Advanced Analytics Methods
Why retailers can't escape markdown pricing—a deep dive into the lifecycle, inventory, and pricing constraints that shape modern retail optimization strategies
Understanding why retail revenue functions are concave, how this property enables convex optimization, and the network flow interpretation that powers modern markdown pricing algorithms
How retailers transform predictive demand models into prescriptive pricing decisions through stochastic programming, jointly optimizing markdown prices and inventory allocation under uncertainty
A deep dive into why measuring price elasticity remains the biggest challenge in demand modeling, from data scarcity and endogeneity to seasonal confounding—and how modern techniques address these challenges
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
How predictive demand models serve as the critical bridge between historical sales data and prescriptive markdown optimization, providing both demand estimates and uncertainty quantification
Understanding why prescriptive pricing models must respect the law of demand, and how this fundamental constraint shapes our choice between GLMs and machine learning approaches
Analyzing 150 years of NYC's fish market data to understand how weather shocks reveal true price elasticity and market dynamics using causal inference
How we leveraged Bayesian analysis and mixed-integer programming to optimize pricing strategy, resulting in revenue increase
How we built an automated credit decisioning system that reduced decision time by 90% while improving accuracy by 25% using gradient boosted trees