A Machine Learning Approach for Targeting Lucrative Customer Segments

Abstract: Effective marketing plays a pivotal role in modern businesses, where strategic allocation of resources is essential for maximizing return on investment (ROI). Customer segmentation involves dividing users into distinct sub-groups based on common characteristics, enabling each segment to receive tailored promotions according to their behavior. However, identifying which customer segments to target can be financially challenging due to the significant costs associated with marketing campaigns. This study proposes a machine learning framework to forecast the profitability of various customer segments and optimize marketing strategies accordingly. The proposed solution leverages machine learning and deep learning techniques to classify customers based on their potential value. Specifically, conventional classifiers including AdaBoost, Gradient Boosting, Extreme Gradient Boosting (XGBoost), Linear Discriminant Analysis (LDA), Random Forest, Naïve Bayes, Support Vector Machines (SVM), and K-Nearest Neighbors (KNN) are evaluated. In addition, deep learning models, namely 1D ResNet and 1D DenseNet, are investigated. All models are trained and evaluated under a unified protocol that includes SMOTE-based class im-balance handling, systematic hyperparameter tuning, and a joint analysis of predictive performance and computational cost. The experimental findings reveal that traditional models, particularly XGBoost and Gradient Boosting, consistently outperform deep learning models in terms of accuracy, precision, and computational efficiency, with both achieving the highest weighted F1 score of 0.62 while requiring nearly six orders of magnitude fewer computations than DenseNet-121. These results provide concrete evidence that ensemble tree-based methods are better suited than deep architectures for moderately sized, imbalanced, tabular marketing datasets.

Leen Almajed, Haifa Almakhdhoub, Jana Alzeydan, Alaa Bin Sleem and Ouiem Bchir. “A Machine Learning Approach for Targeting Lucrative Customer Segments”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.5 (2026). http://dx.doi.org/10.14569/IJACSA.2026.0170590

@article{Almajed2026,
title = {A Machine Learning Approach for Targeting Lucrative Customer Segments},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.0170590},
url = {http://dx.doi.org/10.14569/IJACSA.2026.0170590},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {5},
author = {Leen Almajed and Haifa Almakhdhoub and Jana Alzeydan and Alaa Bin Sleem and Ouiem Bchir}
}