An intelligent multi-objective analytics framework for customer segmentation and value-based decision-making

This study presents a Hybrid Multi-Objective Framework that integrates Principal Component Analysis (PCA), Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), Extreme Gradient Boosting (XGBoost), and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to enhance intelligent customer segmentation and Customer Lifetime Value (CLV) estimation. The framework jointly performs dimensionality reduction, behavioural segmentation, predictive modelling, and multi-objective optimization within a single analytical pipeline. PCA effectively condenses complex customer data into a compact representation that preserves the main behavioural patterns. HDBSCAN uncovers distinct clusters that correspond to high-value, retention-sensitive, and high-risk customer groups with clear behavioural differentiation. The XGBoost models demonstrate strong predictive capability in estimating both customer value and churn likelihood, enabling accurate forecasting of long-term profitability. NSGA-II optimization identifies a set of Pareto-efficient solutions that balance profitability improvement with churn reduction, providing a flexible decision space for strategic trade-offs. Overall, the integrated framework produces interpretable and actionable insights that support data-driven marketing strategies, personalized retention initiatives, and value-based decision-making.