Machine learning framework optimizes telecom marketing with churn prediction and segmentation

A new machine learning framework has been developed to help telecommunication companies optimize marketing strategies by predicting customer churn and segmenting customers based on their value and churn risk. The framework utilizes gradient boosting models like CatBoost, XGBoost, and LightGBM, achieving strong performance metrics on the IBM Telco Customer Churn dataset. By combining churn prediction with customer segmentation through k-means clustering and principal component analysis, the system identifies four actionable clusters, enabling the design of tailored retention and engagement strategies with a focus on maximizing return on investment and customer lifetime value. AI

IMPACT Enables more targeted and profitable marketing campaigns for telecommunication companies by improving customer retention.

RANK_REASON The item is an academic paper detailing a machine learning framework for a specific industry problem. [lever_c_demoted from research: ic=1 ai=1.0]

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• Catboost
• IBM Telco Customer Churn dataset
• k-means clustering
• LightGBM
• principal component analysis
• Shap
• streamlit
• XGBoost

• paper
• product
• other

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