Why a Hierarchical Modeling approach is essential in retail demand estimation

How hierarchical modeling solves the challenge of sparse data in retail by pooling statistical strength across products, enabling reliable demand parameter estimation even for low-volume SKUs

Cyril NoirotApril 10, 20254 min read

Cyril Noirot

Analytics consultant specializing in marketing science, forecasting, and strategic data solutions. Passionate about bridging the gap between advanced analytics and business impact.

Data Science

From lifecycle limits to markdown rules: The product constraints behind retail profit optimization

Why retailers can't escape markdown pricing—a deep dive into the lifecycle, inventory, and pricing constraints that shape modern retail optimization strategies

May 156 min

Data Science

Concavity and Network Flow in Profit Optimization: How Diminishing Returns Enable Efficient Solutions

Understanding why retail revenue functions are concave, how this property enables convex optimization, and the network flow interpretation that powers modern markdown pricing algorithms

May 157 min

Data Science

Inside the profit optimization problem: From price uncertainty to prescriptive decision-making

How retailers transform predictive demand models into prescriptive pricing decisions through stochastic programming, jointly optimizing markdown prices and inventory allocation under uncertainty

May 15 min

Cyril Noirot

Related Articles

From lifecycle limits to markdown rules: The product constraints behind retail profit optimization

Concavity and Network Flow in Profit Optimization: How Diminishing Returns Enable Efficient Solutions

Inside the profit optimization problem: From price uncertainty to prescriptive decision-making