Marketing Foundation Models Bayesian MMM Without Per-Dataset Fitting
Details
Building a trustworthy Bayesian marketing mix model (MMM) takes a lot of skilled, repetitive work. Someone has to choose a structure, fit it, diagnose it and revisit the assumptions, then do it all again for the next brand, market or quarter. And because the reported uncertainty depends on the one structure the modeler picked, it is usually too narrow.
In this session, Carlos Trujillo and Teemu Säilynoja present Marketing Foundation Models (MFM): a model trained on simulated marketing worlds that estimates baseline, channel and control contributions, with uncertainty, in a single pass, without fitting a model to each new dataset. They will walk through how it works, what the evaluation on synthetic data shows, and where this early research prototype still falls short. Luca Fiaschi hosts, with time for your questions.
What you will learn:
- How MFM combines a simulator of marketing worlds, a transformer encoder and flow matching to return contribution estimates without per-dataset fitting.
- What the synthetic evaluation found: channel contribution error about a quarter of a standard PyMC-Marketing MMM's, and better calibrated uncertainty than standard regression.
- Why sampling over causal structures gives more honest uncertainty than a single hand-built model.
- The model's known limits today, and how it differs from foundation models such as TabPFN and TimesFM.
Who should attend:
- Marketing science and analytics teams that already run MMMs, or have been put off by how long they take to build.
- Data scientists working on Bayesian modeling, causal inference or amortized inference.
- Analytics leaders who want measurement answers inside the planning cycle.
📜 Outline of Talk / Agenda:
- 5 min: Introduction to PyMC Labs and speakers
- 40 min: Panel discussion
- 15 min: Q&A
💼 About the speakers:
Dr. Luca Fiaschi (PyMC Labs Partner, Gen AI Vertical)
Luca helps organizations unlock the value of data and AI. With 15+ years of experience, he’s led and scaled teams at Mistplay, HelloFresh, Alibaba, and Stitch Fix, driving breakthroughs in personalization, marketing optimization, and causal modeling. He holds a PhD in Computer Science from Heidelberg University.
Connect with Luca 👉 LinkedIn
Carlos Eduardo Trujillo Agostini (Principal Data Scientist at PyMC Labs)
Carlos is a Marketing Scientist passionate about using data and AI to turn marketing strategy into measurable results. He’s worked with teams across Latin America, Europe, and Africa, including roles at Wise, Bolt, and Omnicom Media Group. As a core member of PyMC Labs, he contributes to open-source projects like PyMC-Marketing, blending statistical rigor with practical marketing insight.
Connect with Carlos: 👉 LinkedIn
Teemu Säilynoja (Principal Data Scientist at PyMC Labs)
Teemu Säilynoja is a data scientist with expertise in Bayesian modeling, marketing analytics and trading analytics. He is completing a Doctor of Science degree focused on calibration assessment of Bayesian models. He also contributes to open-source tools for probabilistic programming and model evaluation in Python and R.
Connect with Teemu: 👉 LinkedIn
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📖 Code of Conduct:
Please note that participants are expected to abide by PyMC's Code of Conduct.
Connecting with PyMC Labs:
🌐 Website: https://www.pymc-labs.com/
👥 LinkedIn: [https://www.linkedin.com/company/pymc-labs/](https://www.pymc-labs.com/)
🐦 Twitter: [https://twitter.com/pymc_labs](https://www.pymc-labs.com/)
🎥 YouTube: [https://www.youtube.com/c/PyMCLabs](https://www.pymc-labs.com/)
🎮 Discord: https://discord.gg/MARSCNemw3