Volume 5 , Issue 2 , PP: 30-58, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Ebrahim A. Mattar 1 * , S. K. Towfek 2
Accurate prediction of customer behavior remains a core methodological and operational challenge in retail economics and financial decision-making, particularly as institutions increasingly depend on data-driven forecasting systems to improve credit risk assessment, refine customer segmentation, and deliver targeted financial services in competitive and rapidly changing markets. In practice, the economic value of customer behavior prediction lies in its direct connection to profit maximization, loss minimization, and resource allocation efficiency: retailers seek to anticipate spending tendencies and product affinities to reduce marketing waste and optimize inventory, while financial institutions aim to infer creditworthiness and repayment capacity to reduce default exposure and enhance portfolio stability. Despite the demonstrated advantages of data-driven approaches, the predictive performance of advanced learning systems in such contexts is frequently constrained by the dual challenge of high-dimensional, heterogeneous feature spaces and the sensitivity of model outcomes to hyperparameter choices, often resulting in limited generalization, unstable convergence, or performance degradation when applied to unseen customer groups. To address these constraints, this study develops an integrated optimization framework that couples a high-capacity predictive model with a physics-inspired search mechanism, namely the Kirchhoff’s Law Algorithm (KLA), and employs it for automated hyperparameter optimization of an End-to-End Attention Long Short-Term Memory model (EALSTM), thereby reducing reliance on manual tuning and improving model reliability under financially meaningful data complexity. In addition to introducing the KLA-driven optimization pipeline, the study conducts a rigorous comparative evaluation against established state-of-the-art metaheuristic optimizers, including Particle Swarm Optimizer (PSO), Biogeography Based Optimizer (BBO), Whale Optimization Algorithm (WOA), Bat Algorithm (BA), Artificial Protozoa Optimizer (APO), Genetic Algorithm (GA), and Stochastic Fractal Search (SFS), enabling a systematic assessment of how different search dynamics influence predictive quality in customer analytics applications. Experimental evaluation is performed using an enhanced customer dataset that integrates demographic descriptors, behavioral spending indicators, and financially meaningful constructs—thereby better reflecting real-world decision environments where customer profiling depends on both consumption behavior and financial capacity—and the results demonstrate that the KLA + EALSTM configuration consistently achieves the strongest predictive performance across the full suite of regression metrics. Specifically, KLA + EALSTM attains a Mean Squared Error (MSE) of 3.60 × 10−7, a Root Mean Squared Error (RMSE) of 0.00728, a Mean Absolute Error (MAE) of 0.000372, a Mean Bias Error (MBE) of 0.000091, a correlation coefficient (r) of 0.972, a coefficient of determination (R2) of 0.969, a Relative RMSE (RRMSE) of 0.095, a Nash–Sutcliffe Efficiency (NSE) of 0.971, and a Willmott Index (WI) of 0.977, collectively indicating extremely low error magnitude, minimal systematic bias, strong explanatory power, and high agreement between predicted and observed outcomes, and representing a substantial improvement over the unoptimized EALSTM baseline. From an economic and financial viewpoint, these gains are practically consequential because they strengthen the reliability of predictive decision-support systems used for credit scoring, personalized marketing, customer valueassessment, and financially efficient resource allocation, where even small prediction errors can translate into measurable cost, risk, or revenue impacts. Overall, the findings provide strong empirical support for physics-inspired, non-parametric optimization as a robust mechanism for improving predictive accuracy, stability, and generalization in customer analytics, and they position KLA-based optimization as a scalable and methodologically efficient solution for next-generation retail and financial analytics systems operating under high-dimensional behavioral and financial data conditions.
Customer behavior analytics , Financial prediction , Deep learning optimization , Metaheuristic algorithms , Retail and financial decision-making
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