A Hybrid Data Mining Method for Customer Churn Prediction

Elham Jamalian · Rahim Foukerdi

30 citations0/0 files storedEngineering Technology & Applied Science Research

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

Abstract

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 predicts customers churn more accurately, using data fusion and feature extraction techniques. After data preparation and feature selection, two algorithms, LOLIMOT and C5.0, were trained with different size of features and performed on test data. Then the outputs of the individual classifiers were combined with weighted voting. The results of applying this method on real data of a telecommunication company proved the effectiveness of the method.

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References

2006Modeling Customer Lifetime Value 2006Defection Detection: Measuring and Understanding the Predictive Accuracy of Customer Churn Models 2005Applying data mining to telecom churn management 2002Turning telecommunications call details to churn prediction: a data mining approach 2003Customer attrition analysis for financial services using proportional hazard models 2008Customer churn prediction using improved balanced random forests 2003An LTV model and customer segmentation based on customer value: a case study on the wireless telecommunication industry 2006Bagging and Boosting Classification Trees to Predict Churn

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2022Applying hybrid machine learning algorithms to assess customer risk-adjusted revenue in the financial industry 2024Customer Churn Prediction for Telecommunication Companies using Machine Learning and Ensemble Methods 2023Marketing Decision Support System Based on Data Mining Technology

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