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Edited by: Editorial Board of Journal of Data Acquisition and Processing
P.O. Box 2704, Beijing 100190, P.R. China
Sponsored by: Institute of Computing Technology, CAS & China Computer Federation
Undertaken by: Institute of Computing Technology, CAS
Published by: SCIENCE PRESS, BEIJING, CHINA
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      09 May 2023, Volume 38 Issue 3
    Article

    CUSTOMER CHURN PREDICTION IN TELECOMMUNICATION INDUSTRY USING DEEP LEARNING
    Mr. Abhinav Sudhir Thorat, Dr. Vijay Ramnath Sonawane
    Journal of Data Acquisition and Processing, 2023, 38 (3): 1417-1425 . 

    Abstract

    Customer churn prediction in the telecommunication industry is a critical task for businesses as it affects their revenues. Customers churn when they decide to switch to a different service provider after perceiving that it is more lucrative to do so. Deep learning offers powerful approaches for accurately predicting and preventing customer churn in the telecommunication industry. Deep learning algorithms can be used to develop models that can take into account large datasets and complex customer behavior which enables better accuracy in predicting customer churn. The deep learning models can take into account various factors such as customer behavior, location, frequency of customer contact, customer demographic, credit rating, customer loyalty, and customer needs. By analyzing these factors, a deep learning model can accurately identify customer with a high risk of churn and accurately predict the probability of churn. This model can be used to take proactive action to retain customers with high probability of churn and offer them better deals and offers to reduce the probability of them leaving the service. Deep learning can also be used to identify the drivers of customer churn and segment customer churn into different categories. This will help the service providers to identify what factors are driving customer churn and take action accordingly. Overall, deep learning can be an effective tool for the telecommunication industry in order to predict and prevent customer churn. It offers accurate insights into customer behaviour and helps to identify personalized targeted actions to prevent customer churn and retain valuable customers.

    Keyword

    Telecom churn, Xgboost(Extreme Gradient Boosting) Classification algorithms, Decision Trees, Random Forest.


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