INTEGRATION OF MACHINE LEARNING AND ADVANCED COMPUTING FOR OPTIMIZING RETAIL CUSTOMER ANALYTICS

Abstract

This study examines how the joint deployment of machine learning (ML) and advanced computing (AC) optimizes retail customer analytics across forecasting, personalization, segmentation and targeting, churn/retention and customer lifetime value (CLV), pricing and promotions, and omni-channel operations. Drawing on a structured review of 150 peer-reviewed papers, we synthesize quantitative evidence on model performance (e.g., sequence models, tree ensembles, contextual bandits, survival models, and multimodal recommenders) alongside infrastructure choices (distributed clusters, accelerators, streaming feature stores, and CI/CD practices). We organize findings by prediction and decision tasks, data regimes (transactional, clickstream, catalog/price, and logistics), and compute regimes (batch vs. streaming; CPU vs. GPU/TPU), and we standardize outcomes using commonly reported accuracy, ranking, calibration, and utility measures together with operational key performance indicators such as service level, stock-out exposure, inventory turns, and on-time fulfillment. Across tasks, integrated ML+AC pipelines consistently outperform classical baselines when evaluation respects temporal order, features are governed for point-in-time correctness and freshness, and serving architectures meet tail-latency budgets. Retrieval→ranking recommenders, representation learning augmented with graph signals, and survival-aware churn models translate directly into measurable lift; hierarchical elasticity estimation and credible causal designs support interpretable pricing and promotion effects under operational constraints. Robustness and risk controls—drift surveillance, anomaly detection, adversarial-aware training, and privacy-preserving learning—stabilize performance under demand shocks and data shifts, while standardized cost and carbon accounting clarify the economic and environmental price of incremental accuracy. The synthesis shows that durable value in retail arises less from any single algorithm and more from the end-to-end coupling of sound empirical methods, engineered data pipelines, governable deployment, and transparent trade-off reporting on cost, carbon, and latency. These findings provide an actionable evidence base and a reproducible blueprint for retailers seeking to convert predictive insight into reliable commercial impact.