Machine Learning for Dynamic Pricing Strategies and Optimization in Retail E-commerce

Abstract

Dynamic pricing the real-time adjustment of prices in response to market conditions, demand signals, inventory, and customer attributes has become a core capability for modern retail e-commerce platforms. Machine learning (ML) enables firms to estimate demand elasticities from heterogeneous data, to forecast micro-demand, and to optimize pricing policies that balance immediate revenue with long-term customer value. This paper delivers a comprehensive, scholarly treatment of ML methods for dynamic pricing in retail e-commerce. We synthesize prior work from economics, operations research, and computer science, provide formal mathematical formulations (demand estimation; revenue optimization under constraints; reinforcement learning and contextual bandit formulations), and survey algorithmic approaches (parametric elasticity models; nonparametric demand learning; Bayesian and frequentist bandits; model-based and model-free RL; causal inference for price effects; and personalization with fairness constraints). We propose practical, production-oriented architecture patterns and evaluation protocols for offline and online experimentation, address regulatory and ethical considerations (price discrimination, fairness, transparency), and present reproducible experimental blueprints. The article also discusses scalability, cold-start solutions, inventory-coupled pricing, and handling strategic customer behavior. Finally, we include actionable recommendations and a research agenda that highlights open problems: causal demand estimation under unobserved confounding, robust pricing under model misspecification, multi-agent market effects, and privacy-preserving personalization. The manuscript is intended as both a reference for academics and a practical guide for practitioners implementing ML-driven dynamic pricing in e-commerce.