The Application of the Locally Linear Model Tree on Customer Churn Prediction

Amineh Ghorbani · Fattaneh Taghiyareh · Caro Lucas

19 citations0/0 files storedInternational Conference of Soft Computing and Pattern Recognition (SoCPaR)

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Customer churn and segmentation Customer Service Quality and Loyalty Data Mining and Machine Learning Applications

Abstract

Acquiring new customers in any business is much more expensive than trying to keep the existing ones. Thus many prediction models are presented to detect churning customers. The objective of this paper was to improve the predictive accuracy and interpretability of churn detection. For this purpose, the application of the locally linear model tree (LOLIMOT) algorithm, which integrates the advantage of neural networks, tree model and fuzzy modeling, was experimented. Applied to the data of a major telecommunication company, the method is found to improve prediction accuracy significantly compared to other algorithms, such as artificial neural networks, decision trees, and logistic regression. The results also indicate that LOLIMOT can have accurate outcome in extremely unbalanced datasets.

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References

2004Valuing Customers 2006Defection Detection: Measuring and Understanding the Predictive Accuracy of Customer Churn Models 2002Data mining techniques for customer relationship management 2004Determinants of subscriber churn and customer loyalty in the Korean mobile telephony market 2005Computer assisted customer churn management: State-of-the-art and future trends 2008Modeling churn using customer lifetime value 2006Toward a hybrid data mining model for customer retention 2005Subscription fraud prevention in telecommunications using fuzzy rules and neural networks

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2013An application of locally linear model tree algorithm with combination of feature selection in credit scoring 2015Customer churn prediction in telecommunication 2022Improved Churn Causal Analysis Through Restrained High-Dimensional Feature Space Effects in Financial Institutions 2011CUSTOMER CHURN PREDICTION USING LOCAL LINEAR MODEL TREE FOR IRANIAN TELECOMMUNICATION COMPANIES

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