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Customer churn prediction in Telecom Sector

Mastan Rushika, Dr.Gousiya Begum, Ms. N. Musrat Sulthana, K Sunitha

Abstract


In the telecom industry, many customers use services every day, and companies collect a lot of data about them. Keeping current customers is easier than getting new ones, so businesses want to understand why people leave. Customer churn happens when users switch from one telecom provider to another. When companies know the reasons behind customer churn, they can take steps to improve their services and retain more customers.

Machine learning techniques like Random Forest, K-Nearest Neighbors (KNN), and Decision Trees can help analyze customer behavior and predict who might switch to another provider. The objective is to help businesses identify customers  who are at risk of leaving (churning) and implement strategies to retain them. By using these techniques, businesses can reduce customer loss, prevent revenue drop, and improve customer service. These insights can also help other industries keep their customers happy and loyal, ensuring long-term business success.

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References


Singh et al. (2024) “Customer Churn Prediction

in Telecom Industry Using Data Mining Techniques”

https://www.researchgate.net/publication/38292055

Wagh et al. (2024) “Customer Churn Prediction

in Telecom Sector Using Machine Learning

Techniques”.

https://doi.org/10.1016/j.rico.2023.100342

Srinivasan et al. (2023) “Customer Churn

Prediction Using Machine Learning Approaches”.

DOI: 10.1109/ICECONF57129.2023.10083813

Mohammed et al. (2024)Telecom Customer

Churn Prediction Analysis

DOI: 10.13140/RG.2.2.17996.18564

Akbar et al. (2024) “Analysis of Customer

Churn in Telecom Industry”.

DOI: 10.13140/RG.2.2.17996.18564


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