Data Science
10 articles
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
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
Markdown Pricing Optimization: Stochastic Programming for Retail
How retailers transform predictive demand models into prescriptive pricing decisions through stochastic programming, jointly optimizing markdown prices and inventory allocation under uncertainty
Why price elasticity is the most difficult parameter to estimate in retail analytics
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
Hierarchical Bayesian Modeling for 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
The predictive demand model: Bridging historical data and markdown optimization
How predictive demand models serve as the critical bridge between historical sales data and prescriptive markdown optimization, providing both demand estimates and uncertainty quantification
Why demand models must be monotonic in price: A deep dive into model choice for markdown optimization
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
Supply, Demand, and Price Elasticity: Lessons from the Fulton Fish Market
Analyzing 150 years of NYC's fish market data to understand how weather shocks reveal true price elasticity and market dynamics using causal inference
Optimizing Vitamin Product Pricing Through Discrete Choice Modeling
Using Bayesian demand modeling and mixed-integer programming to set optimal prices for a new product line — balancing revenue, cannibalization, and market share
Implementing automated credit decisioning: A Data Science Approach
Designing an automated credit decisioning system with gradient boosted trees — from manual review to real-time scoring in production