Telecom Churn Prediction based on Deep Learning Approach

Israa Mahmood · Hasanen Samir Abdullah

10 citations0/0 files storedIraqi Journal of Science

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

Abstract

The transition of customers from one telecom operator to another has a direct impact on the company's growth and revenue. Traditional classification algorithms fail to predict churn effectively. This research introduces a deep learning model for predicting customers planning to leave to another operator. The model works on a high-dimensional large-scale data set. The performance of the model was measured against other classification algorithms, such as Gaussian NB, Random Forrest, and Decision Tree in predicting churn. The evaluation was performed based on accuracy, precision, recall, F-measure, Area Under Curve (AUC), and Receiver Operating Characteristic (ROC) Curve. The proposed deep learning model performs better than other prediction models and achieves a high accuracy rate of 91%. Furthermore, it was noticed that the deep learning model outperforms a small size Neural Network for the customer churn prediction.

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References

2017Big Data technologies: A survey 2019A Churn Prediction Model Using Random Forest: Analysis of Machine Learning Techniques for Churn Prediction and Factor Identification in Telecom Sector 2018Customer churn prediction in telecommunication industry using data certainty 2012A Customer Churn Prediction Model in Telecom Industry Using Boosting 2013Social network analysis for customer churn prediction 2013A Neural Network based Approach for Predicting Customer Churn in Cellular Network Services 2015Customer churn analysis in telecom industry 2004Predicting customer behavior in telecommunications

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