority of fuzzy classifiers in predicting the accurate set of churners.},
  doi = {10.1007/s11235-017-0310-7},
  journal = {Telecommunication Systems},
  bibtexkey = {azeem_churn_2017},
  author = {Azeem, Muhammad, Usman, Muhammad, Fong, A. C. M.},
  source = {SPRINGER}
}

@{,
  category = {INCOLLECTION},
  year = {2011},
  title = {Applying Fuzzy Data Mining to Telecom Churn Management},
  abstract = {Customers tend to change telecommunications service providers in pursuit of more favorable telecommunication rates. Therefore, how to avoid customer churn is an extremely critical topic for the intensely competitive telecommunications industry. To assist telecommunications service providers in effectively reducing the rate of customer churn, this study used fuzzy data mining to determine effective marketing strategies by analyzing the responses of customers to various marketing activities. These techniques can help telecommunications service providers determine the most appropriate marketing opportunities and methods for different customer groups, to reduce effectively the rate of customer turnover.},
  doi = {10.1007/978-3-642-18129-0_41},
  bibtexkey = {chen_applying_2011},
  author = {Liao, Kuo-Hsiung, Chueh, Hao-En},
  source = {SPRINGER}
}

@{,
  category = {INCOLLECTION},
  year = {2019},
  title = {Marketing Meets Data Science: Bridging the Gap},
  abstract = {It is certain that computer science is completely reformulating the way that business is being conducted around the world. We are witnessing the increasing availability of large volumes of data together with the advances in artificial intelligence, machine learning and optimization techniques. Breakthroughs in statistics, discrete applied mathematics and new algorithms are leading to the development of a new interdisciplinary field: data science. The purpose of this chapter is to provide a bridge, a short-cut to understand some of the questions that computer science deals with in a context of developing new techniques to get knowledge from data.},
  doi = {10.1007/978-3-030-06222-4_1},
  bibtexkey = {moscato_marketing_2019},
  author = {Moscato, Pablo, de Vries, Natalie Jane},
  source = {SPRINGER}
}

@{,
  category = {INCOLLECTION},
  year = {2018},
  title = {Data Analytics and Its Application in Various Industries},
  abstract = {Data analytics has become part and parcel of any business in today’s world. In fact, it has evolved into an industry in itself. Vast numbers of software platforms are available for data extraction, scrubbing, analysis, and visualization. Some of these platforms are specialized for carrying out one of the above-listed aspects of data analytics, while others offer a generalist tool to carry out almost all tasks ranging from data scrubbing to visualization. Of these platforms, SAS® and R are the most popular for data analytics with a large global clientele.},
  doi = {10.1007/978-1-4842-3525-6_1},
  bibtexkey = {gupta_data_2018},
  author = {Gupta, Deepti},
  source = {SPRINGER}
}

@{,
  category = {ARTICLE},
  year = {2020},
  title = {Consumer dynamics: theories, methods, and emerging directions},
  abstract = {Consumer attitudes and behaviors are fundamentally dynamic processes; thus, understanding consumer dynamics is crucial for truly understanding consumer behaviors and for firms to formulate appropriate actions. Recent history in empirical marketing research has enjoyed increasingly richer consumer data as the result of technology and firms’ conscious data collection efforts. Richer data, in turn, have propelled the development and application of quantitative methods in modeling consumer dynamics, and have contributed to the understanding of complex dynamic behaviors across many domains. In this paper, we discuss the sources of consumer dynamics and how our understanding in this area has improved over the past four decades. Accordingly, we discuss several commonly used empirical methods for conducting dynamics research. Finally, as the data evolution continues into new forms and new environments, we identify cutting-edge trends and domains, and offer directions for advancing the understanding of consumer dynamics in these emerging areas.},
  doi = {10.1007/s11747-020-00720-8},
  journal = {Journal of the Academy of Marketing Science},
  bibtexkey = {zhang_consumer_2020},
  author = {Zhang, Jonathan Z., Chang, Chun-Wei},
  source = {SPRINGER}
}

@{,
  category = {INCOLLECTION},
  year = {2010},
  title = {Rule Extraction from Support Vector Machine Using Modified Active Learning Based Approach: An Application to CRM},
  abstract = {Despite superior generalization performance Support vector machines (SVMs) generate black box models. The process of converting such opaque models into transparent model is often regarded as rule extraction. This paper presents a new approach for rule extraction from SVMs using modified active learning based approach (mALBA), to predict churn in bank credit cards. The dataset is obtained from Business Intelligence Cup 2004, which is highly unbalanced with 93% loyal and 7% churned customers’ data. Since identifying churner is paramount from business perspective, therefore considering sensitivity alone, the empirical results suggest that the proposed rule extraction approach using mALBA yielded the best sensitivity compared to other classifiers.},
  doi = {10.1007/978-3-642-15387-7_50},
  bibtexkey = {hutchison_rule_2010},
  author = {Hutchison, David, Kanade, Takeo, Kittler, Josef, Kleinberg, Jon M., Mattern, Friedemann, Mitchell, John C., Naor, Moni, Nierstrasz, Oscar, Pandu Rangan, C., Steffen, Bernhard, Sudan, Madhu, Terzopoulos, Demetri, Tygar, Doug, Vardi, Moshe Y., Weikum, Gerhard, Farquad, M. A. H., Ravi, V., Raju, S. Bapi},
  source = {SPRINGER}
}

@{,
  category = {INCOLLECTION},
  year = {2012},
  title = {A Graph-Based Churn Prediction Model for Mobile Telecom Networks},
  abstract = {With the ever-increasing demand to retain the existing customers with the service provider and to meet up the competition between various telecom operators, it is imperative to identify the number of visible churners in advance, arbitrarily in telecom networks. In this paper, we consider this issue as a social phenomenon introduced to mathematical solution rather than a simple mathematical process. So, we explore the application of graph parameter analysis to the churner behavior. Initially, we try to analyze the graph parameters on a network that is best suited for node level analysis. Machine learning and Statistical techniques are run on the obtained graph parameters from the graph DB to select the most significant parameters towards the churner prediction. The proposed novel churn prediction methodology is finally perceived by constructing a linear model with the relevant list of graph parameters that works in a dynamic and a scalable environment. We have measured the performance of the proposed model on different datasets related to the telecom domain and also compared with our earlier successful models.},
  doi = {10.1007/978-3-642-35527-1_31},
  bibtexkey = {hutchison_graph-based_2012},
  author = {Saravanan, M., Vijay Raajaa, G. S.},
  source = {SPRINGER}
}

@{,
  category = {INCOLLECTION},
  year = {2008},
  title = {The Predictive Modeling Process},
  abstract = {The third cornerstone of database marketing (the other two being LTV and testing) is predictive modeling. Predictive modeling is the use of statistical methods to predict customer behavior — e.g., will the customer respond to this offer or catalog? Will the customer churn in the next 2 months? Which product in our product line would be most attractive to the customer? Which sales channel will the customer use if we send the customer an email? Predictive modeling first and foremost is a process, consisting of defining the problem, preparing the data, estimating the model, evaluating the model, and selecting customers to target.We discuss the process in depth, and conclude with a review of some important long-term considerations related to predictive modeling.},
  doi = {10.1007/978-0-387-72579-6_10},
  bibtexkey = {blattberg_predictive_2008},
  author = {Blattberg, Robert C., Kim, Byung-Do, Neslin, Scott A.},
  source = {SPRINGER}
}

@{,
  category = {INCOLLECTION},
  year = {2020},
  title = {Call Churn Prediction with PySpark},
  abstract = {Different markets over the world are ending up progressively more saturated, with an ever-increasing number of customers swapping their enrolled benefits between contending organizations. Consequently, organizations have understood that they should center their promoting endeavors in client maintenance instead of client procurement. It limits client surrender by foreseeing which clients are probably going to cross out a membership to an administration. In spite of the fact that initially utilized inside the telecommunication business, it has turned out to be regular practice crosswise over banks, ISPs, insurance firms and other verticals. In this paper, an end to end churn prediction is done in view of client call information records. We take a gander at what sorts of client information are normally utilized, do some preparatory investigation of the information and create churn prediction models with PySpark. PySpark processes huge datasets at minimal time and when it comes to the synchronization points as well as errors, framework easily handles at the back end. The PySpark API takes advantage of Spark to deliver dramatic improvements in processing speed for large sets of data.
},
  doi = {10.1007/978-981-15-0184-5_67},
  bibtexkey = {das_call_2020},
  author = {Nonghuloo, Mark Sheridan, Aravind Reddy, Rangapuram, Manideep, Ganji, SarathVamsi, M. R., Lavanya, K.},
  source = {SPRINGER}
}

@{,
  category = {INCOLLECTION},
  year = {2013},
  title = {Preventing Churn in Telecommunications: The Forgotten Network},
  abstract = {This paper outlines an approach developed as a part of a company-wide churn management initiative of a major European telecom operator. We are focusing on explanatory churn model for the postpaid segment, assuming that the mobile telecom network, the key resource of operators, is also a churn driver in case it under delivers to customers’ expectations. Typically, insights generated by churn models are deployed in marketing campaigns; our model’s insights are used in network optimization in order to remove the key network related churn drivers and therefore prevent churn, rather than cure it. The insights generated by the model have caused a paradigm shift in managing the network with the operator where the research was conducted.},
  doi = {10.1007/978-3-642-41398-8_31},
  bibtexkey = {hutchison_preventing_2013},
  author = {Radosavljevik, Dejan, van der Putten, Peter},
  source = {SPRINGER}
}

@{,
  category = {INCOLLECTION},
  year = {2017},
  title = {Towards a Large Scale Practical Churn Model for Prepaid Mobile Markets},
  abstract = {Communication Service Providers (CSPs) are increasingly focusing on effective churn management strategies due to fierce competition, price wars and high subscriber acquisition cost. Though there are many existing studies utilizing data mining techniques for building churn prediction models, addressing aspects such as large data volumes and high dimensionality of subscriber data is challenging in practice. In this paper, we present a large scale churn management system, utilizing dimensionality reduction and a decision-tree ensemble learning. We consider subscriber behavior trends as well as aggregated key performance indicators (KPIs) as features and use a combination of feature selection strategies for dimensionality reduction. We report favorable results from evaluating our model on a real-world dataset consisting of approximately 5 million active subscribers from a popular Asian CSP. The model output is presented as an actionable lift chart which will help marketers in deciding the target subscriber base for retention campaigns. We also present the practical aspects involved in productionizing our model using the Apache Oozie workflow engine.},
  doi = {10.1007/978-3-319-62701-4_8},
  bibtexkey = {perner_towards_2017},
  author = {Meher, Amit Kumar, Wilson, Jobin, Prashanth, R.},
  source = {SPRINGER}
}

@{,
  category = {INCOLLECTION},
  year = {2010},
  title = {Bayesian Network Approach to Predict Mobile Churn Motivations: Emphasis on General Bayesian Network, Markov Blanket, and What-If Simulation},
  abstract = {As mobile telecommunication service becomes indispensable to our daily life, predicting the reasons of mobile churn seems essential from the perspective of mobile service providers. Previous studies have been focused on mobile churn prediction itself, not churn motivations which can play as a good indicator to forecasting real churn. Therefore, main focus of this study is placed on predicting mobile churn motivations, instead of mobile churn prediction. We propose BN approach to predict mobile churn motivation, adopting three types of BN models such as Naïve BN (NBN), Tree Augmented NBN (TAN), and General BN (GBN). To prove its validity in predicting mobile churn motivations, benchmarking classifiers were adopted and their performance was compared with BN classifiers. Through analyzing the empirical results, we found three advantages of GBN-(1) GBN performance is competitive compared with other benchmarking classifiers, (2) Markov Blanket (MB) variables are considerably small in number and make it handy for decision makers, and (3) what-if simulation is possible, which is not possible in other benchmarking classifiers. Practical implications of empirical results were addressed.},
  doi = {10.1007/978-3-642-17569-5_30},
  bibtexkey = {kim_bayesian_2010},
  author = {Lee, Kun Chang, Jo, Nam Yong},
  source = {SPRINGER}
}

@{,
  category = {INCOLLECTION},
  year = {2018},
  title = {AI Capabilities Framework},
  abstract = {This chapter introduces Burgess’s AI Capability Framework. It explains why this approach is important for executives to be able to understand and exploit AI. Each of the capabilities are described and explained in turn, including the relationships and dependencies between them. Technical jargon is kept to an absolute minimum to ensure that executives are able to understand and appreciate the uses and benefits. Real–life examples are provided to bring each of the capabilities to life. The final capability (understanding) is explained in the context of Artificial General Intelligence.},
  doi = {10.1007/978-3-319-63820-1_3},
  bibtexkey = {burgess_ai_2018},
  author = {Burgess, Andrew},
  source = {SPRINGER}
}

@{,
  category = {INCOLLECTION},
  year = {2011},
  title = {Rough Sets for Modeling Interstate Conflict},
  abstract = {This chapter applies the rough set technique to model militarized interstate disputes. One aspect of modeling using rough sets is the issue of granulizing the input data. In this chapter, two granulization techniques are introduced, implemented, and compared. These are the equal-width-bin and equal-frequency-bin partitioning techniques. The rough set model is also compared to the neuro-fuzzy model introduced in  Chap. 6. The results obtained demonstrate that equal-width-bin partitioning gives better accuracy than equal-frequency-bin partitioning. However, both techniques were found to give less accurate results than neuro-fuzzy sets. Also, they were found to be more transparent than neuro-rough sets. Furthermore, it is observed that the rules generated from the rough sets are linguistic and easy-to-interpret in comparison with the ones generated from the neuro-fuzzy model.},
  doi = {10.1007/978-0-85729-790-7_7},
  bibtexkey = {marwala_rough_2011},
  author = {Marwala, Tshilidzi, Lagazio, Monica},
  source = {SPRINGER}
}

@{,
  category = {ARTICLE},
  year = {2020},
  title = {Optimum profit-driven churn decision making: innovative artificial neural networks in telecom industry},
  abstract = {Knowledge-based churn prediction and decision making is invaluable for telecom companies due to highly competitive markets. The comprehensiveness and action ability of a data-driven churn prediction system depend on the effective extraction of hidden patterns from the data. Generally, data analytics is employed to extrapolate the extracted patterns from the training dataset to the test set. In this study, one more step is taken; the improved prediction performance is attained by capturing the individuality of each customer while discovering the hidden pattern from the train set and then applying all the knowledge to the test set. The proposed churn prediction system is developed using artificial neural networks that take advantage of both self-organizing and error-driven learning approaches (ChP-SOEDNN). We are introducing a new dimension to the study of churn prediction in telecom industry: a systematic and profit-driven churn decision-making framework. The comparison of the ChP-SOEDNN with other techniques shows its superiority regarding both accuracy and misclassification cost. Misclassification cost is a realistic criterion this article introduces to measure the success of a method in finding the best set of decisions that leads to the minimum possible loss of profit. Moreover, ChP-SOEDNN shows capability in devising a cost-efficient retention strategy for each cluster of customers, in addition to strength in dealing with the typical issue of imbalanced class distribution that is common in churn prediction problems.},
  doi = {10.1007/s00521-020-04850-6},
  journal = {Neural Computing and Applications},
  bibtexkey = {jafari-marandi_optimum_2020},
  author = {Jafari-Marandi, Ruholla, Denton, Joshua, Idris, Adnan, Smith, Brian K., Keramati, Abbas

license: MIT

license_type: permissive

branch_name: refs/heads/master