IMPACT OF PREDICTIVE DATA MODELING ON BUSINESS DECISION-MAKING: A REVIEW OF STUDIES ACROSS RETAIL, FINANCE, AND LOGISTICS

Abstract

This study investigates how predictive data modeling influences business decision-making across retail, finance, and logistics, emphasizing the practices that convert predictive accuracy into measurable organizational impact. Evidence from 100 peer-reviewed empirical studies linking predictive outputs to operational actions such as inventory replenishment, dynamic pricing, credit approvals, fraud triage, routing optimization, and service-level promise windows was synthesized. Studies employing temporal validation, probability calibration, explicit operating thresholds, and structured translation into operational policies reported business improvements in 93 percent of cases, achieving median gains of approximately 9–12 percent on the primary KPI. In contrast, minimally aligned designs succeeded in only 48 percent of cases, with modest gains of about 3–5 percent. Sector-specific results revealed consistent yet domain-sensitive patterns. In retail, hierarchical forecasting methods and decision-aware pricing systems yielded a median 2.8 percentage-point reduction in stockouts and a 2.2 percent revenue lift when forecast distributions were directly integrated into service curves and inventory or pricing rules. In finance, calibrated scorecards, cost-sensitive thresholds, and temporally validated probability estimates reduced expected credit loss by nearly 8 percent at constant approval rates or raised approvals by approximately 3.5 percentage points at constant risk levels. Fraud detection and anti–money laundering systems achieved a median 22 percent reduction in false positives while improving cost per true positive when precision–recall evaluation, network features, and workload-aware thresholds informed operational decision-making. In logistics, uncertainty-aware demand and travel-time prediction models enhanced on-time delivery by about 3.9 percentage points and reduced routing costs by nearly 6 percent when embedded into promise windows, lateness-penalty formulations, and quantile-based safety stock policies. These findings emphasize the critical role of end-to-end decision pipelines rather than algorithmic novelty alone, underscoring the value of predict-then-optimize workflows, decision alignment mechanisms, and governance artifacts—including calibration plots, cost curves, and threshold rationales—that ensure operating points remain auditable, interpretable, and resilient to model drift over time.