What question did this study set out to answer?

This research aims to develop a machine learning framework that forecasts the profitability of customer segments for better marketing strategies.

June 7, 2026Open Access

A Machine Learning Approach for Targeting Lucrative Customer Segments

Key Points

This research aims to develop a machine learning framework that forecasts the profitability of customer segments for better marketing strategies.
Evaluated conventional classifiers such as XGBoost, Gradient Boosting, and Random Forest in a unified protocol.
Implemented deep learning models including 1D ResNet and 1D DenseNet.
Utilized SMOTE for class imbalance handling and systematic hyperparameter tuning.
XGBoost and Gradient Boosting achieved the highest weighted F1 score of 0.62.
Traditional models outperformed deep learning methods in accuracy, precision, and computational efficiency.
Ensemble tree-based methods required nearly six orders of magnitude fewer computations than DenseNet-121.

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.

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