Churn prediction using comprehensible support vector machine: An analytical CRM application

Farquad, M.A.H.; Ravi, Vadlamani; Bapi, Raju S.

doi:10.1016/j.asoc.2014.01.031

Cited by 147 publications

(77 citation statements)

References 33 publications

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“…Farquad [3], A. Rodan [4], Huang Bingquan [5], T. Vafeiadis [6], Huang Ying and T. Kechadi [7], A. Keramati [8] et al In the earlier stages of our research, we have studied the following basic methods used for binary classification: Decision Trees, k-Nearest Neighbors, Support Vector Machines, and Back-Propagation Artificial Neural Networks. We have evaluated model quality over different sets of parameters, such as the number of neurons in a hidden layer in artificial neural networks (Fig.…”

Section: Previous Research and Problem Statementmentioning

confidence: 99%

Combined Optimization and Modified Performance Metrics for Automated Model and Parameter Selection in Telecom Customer Churn Prediction

Alkhasov¹,

Tselykh²

2017

Proceedings of the IV International Research Conference "Information Technologies in Science, Management, Social Sphere and Med

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Section: Previous Research and Problem Statementmentioning

confidence: 99%

Combined Optimization and Modified Performance Metrics for Automated Model and Parameter Selection in Telecom Customer Churn Prediction

Alkhasov¹,

Tselykh²

2017

Proceedings of the IV International Research Conference "Information Technologies in Science, Management, Social Sphere and Med

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“…• A decrease of money misspending, focusing resources on churn candidates instead of the whole customer database, reducing marketing and operational costs [15].…”

Section: The Cost Benefit Analysis Framework For Customer Retentionmentioning

confidence: 99%

“…In the first category, this kind of models tries to not assume that the churn will occur in a given period, determining probabilities of churning up to a number of months, and taking into consideration time-varying covariates [4]. In the latter, we find approaches aiming to predict if a customer decides to churn in the next period, where the most common approaches are based on statistical methods, such as logistic regression [8,23,29], non-parametric statistical models such as M a n u s c r i p t k-nearest neighbor [13], decision trees [39], and other machine learning techniques [15,36]. A review on customer churn prediction modeling can be found in [37].…”

Section: The Cost Benefit Analysis Framework For Customer Retentionmentioning

confidence: 99%

Profit-based feature selection using support vector machines – General framework and an application for customer retention

Maldonado

Flores

Verbraken

et al. 2015

Applied Soft Computing

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A c c e p t e d M a n u s c r i p t  A novel profit-based feature selection method for churn prediction with SVM is presented.  A backward elimination algorithm is performed to maximize the profit of a retention campaign. Our experiments on churn prediction datasets underline the potential of the proposed approaches. *Highlights (for review)Page 2 of 36 A c c e p t e d M a n u s c r i p Effective churners Outflow New customers InflowConsists of N customers with average customer lifetime value CLV.The cost of contacting a customer is f.The cost of an incentive offer is dWould-be churnersClassified as churners (η η η η)Would-be churnersClassified as nonchurners (1-η η η η)Customer Churn Management Campaign AbstractChurn prediction is an important application of classification models that identify those customers most likely to attrite based on their respective characteristics described by e.g. socio-demographic and behavioral variables.Since nowadays more and more of such features are captured and stored in the respective computational systems, an appropriate handling of the resulting information overload becomes a highly relevant issue when it comes to build customer retention systems based on churn prediction models. As a consequence, feature selection is an important step of the classifier construction process. Most feature selection techniques; however, are based on statistically inspired validation criteria, which not necessarily lead to models that optimize goals specified by the respective organization. In this paper we propose a profit-driven approach for classifier construction and simultaneous variable selection based on Support Vector Machines. Experimental results show that our models outperform conventional techniques for feature selection achieving superior performance with respect to business-related goals.

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“…Farquad [4] proposed a hybrid approach to overcome the drawbacks of general SVM model which generates a black box model (i.e., it does not reveal the knowledge gained during training in human understandable form). The hybrid approach contains three phases: In the first phase, SVM-RFE (SVM-recursive feature elimination) is employed to reduce the feature set.…”

Section: Mmentioning

confidence: 99%

A Survey on Customer Churn Prediction using Machine Learning Techniques

Kumar¹,

Chandrakala²

2016

IJCA

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The fast expansion of the market in every sector is leading to superior subscriber base for service providers. Added competitors, novel and innovative business models and enhanced services are increasing the cost of customer acquisition. In such a fast set up, service providers have realized the importance of retaining the on-hand customers. It is therefore essential for the service providers to prevent churn-a phenomenon which states that customer wishes to quit the service of the company. This paper reviews the most popular machine learning algorithms used by researchers for churn predicting, not only in banking sector but also other sectors which highly depends on customer participation.

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Churn prediction using comprehensible support vector machine: An analytical CRM application

Cited by 147 publications

References 33 publications

Combined Optimization and Modified Performance Metrics for Automated Model and Parameter Selection in Telecom Customer Churn Prediction

Combined Optimization and Modified Performance Metrics for Automated Model and Parameter Selection in Telecom Customer Churn Prediction

Profit-based feature selection using support vector machines – General framework and an application for customer retention

A Survey on Customer Churn Prediction using Machine Learning Techniques

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