Machine Learning for Retail Pricing Optimization

Abstract

Retail price optimization is necessary to realize optimal revenues and enhance competitive edge in fast-changing markets. Recent advances in machine learning (ML) have provided new methodologies to deal with the complexities of pricing decision-making in retail environments. However, despite the growing literature, significant gaps remain in the application of ML methods to formulate real-time, individualized, and fair pricing policies coupled with overall retail systems. Traditional pricing methods rely on linear models or simplistic demand forecasts, which fail to consider the dynamically changing market conditions and consumer behavior. The proposed research project will fill this gap by exploring the use of sophisticated machine learning methods like reinforcement learning, deep learning, and ensemble methods to optimize pricing policies across different retail environments. The research will concentrate on addressing the principal challenges like data integrity, interpretability, pricing fairness, and integrating ML-based pricing models with inventory management and customer segmentation policies. The research will also examine how emerging technologies like real-time data analytics and explainable AI can be employed to improve decision-making processes and establish trust in ML-based pricing systems. The study implications are expected to provide actionable recommendations to retailers to implement adaptive, transparent, and consumer-centric pricing policies that facilitate profitability while enhancing customer satisfaction. Finally, the research contributes to the emerging field of ML in retail by developing an integrated framework for dynamic pricing optimization, which facilitates more efficient and fair solutions in competitive markets.