AI framework enhances telecom churn prediction with CRM integration

A new research paper proposes a framework for integrating explainable AI into customer relationship management (CRM) systems within the telecommunications industry. The study benchmarks four classifiers—Logistic Regression, Random Forest, XGBoost, and LightGBM—on the IBM Telco Customer Churn dataset, finding that Logistic Regression and LightGBM perform comparably. The framework utilizes SHAP and LIME to provide both global and instance-level explanations, identifying key churn drivers like tenure and contract type. This approach aims to enable retention specialists to design personalized interventions, with projections indicating a potential reduction in churn by 3.3-5.3 percentage points. AI

IMPACT Enables more targeted customer retention strategies by making AI predictions actionable for CRM workflows.

RANK_REASON The cluster contains a research paper detailing a new framework and benchmark results for explainable AI in a specific industry application. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• customer relationship management
• IBM Telco Customer Churn
• LightGBM
• logistic regression model
• random forest
• Sandeep Gaddamwar
• Shap
• XGBoost

• paper
• product
• other

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