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September 10, 2025Iconic Research And Engineering JournalsOpen Access

Machine Learning for Telecom Customer Retention and Growth

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Overview

This research demonstrates churn prediction frameworks using machine learning in telecom, highlighting model performance and implications for strategy.

Key Points

  • Ensemble techniques like Random Forest and XGBoost significantly improve churn prediction accuracy.
  • Experiment results show models achieved the best balance between precision and recall, indicating effective predictions.
  • The study includes data preprocessing and imbalance handling, crucial for enhancing model performance in customer retention.
  • Deployment considerations in the telecom sector highlight the need for strategies like explainability and cost-sensitive approaches.

Cite This Study

A 2025 study studied this question.

synapsesocial.com/papers/68c23bd7b210217d64786bf6https://doi.org/10.64388/irev9i2-1710393-6842
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Identifying customer churn in Telecom sector: A Machine Learning Approach2024 · 1 citations
  2. 2A predictive analytics approach to improve telecom's customer retention2025 · 4 citations
  3. 3Comparative Analysis of Machine Learning Models for Telecom Customer Churn Prediction2025 · 1 citations
  4. 4Comparing Traditional Machine Learning and Advanced Gradient Boosting Techniques in Customer Churn Prediction: A Telecom Industry Case Study2025
  5. 5Enhanced Customer Retention: Deep Learning- Based Churn Prediction for Telecom Industry2025