Customer churn prediction in telecom using big data analytics

Weilong Li · Chujin Zhou

23 citations0/0 files storedIOP Conference Series Materials Science and Engineering

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

Abstract

Abstract Customer churn will cause huge losses to the communication company and has become a real problem. The article uses big data analysis technology to analyse user characteristics of churn customer historical information data, establish a churn prediction model, find users with a higher risk of churn in advance, develop targeted strategies, and carry out a series of retention activities to retrieve them. The paper presents a strategy of user segmentation and piecewise regression to find the highly relevant fields and divide the customers into different groups based on these fields, and then use regression analysis to establish the prediction models for different groups. Online test shows that the model can effectively identify most of the lost customers, effectively reduce the user off-network rate, and improve efficiency and effectiveness than traditional methods.

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References

2018A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees 2019Customer churn prediction in telecom using machine learning in big data platform 2018Customer churn prediction in telecommunication industry using data certainty 2013Telecommunication subscribers' churn prediction model using machine learning 2017Comparison of supervised machine learning techniques for customer churn prediction based on analysis of customer behavior 2017Churn prediction on huge telecom data using hybrid firefly based classification 2018Computing efficient features using rough set theory combined with ensemble classification techniques to improve the customer churn prediction in telecommunication sector

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2021An Enhanced Bank Customers Churn Prediction Model Using A Hybrid Genetic Algorithm And K-Means Filter And Artificial Neural Network 2023Telecom Churn Classification using Scatter Search and Random Forest Classifier 2024An autonomous mixed data oversampling method for AIOT-based churn recognition and personalized recommendations using behavioral segmentation 2024Churn Prediction Based on Customer Segmentation in Banking Industry using Machine Learning Techniques

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