Negative Correlation Learning for Customer Churn Prediction: A Comparison Study

Ali Rodan · Ayham Fayyoumi · Hossam Faris · Jamal Alsakran · Omar S. Al-Kadi

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Customer churn and segmentation Neural Networks and Applications Data Mining and Machine Learning Applications

Abstract

Recently, telecommunication companies have been paying more attention toward the problem of identification of customer churn behavior. In business, it is well known for service providers that attracting new customers is much more expensive than retaining existing ones. Therefore, adopting accurate models that are able to predict customer churn can effectively help in customer retention campaigns and maximizing the profit. In this paper we will utilize an ensemble of Multilayer perceptrons (MLP) whose training is obtained using negative correlation learning (NCL) for predicting customer churn in a telecommunication company. Experiments results confirm that NCL based MLP ensemble can achieve better generalization performance (high churn rate) compared with ensemble of MLP without NCL (flat ensemble) and other common data mining techniques used for churn analysis.

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References

1997A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting 2008Application of data mining techniques in customer relationship management: A literature review and classification 2008Handling class imbalance in customer churn prediction 2005Applying data mining to telecom churn management 2011Customer churn prediction in telecommunications 2005Computer assisted customer churn management: State-of-the-art and future trends 2009Customer churn prediction by hybrid neural networks 2008Model of Customer Churn Prediction on Support Vector Machine

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2020Sequential Feature Selection in Customer Churn Prediction Based on Naive Bayes 2020Predicting Customer Retention using XGBoost and Balancing Methods 2017Customer churn analysis for a software-as-a-service company 2017A Survey of Evolution in Predictive Models and Impacting Factors in Customer Churn 2022Customer Churn Prediction in Telecommunication Industry Using Classification and Regression Trees and Artificial Neural Network Algorithms

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