Marketing teams rarely pursue a single objective: lifting sales must coexist with brand‑awareness targets, customer‑acquisition caps, or sustainability constraints. Traditional media‑mix models (MMMs) treat each metric in isolation, leaving decision‑makers to reconcile incompatible recommendations by hand. This talk demonstrates how graphical Bayesian modelling enables simultaneous inference and optimization across multiple objectives, producing allocation strategies that respect every KPI—and clearly flag when trade‑offs become infeasible.
Description: During the days when every euro of media spend juggles revenue, reach, churn, and business constraints, classic media‑mix models fall short: they optimize one KPI and leave the rest to managerial guesswork. This talk reveals how graphical Bayesian modelling lets multiple causal MMMs—each focused on a different target variable—cohabit in a single, principled budget‑allocation problem.
We'll focus on the PyMC Ecosystem, showing how PyTensor, PyMC and PyMC-Marketing help to solve this issue. Mentioning the advantages of graphical models, and how to use them to build causal media mix models and perform complex operations in a very straightforward manner.
Attendees will leave with a reproducible notebook template, a principled framework for embedding several MMMs in one optimisation problem, and a checklist for detecting—and communicating—when certain goals cannot be met concurrently. The material assumes familiarity with Bayesian inference but will provide a concise refresher on PyMC syntax and the latest PyMC‑Marketing utilities.
Carlos Trujillo is a marketing scientist and data scientist specializing in Bayesian statistics and advanced analytics. He has over seven years of experience applying statistical modeling and machine learning to real-world marketing problems, including work at Omnicom Media Group and Bolt, and he currently works at Wise. He is a contributor to PyMC-Marketing, an open-source Bayesian marketing analytics library within the PyMC ecosystem, where he helps develop tools for media mix modeling, budget optimization, and causal inference. His work bridges theory and practice, and he regularly collaborates with international teams while contributing to the open-source community and technical knowledge sharing.
