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Journal articles on the topic 'Churn management'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Pick, Doreén. "Churn Management." WiSt - Wirtschaftswissenschaftliches Studium 45, no. 2 (2016): 60–64. http://dx.doi.org/10.15358/0340-1650-2016-2-60.

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2

Vineeta and Akanksha Bharti. "Customer churn management." ACADEMICIA: An International Multidisciplinary Research Journal 7, no. 5 (2017): 96. http://dx.doi.org/10.5958/2249-7137.2017.00056.8.

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3

Coussement, Kristof. "Improving customer retention management through cost-sensitive learning." European Journal of Marketing 48, no. 3/4 (2014): 477–95. http://dx.doi.org/10.1108/ejm-03-2012-0180.

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Purpose – Retailers realize that customer churn detection is a critical success factor. However, no research study has taken into consideration that misclassifying a customer as a non-churner (i.e. predicting that (s)he will not leave the company, while in reality (s)he does) results in higher costs than predicting that a staying customer will churn. The aim of this paper is to examine the prediction performance of various cost-sensitive methodologies (direct minimum expected cost (DMECC), metacost, thresholding and weighting) that incorporate these different costs of misclassifying customers

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4

Kaya, Sezgin, and Bernard Williams. "Effective churn management for business." Journal of Corporate Real Estate 7, no. 2 (2005): 154–63. http://dx.doi.org/10.1108/14630010510812576.

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5

Adebiyi, Sulaimon Olanrewaju, Emmanuel Olateju Oyatoye, and Bilqis Bolanle Amole. "Improved Customer Churn and Retention Decision Management Using Operations Research Approach." EMAJ: Emerging Markets Journal 6, no. 2 (2017): 12–21. http://dx.doi.org/10.5195/emaj.2016.101.

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The relevance of operations research cannot be overemphasized, as it provides the best possible results in any given circumstance, through analysis of operations and the use of scientific method thus, this paper explore the combination of two operations research models (analytic hierarchy process and Markov chain) for solving subscribers’ churn and retention problem peculiar to most service firms. A conceptual model for unraveling the problem customer churn and retention decision management was proposed and tested with data on third level analysis of AHP for determining appropriate strategies

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Visentin, Marco, and Francesco Lo Vasto. "La gestione delle relazioni con i clienti delle banche retail a vocazione locale: uno strumento di CRM per l'analisi e la previsione dei comportamenti di churn." MERCATI & COMPETITIVITÀ, no. 2 (May 2009): 105–23. http://dx.doi.org/10.3280/mc2009-002006.

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- The changing competitive context of European banking could be an issue also for small locally oriented banks. This sector is facing a growing trend of customer churn that may seriously affect profitability and threat the competitive position of small retail banks. This paper proposes an analysis of customer attrition within the customer base of an Italian Banca di Credito Cooperativo to support the management of small banks in dealing with customer churn. Our work identifies determinants and magnitude of churn dynamics as long as it allows management a tool to select, target and manage CRM p

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Leggat, Sandra, and Cathy Balding. "The impact of leadership churn on quality management in Australian hospitals." Journal of Health Organization and Management 33, no. 7/8 (2019): 809–20. http://dx.doi.org/10.1108/jhom-08-2018-0216.

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Purpose The purpose of this paper is to explore the relationship between frequent turnover (churn) of the chief executive officer (CEO), quality manager and members of the governing board with the management of quality in eight Australian hospitals. Design/methodology/approach A mixed method three-year longitudinal study was conducted using validated quality system scales, quality indicators and focus groups involving over 800 board members, managers and clinical staff. Findings There were unexpected high levels of both governance and management churn over the three years. Churn among CEOs and

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8

Pettersson, Magnus. "SPC with Applications to Churn Management." Quality and Reliability Engineering International 20, no. 5 (2004): 397–406. http://dx.doi.org/10.1002/qre.654.

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9

Ferreira, Jorge Brantes, and Jorge Ferreira da Silva. "Unraveling Wireless Churn." Latin American Business Review 5, no. 4 (2005): 99–112. http://dx.doi.org/10.1300/j140v05n04_05.

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10

Kumar, V., Agata Leszkiewicz, and Angeliki Herbst. "Are you Back for Good or Still Shopping Around? Investigating Customers' Repeat Churn Behavior." Journal of Marketing Research 55, no. 2 (2018): 208–25. http://dx.doi.org/10.1509/jmr.16.0623.

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Service firms develop win-back strategies to rectify issues that cause customer churn and rebuild relationships with lost customers. To better support retention, it is important to understand how the revived relationship evolves and possibly ends again. To examine customers' repeat churn behavior, we develop a “mixture cure-competing risks” model, jointly estimating the duration of second lifetimes, multiple reasons for churn, and heterogeneity of customers in exhibiting a related churn reason. The proposed model is tested using a data set from a large telecommunications provider including inf

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Xu, Tianpei, Ying Ma, and Kangchul Kim. "Telecom Churn Prediction System Based on Ensemble Learning Using Feature Grouping." Applied Sciences 11, no. 11 (2021): 4742. http://dx.doi.org/10.3390/app11114742.

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In recent years, the telecom market has been very competitive. The cost of retaining existing telecom customers is lower than attracting new customers. It is necessary for a telecom company to understand customer churn through customer relationship management (CRM). Therefore, CRM analyzers are required to predict which customers will churn. This study proposes a customer-churn prediction system that uses an ensemble-learning technique consisting of stacking models and soft voting. Xgboost, Logistic regression, Decision tree, and Naïve Bayes machine-learning algorithms are selected to build a

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12

Singh, Hergovind, and Harsh Vardhan Samalia. "A Business Intelligence Perspective for Churn Management." Procedia - Social and Behavioral Sciences 109 (January 2014): 51–56. http://dx.doi.org/10.1016/j.sbspro.2013.12.420.

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Adikari, A. A. R. E., A. T. L. K. Samarasinghe, and S. U. Rabel. "Churn Management in Sri Lankan Mobile Market." Engineer: Journal of the Institution of Engineers, Sri Lanka 41, no. 1 (2008): 37. http://dx.doi.org/10.4038/engineer.v41i1.7082.

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14

Pratt, Keith. "Current space use strategies and churn management." Facilities 15, no. 9/10 (1997): 224–26. http://dx.doi.org/10.1108/02632779710178758.

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15

Hung, Shin-Yuan, David C. Yen, and Hsiu-Yu Wang. "Applying data mining to telecom churn management." Expert Systems with Applications 31, no. 3 (2006): 515–24. http://dx.doi.org/10.1016/j.eswa.2005.09.080.

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16

Ibitoye, Ayodeji, and Olufade Onifade. "Blended Churn Predictive System for Quadruple-Patterned Churn Classification Towards Effective Customer Behavioural Management." International Journal on Data Science and Technology 6, no. 2 (2020): 56. http://dx.doi.org/10.11648/j.ijdst.20200602.12.

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Dechant, Andrea, Martin Spann, and Jan U. Becker. "Positive Customer Churn: An Application to Online Dating." Journal of Service Research 22, no. 1 (2018): 90–100. http://dx.doi.org/10.1177/1094670518795054.

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In the service literature, churn is primarily attributed to customers who are dissatisfied with a service. However, in several industries, such as health care, weight loss services, and online dating, satisfied customers also churn because the service delivers on its promise, for example, by providing a cure, facilitating weight loss, or creating the circumstances that allow a person to meet their partner. Considering these dual churn pathways, it is necessary for companies in these markets to create awareness of what drives positive and negative churn to address the corresponding challenges f

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18

Lai, Yuangen, and Jianxun Zeng. "Analysis of customer churn behavior in digital libraries." Program 48, no. 4 (2014): 370–82. http://dx.doi.org/10.1108/prog-08-2011-0035.

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Purpose – The purpose of this paper is to discuss issues related to customer churn behavior in digital libraries (DLs) and demonstrate the successful application of Survival Analysis for understanding customer churn status and relationship duration distribution between customers and libraries. Design/methodology/approach – The study applies non-parametric methods of Survival Analysis to analyze churn behaviors of 8,054 customers from a famous Chinese digital library, and a cluster method to make customer segmentation according to customer behavioral features. Findings – The customer churn rate

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19

Jamalian, E., and R. Foukerdi. "A Hybrid Data Mining Method for Customer Churn Prediction." Engineering, Technology & Applied Science Research 8, no. 3 (2018): 2991–97. http://dx.doi.org/10.48084/etasr.2108.

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The expenses for attracting new customers are much higher compared to the ones needed to maintain old customers due to the increasing competition and business saturation. So customer retention is one of the leading factors in companies’ marketing. Customer retention requires a churn management, and an effective management requires an exact and effective model for churn prediction. A variety of techniques and methodologies have been used for churn prediction, such as logistic regression, neural networks, genetic algorithm, decision tree etc.. In this article, a hybrid method is presented that p

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20

Zhao, Ming, Qingjun Zeng, Ming Chang, Qian Tong, and Jiafu Su. "A Prediction Model of Customer Churn considering Customer Value: An Empirical Research of Telecom Industry in China." Discrete Dynamics in Nature and Society 2021 (August 7, 2021): 1–12. http://dx.doi.org/10.1155/2021/7160527.

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Customer churn will cause the value flowing from customers to enterprises to decrease. If customer churn continues to occur, the enterprise will gradually lose its competitive advantage. When the growth of new customers cannot meet the needs of enterprise development, the enterprise will fall into a survival dilemma. Focusing on the customer churn prediction model, this paper takes the telecom industry in China as the research object, establishes a customer churn prediction model by using a logistic regression algorithm based on the big data of high-value customer operation in the telecom indu

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21

Rodriguez, Stephen, and Heechang Shin. "Developing customer churn models for customer relationship management." International Journal of Business Continuity and Risk Management 4, no. 4 (2013): 302. http://dx.doi.org/10.1504/ijbcrm.2013.058990.

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22

Kehl, Roger E. "Customer Lifetime Value und Churn Management im Kundenbeziehungsmanagement." Controlling 13, no. 4-5 (2001): 203–10. http://dx.doi.org/10.15358/0935-0381-2001-4-5-203.

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23

Prince, Jeffrey, and Shane Greenstein. "Does Service Bundling Reduce Churn?" Journal of Economics & Management Strategy 23, no. 4 (2014): 839–75. http://dx.doi.org/10.1111/jems.12073.

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Anh, Nguyen Thi Ngoc, Nguyen Danh Tu, Vijender Kumar Solanki, et al. "Integrating Employee Value Model with Churn Prediction." International Journal of Sensors, Wireless Communications and Control 10, no. 4 (2020): 484–93. http://dx.doi.org/10.2174/2210327910666200213123728.

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Background: In recent years, human resource management is a crucial role in every companies or organization’s operation. Loyalty employee or Churn employee influence the operation of the organization. The impact of Churn employees is difference because of their role in organization. Objective: Thus, we define two Employee Value Models (EVMs) of organizations or companies based on employee features that are popular of almost companies. Methods: Meanwhile, with the development of Artificial intelligent, machine learning is possible to give predict data-based models having high accuracy.Thus, int

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Mishachandar, B., and Kakelli Anil Kumar. "Predicting customer churn using targeted proactive retention." International Journal of Engineering & Technology 7, no. 2.27 (2018): 69. http://dx.doi.org/10.14419/ijet.v7i2.27.10180.

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With the advent of innovative technologies and fierce competition, the choices for customers to choose from have increased tremendously in number. Especially in the case of a telecommunication industry, where deregulation is at its peak. Every year a new company springs up offering fitter options for its customers. This has turned the concentration of the business doers on churn prediction and business management models to sustain their places. Businesses approach churn in two ways, one is through targeted customer retention and through cause identification strategy. The literature of this pap

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26

McDonald, Heath. "The Factors Influencing Churn Rates Among Season Ticket Holders: An Empirical Analysis." Journal of Sport Management 24, no. 6 (2010): 676–701. http://dx.doi.org/10.1123/jsm.24.6.676.

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Season ticket holders (STHs) are vital to professional sport club revenue and are purported to be the most loyal and involved of fans. Nonrenewal (churn) rates among STHs, however, often exceed 20%. Low member satisfaction, poor on-field performance and low game attendance have all been posited as explanations of high churn rates, but rarely empirically examined. The research reported here employed a unique study of over 4,500 STHs, incorporating both survey research and measures of actual behavior, to determine which variables best explain and predict churn within two professional sport teams

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27

Kim, Yong Seog, Hyeseon Lee, and John D. Johnson. "Churn management optimization with controllable marketing variables and associated management costs." Expert Systems with Applications 40, no. 6 (2013): 2198–207. http://dx.doi.org/10.1016/j.eswa.2012.10.043.

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28

Meghyasi, Homa, and Abas Rad. "Customer Churn Prediction in Telecommunication Industry Using Data Mining Methods." Innovaciencia Facultad de Ciencias Exactas Físicas y Naturales 8, no. 1 (2020): 1–8. http://dx.doi.org/10.15649/2346075x.999.

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At present, in competitive space between companies and organizations, customers churn is their most important challenge. When a customer becomes churn, organizations lose one of their most important assets, which can lead to financial losses and even bankruptcy. Customer churn prediction using data mining techniques can alleviate these problems to some extent. The aim of the present study is to provide a hybrid method based on Genetic Algorithm and Modular Neural Network to customer churn prediction in telecommunication industries and use Irancell data as a sample. The accuracy result of this

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29

Lemmens, Aurélie, and Sunil Gupta. "Managing Churn to Maximize Profits." Marketing Science 39, no. 5 (2020): 956–73. http://dx.doi.org/10.1287/mksc.2020.1229.

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Kamalakannan, T., and P. Mayilvaghnan. "Optimal customer relationship management in telecalling industry by using data mining and business intelligence." International Journal of Engineering & Technology 7, no. 1.1 (2017): 12. http://dx.doi.org/10.14419/ijet.v7i1.1.8907.

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Decision making system in telecommunication industries plays a more important role where it is required to find customer churn. Customer churn prediction requires finding out and analyzing the information about the business data intelligence techniques which can be done efficiently by adapting the business intelligence techniques. Business intelligence provides tools to predict and analyze the historical, current and predictive views of business operations. However, this would be more complex task with high volume of data which are gathered from million of telephone users for the time being. I

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31

Ahmed, Hussein Moselhy Syead. "The Impact of Customer Churn Factors (CCF) on Customer's Loyalty." International Journal of Customer Relationship Marketing and Management 10, no. 1 (2019): 48–70. http://dx.doi.org/10.4018/ijcrmm.2019010104.

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This article analyzes the impact of customer churn factors on improving the customer loyalty towards telecommunication service providers in Egypt. To accomplish this, a descriptive method is used. 1500 unique e-mails of customers of telecommunication service providers who have used telecommunication services of these providers were randomly selected. With a 25.6% response rate, the questionnaires were distributed through email and self-administered for data collection. Linear regression analysis was used on the responses. The results showed that there is a statistically significant relationshi

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Lee, Eui-Bang, Jinwha Kim, and Sang-Gun Lee. "Predicting customer churn in mobile industry using data mining technology." Industrial Management & Data Systems 117, no. 1 (2017): 90–109. http://dx.doi.org/10.1108/imds-12-2015-0509.

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Purpose The purpose of this paper is to identify the influence of the frequency of word exposure on online news based on the availability heuristic concept. So that this is different from most churn prediction studies that focus on subscriber data. Design/methodology/approach This study examined the churn prediction through words presented the previous studies and additionally identified words what churn generate using data mining technology in combination with logistic regression, decision tree graphing, neural network models, and a partial least square (PLS) model. Findings This study found

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Moser, Sabine, Jan H. Schumann, Florian von Wangenheim, Fabian Uhrich, and Felix Frank. "The Effect of a Service Provider’s Competitive Market Position on Churn Among Flat-Rate Customers." Journal of Service Research 21, no. 3 (2018): 319–35. http://dx.doi.org/10.1177/1094670517752458.

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Flat-rate pricing, as opposed to charging customers for actual usage, dominates many service industries (e.g., telecommunications, health clubs, and music streaming), and customers often express a flat-rate bias and choose flat rates even if a pay-per-use tariff would be less expensive for them. However, evidence of the effect of this bias on churn is mixed. The competitive market position of a service provider may represent a relevant contingency factor related to this effect; building on attribution theory, the current study predicts that customers attribute their flat-rate bias differently,

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34

Popukaylo, V. S. "ANALYSIS AND CHURN PREDICTION OF THE PROJECT MANAGEMENT SYSTEM." ELECTRICAL AND COMPUTER SYSTEMS 27, no. 103 (2018): 280–85. http://dx.doi.org/10.15276/eltecs.27.103.2018.33.

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Lejeune, Miguel A. P. M. "Measuring the impact of data mining on churn management." Internet Research 11, no. 5 (2001): 375–87. http://dx.doi.org/10.1108/10662240110410183.

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36

Lin, Wei-Chao, Chih-Fong Tsai, and Shih-Wen Ke. "Dimensionality and data reduction in telecom churn prediction." Kybernetes 43, no. 5 (2014): 737–49. http://dx.doi.org/10.1108/k-03-2013-0045.

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Purpose – Churn prediction is a very important task for successful customer relationship management. In general, churn prediction can be achieved by many data mining techniques. However, during data mining, dimensionality reduction (or feature selection) and data reduction are the two important data preprocessing steps. In particular, the aims of feature selection and data reduction are to filter out irrelevant features and noisy data samples, respectively. The purpose of this paper, performing these data preprocessing tasks, is to make the mining algorithm produce good quality mining results.

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37

Gustafsson, Anders, Michael D. Johnson, and Inger Roos. "The Effects of Customer Satisfaction, Relationship Commitment Dimensions, and Triggers on Customer Retention." Journal of Marketing 69, no. 4 (2005): 210–18. http://dx.doi.org/10.1509/jmkg.2005.69.4.210.

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In a study of telecommunications services, the authors examine the effects of customer satisfaction, affective commitment, and calculative commitment on retention. The study further examines the potential for situational and reactional trigger conditions to moderate the satisfaction–retention relationship. The results support consistent effects of customer satisfaction, calculative commitment, and prior churn on retention. Prior churn also moderates the satisfaction–retention relationship. The results have implications for both customer relationship managers and researchers who use satisfactio

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Domingos, Edvaldo, Blessing Ojeme, and Olawande Daramola. "Experimental Analysis of Hyperparameters for Deep Learning-Based Churn Prediction in the Banking Sector." Computation 9, no. 3 (2021): 34. http://dx.doi.org/10.3390/computation9030034.

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Until recently, traditional machine learning techniques (TMLTs) such as multilayer perceptrons (MLPs) and support vector machines (SVMs) have been used successfully for churn prediction, but with significant efforts expended on the configuration of the training parameters. The selection of the right training parameters for supervised learning is almost always experimentally determined in an ad hoc manner. Deep neural networks (DNNs) have shown significant predictive strength over TMLTs when used for churn predictions. However, the more complex architecture of DNNs and their capacity to process

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Risselada, Hans, Peter C. Verhoef, and Tammo H. A. Bijmolt. "Staying Power of Churn Prediction Models." Journal of Interactive Marketing 24, no. 3 (2010): 198–208. http://dx.doi.org/10.1016/j.intmar.2010.04.002.

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40

Qian, Zhiguang, Wei Jiang, and Kwok-Leung Tsui. "Churn detection via customer profile modelling." International Journal of Production Research 44, no. 14 (2006): 2913–33. http://dx.doi.org/10.1080/00207540600632240.

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41

Xiao, Jin, Bing Zhu, Geer Teng, Changzheng He, and Dunhu Liu. "One-Step Dynamic Classifier Ensemble Model for Customer Value Segmentation with Missing Values." Mathematical Problems in Engineering 2014 (2014): 1–15. http://dx.doi.org/10.1155/2014/869628.

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Scientific customer value segmentation (CVS) is the base of efficient customer relationship management, and customer credit scoring, fraud detection, and churn prediction all belong to CVS. In real CVS, the customer data usually include lots of missing values, which may affect the performance of CVS model greatly. This study proposes a one-step dynamic classifier ensemble model for missing values (ODCEM) model. On the one hand, ODCEM integrates the preprocess of missing values and the classification modeling into one step; on the other hand, it utilizes multiple classifiers ensemble technology

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42

Botelho, Delane, and Frederico Damian Tostes. "Modelagem de probabilidade de churn." Revista de Administração de Empresas 50, no. 4 (2010): 396–410. http://dx.doi.org/10.1590/s0034-75902010000400005.

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Clientes podem abandonar uma organização ainda que altos investimentos em prospecção e retenção sejam realizados, o que requer diagnóstico e compreensão. Este artigo objetiva modelar a probabilidade de clientes abandonarem o relacionamento com uma organização, fenômeno conhecido como churn, utilizando dados do histórico de relacionamento cliente/empresa, validar o modelo em uma segunda amostra e descrever as possíveis variáveis que influenciam o abandono/permanência do cliente. Utilizou-se o modelo de regressão logística em uma amostra de calibração de 70.000 clientes que possuíam cartão de cr

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Prianto, Didik. "Strategi Manajemen Pelanggan Internet Rumah Pascabayar Berdasarkan Faktor yang Berpengaruh Terhadap Churn." Inovbiz: Jurnal Inovasi Bisnis 8, no. 2 (2020): 128. http://dx.doi.org/10.35314/inovbiz.v8i2.1478.

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In the era of connectivity, the internet industry has significantly increased year by year. The annual compound growth rate (CAGR) of Indonesian internet users is about 21.8% (2007-2017). The penetration rate of Indonesian internet users is about 54.7%, making this industry will be growing up. However, the competition of the internet industry is very high and the company facing the churn of the customer problem. This study aims to analyze the factors that affected customers churn. The result of logistic regression shows that the experience of service blocking, ownership of the residence, chang

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Fathian, Mohammad, Yaser Hoseinpoor, and Behrouz Minaei-Bidgoli. "Offering a hybrid approach of data mining to predict the customer churn based on bagging and boosting methods." Kybernetes 45, no. 5 (2016): 732–43. http://dx.doi.org/10.1108/k-07-2015-0172.

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Purpose – Churn management is a fundamental process in firms to keep their customers. Therefore, predicting the customer’s churn is essential to facilitate such processes. The literature has introduced data mining approaches for this purpose. On the other hand, results indicate that performance of classification models increases by combining two or more techniques. The purpose of this paper is to propose a combined model based on clustering and ensemble classifiers. Design/methodology/approach – Based on churn data set in Cell2Cell, single baseline classifiers, ensemble classifiers are used fo

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Mahajan, Vishal, and Renuka Mahajan. "Variable Selection of Customers for Churn Analysis in Telecommunication Industry." International Journal of Virtual Communities and Social Networking 10, no. 1 (2018): 17–32. http://dx.doi.org/10.4018/ijvcsn.2018010102.

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The telecommunication industry considers customer relationship management as a significant issue for organizational adaptation. Mobile service providers have enforced CRM with the objective to reduce the number of customers that churn. The objective of this article is to detect high impact factors leading to customer churn in the mobile industry over the present-day market situation in Delhi-NCR by using a questionnaire survey and examine their importance. The study is done to understand usage patterns of customers using mobile data services. The data collected was analyzed using descriptive s

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Supangat, Supangat, Mohd Zainuri Bin Saringat, Geri Kusnanto, and Anang Andrianto. "Churn Prediction on Higher Education Data with Fuzzy Logic Algorithm." SISFORMA 8, no. 1 (2021): 22. http://dx.doi.org/10.24167/sisforma.v8i1.3025.

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Colleges are an optional final stage of formal education. However, with time, the Management Section finds the fact that the student churn rate in the university scope becomes a problem. The purpose of this research is to predict whether the student will be churn or loyal in the future, the data will be taken from 2014. The analytical technique used in this research process is the Fuzzy method of C-Means. At a university, the variable that will be tested is the Length of students who are currently attending higher education at the University or campus, when the last student pays enrollment, th

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Gulluoglu, Batuhan, Evren Arifoglu, Adem Karahoca, and Dilek Karahoca. "Customer segmentation for churn management by using ant colony algorithm." Global Journal of Computer Sciences: Theory and Research 10, no. 1 (2020): 18–26. http://dx.doi.org/10.18844/gjcs.v10i1.4749.

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It is extremely important for companies to set customer priorities and act in line with these priorities. The ant colony algorithm is used to perform customer segmentation. To do this, the shortest path approach was chosen. Besides, clustering is done by the Euclidean distance formula in the ant colony algorithm. The customer segmentation attributes are mostly related to the satisfaction factors, but some of them were eliminated by using ranker. These results are mostly related to the customer’s income, tenure, equip call card and reside. These attributes are the most important satisfaction fa

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48

Kim, Namhyoung, Kyu-Hwan Jung, Yong Seog Kim, and Jaewook Lee. "Uniformly subsampled ensemble (USE) for churn management: Theory and implementation." Expert Systems with Applications 39, no. 15 (2012): 11839–45. http://dx.doi.org/10.1016/j.eswa.2012.01.203.

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49

Qi, Jiayin, Li Zhang, Yanping Liu, et al. "ADTreesLogit model for customer churn prediction." Annals of Operations Research 168, no. 1 (2008): 247–65. http://dx.doi.org/10.1007/s10479-008-0400-8.

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Höppner, Sebastiaan, Eugen Stripling, Bart Baesens, Seppe vanden Broucke, and Tim Verdonck. "Profit driven decision trees for churn prediction." European Journal of Operational Research 284, no. 3 (2020): 920–33. http://dx.doi.org/10.1016/j.ejor.2018.11.072.

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