Revolutionizing market surveillance: customer relationship management with machine learning.

Area of Science:

• Telecommunications
• Data Science
• Machine Learning

Background:

• Customer churn prediction is critical for profitability in the telecommunications sector.
• Traditional Customer Relationship Management (CRM) systems struggle with static models, failing to adapt to dynamic customer behaviors.
• Existing CRM models lack the adaptability required for effective, evolving customer retention strategies.

Purpose of the Study:

• To develop an advanced CRM model for enhanced customer churn prediction in the telecommunications industry.
• To improve the accuracy and adaptability of churn prediction beyond traditional CRM system capabilities.
• To integrate a predictive model into a decision support system for actionable customer retention insights.

Main Methods:

• Developed the SmartSurveil CRM model, an ensemble system combining Random Forest, Gradient Boosting, and Support Vector Machine algorithms.
• Utilized a comprehensive telecommunications dataset for training and validation.
• Integrated the predictive model into a decision support system (DSS) to provide actionable insights.

Main Results:

• The SmartSurveil CRM model achieved high performance metrics, including an accuracy of 0.89 and ROC-AUC of 0.91.
• Demonstrated superior predictive accuracy and adaptability compared to baseline approaches.
• Successfully provided actionable insights for dynamic strategy tailoring in customer retention.

Conclusions:

• The SmartSurveil CRM model offers a substantial advancement in predictive accuracy and practical applicability for CRM systems.
• The ensemble approach enhances responsiveness to evolving customer behaviors, improving customer retention.
• The model addresses ethical considerations, ensuring a robust and responsible CRM strategy.