Approaches to Dynamic Pricing in the Retail Industry

This white paper explores dynamic pricing strategies in retail, focusing on understanding and leveraging price elasticity amid economic challenges. With global growth projected to slow to 2.6% in 2024, retailers must optimize pricing to navigate subdued sales environments. The paper highlights key challenges in estimating price elasticity, including computational complexities and data limitations across diverse product hierarchies. It evaluates various demand modeling techniques such as Generalized Linear Models (GLMs), Bayesian learning, and Structural Time Series (STS) models, each addressing scalability and data sparsity in unique ways. The optimization process is outlined with practical constraints like pricing regulations, inventory limits, and channel consistency. The study emphasizes the importance of integrating analytics-driven elasticity estimates into dynamic pricing strategies to enhance profitability and customer satisfaction.