Mixed Integer Programming for MMM Budget Optimization
Learn how Mixed Integer Programming (MIP) with discretized response curves solves marketing budget allocation problems using branch-and-bound methods.
Explore our deep-dive content across the core disciplines of modern analytics and strategic consulting.
MMM, Attribution & Media Optimization
Time Series & Probabilistic Models
Valuation & Decision Frameworks
LLMs & AI Strategy Implementation
Advanced Analytics Methods
Next.js, FastAPI & Cloud Deployment
Latest insights from each of our core expertise areas
Learn how Mixed Integer Programming (MIP) with discretized response curves solves marketing budget allocation problems using branch-and-bound methods.
A practical tutorial implementing causal marketing mix modeling with DoWhy, including code examples and a complete toy dataset
Deep dive into gradient boosting trees with quantile regression for robust uncertainty estimation in travel retail forecasting.
How to build robust forecasting models that account for uncertainty, seasonality, and external shocks in the travel retail sector.
A critical analysis of the market share-profitability relationship through Bourantas & Mandes' dynamic model, revealing when bigger isn't always better
A practical guide to using conjoint analysis for understanding product preferences and pricing optimization
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
A comprehensive guide to deploying production-ready Marketing Mix Models—covering data ingestion, model training, cloud deployment, and real-time optimization APIs for budget allocation