Writing on how decisions get modeled, built, and deployed. By Cyril Noirot.
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
Deep dive into gradient boosting trees with quantile regression for robust uncertainty estimation in travel retail forecasting.
Learn how Mixed Integer Programming (MIP) with discretized response curves solves marketing budget allocation problems using branch-and-bound methods.
How to build robust forecasting models that account for uncertainty, seasonality, and external shocks in the travel retail sector.
A practical tutorial implementing causal marketing mix modeling with DoWhy, including code examples and a complete toy dataset
Walk through real-world MMM optimization scenarios using Mixed Integer Programming. See how to discretize response curves, formulate constraints, and solve practical reallocation problems with actual numbers.
Exploring structural causal models using DAGs, counterfactual reasoning, and frameworks like DoWhy and PyMC-Marketing
How Google's Meridian brings causal inference to marketing mix modeling at global scale
Understanding why modern marketing optimization focuses on efficiency, not volume. Learn how marginal ROI transforms budget allocation and why the slope matters more than the height.
Interactive tools and calculators for practitioners — built to explore ideas, not to sell software.
Available for consulting engagements in forecasting, pricing, and decision systems. More about me