Revolutionizing Marketing Analytics: A Data-Driven Machine Learning Framework for Churn Prediction
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V3I2P113Keywords:
Customer Churn Prediction, Customer Retention, Marketing Analytics, Predictive Analytics, Telco-Customer-Churn dataset, Machine LearningAbstract
One of the biggest problems for big businesses is customer attrition. Since consumers are the primary source of income for businesses in the rental industry, they are specifically searching for strategies to keep them as clients. This study presents the data-driven machine learning technique for telecom sector churn prediction, addressing challenges of noisy, imbalanced, and high-dimensional data through a comprehensive preprocessing pipeline that includes noise removal, SMOTE-based balancing, feature selection, and outlier detection. In order to create predictive models using Decision Trees and Random Forest classifiers, the preprocessed data which consists of 3,333 customer records with attributes ranging from call metrics to charge information is separated into training and testing sets. When measured using measures like accuracy, precision, recall, F1-score, and ROC analysis, the Decision Tree model performed better than other models like ANN and SVM, achieving 88% accuracy, 83% precision, 93% recall, and 88% F1-score. These outcomes show how the approach may produce useful insights for improving customer retention and marketing strategy optimization in changing telecom contexts
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