Deep Convolutional Neural Network Based Churn Prediction for Telecommunication Industry

Naseebah Almufadi · Ali Mustafa Qamar

16 citations0/0 files storedComputer Systems Science and Engineering

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Customer churn and segmentation Customer Service Quality and Loyalty AI and HR Technologies

Abstract

Currently, mobile communication is one of the widely used means of communication. Nevertheless, it is quite challenging for a telecommunication company to attract new customers. The recent concept of mobile number portability has also aggravated the problem of customer churn. Companies need to identify beforehand the customers, who could potentially churn out to the competitors. In the telecommunication industry, such identification could be done based on call detail records. This research presents an extensive experimental study based on various deep learning models, such as the 1D convolutional neural network (CNN) model along with the recurrent neural network (RNN) and deep neural network (DNN) for churn prediction. We use the mobile telephony churn prediction dataset obtained from customers-dna.com, containing the data for around 100,000 individuals, out of which 86,000 are non-churners, whereas 14,000 are churned customers. The imbalanced data are handled using undersampling and oversampling. The accuracy for CNN, RNN, and DNN is 91%, 93%, and 96%, respectively. Furthermore, DNN got 99% for ROC.

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References

2013Telecommunication subscribers' churn prediction model using machine learning 2004Services marketing : an Asia-Pacific and Australian perspective 2018Customer Churn Prediction Modelling Based on Behavioural Patterns Analysis using Deep Learning 2020Telecom churn prediction and used techniques, datasets and performance measures: a review 2017High Accuracy Predictive Modelling for Customer Churn Prediction in Telecom Industry 2018Telecommunication Services Churn Prediction - Deep Learning Approach

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2024Factors, Predictability, and Explainability of Mobile Telephony Customer Departure in Telecommunications Companies: A Systematic Review of the Literature 2025RetenNet: A Deployable Machine Learning Pipeline with Explainable AI and Prescriptive Optimization for Customer Churn Management

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