Enhancing Customer Response Predictions: The Role of Hyperparameter Tuning and Data Balancing in Predictive Models

Predicting customer responses to marketing campaigns plays a critical role in improving engagement and maximizing return on investment (ROI) in data-driven marketing.This study evaluates multiple machine learning models, including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN), to predict customer behaviour and optimize marketing strategies.To address class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was employed, significantly improving all evaluation metrics [1].Hyperparameter tuning was performed using GridSearchCV and cross-validation to ensure robust model performance [2].Among the models tested, the Random Forest classifier achieved the highest accuracy of 93%, along with well-balanced precision, recall, and F1-score [3].Key influential features included the recency of purchases, customer tenure, and previous campaign responses.This research highlights the Random Forest model's superior predictive capabilities and the importance of feature analysis, underscoring the effectiveness of leveraging advanced machine learning and resampling techniques to improve marketing campaign outcomes and customer targeting strategies [4].