Enhancement of Telemarketing Success by using Ensemble-based Decision Tree Classification Technique

Abstract

In today's competitive market, effective customer targeting through telemarketing campaigns is crucial for business success. This project explores the application of online machine learning techniques to enhance telemarketing success. The dataset comprises demographic and financial attributes of potential customers, including age, job type, marital status, education level, and loan status. The study compares an existing system leveraging Decision Tree Classifier, though specific accuracy metrics are not disclosed, with a proposed system implementing Decision Tree Classifier, SMOTE for handling class imbalance, K-means clustering for segmentation, and a Stacking Classifier. The proposed system achieves promising results with Decision Tree achieving 81% accuracy. Key findings underscore the effectiveness of machine learning in optimizing telemarketing campaigns, particularly in segmenting customer demographics and predicting subscription outcomes. The implications suggest that integrating Decision Tree into online platforms can significantly improve campaign targeting and customer engagement. Future research directions may focus on refining techniques, exploring additional feature engineering methods, and integrating real-time data streams for dynamic campaign optimization.