Bayesian vs. Frequentist MMM: What Actually Changes

Hosted by Gui Diaz-Berrio

Thu, Apr 2, 2026

11:30 AM UTC (45 minutes)

Virtual (Zoom)

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Marketing Science Bootcamp: MMM, Attribution and Experiments with Claude Code
Gui Diaz-Berrio
View syllabus

What you'll learn

Understand what Bayesian MMM does differently

Joint parameter estimation, full posteriors, and priors — explained without the jargon, with side-by-side comparisons.

Know when Bayesian is worth the complexity

Small data, prior knowledge, uncertainty for decisions. Learn the conditions where Bayesian wins and where OLS is fine

See how experiments calibrate Bayesian models

The real advantage: encoding geo-test results as priors. See how Bayesian MMM fuses causal and observational evidence.

Why this topic matters

Google Meridian and PyMC-Marketing are Bayesian. Most marketing scientists learned frequentist methods first — and the switch isn't just syntax. Bayesian models estimate parameters jointly, quantify uncertainty, and let you encode experimental results as priors. This lesson breaks down what actually changes, so you can evaluate when to make the switch.

You'll learn from

Gui Diaz-Berrio

Co-founder @ Pinemarsh and author of "Data Analytics for Marketing"

What you'll see in 30 minutes is one session's worth of material.


The full 4-week cohort (starting May 2026) covers the complete stack:

  • Context window management for long analytical sessions
  • Building custom Claude Code Skills that encode your team's modeling standards
  • End-to-end data pipelines — from raw data to executive-ready deliverables
  • Marketing Mix Modeling and Geo-Experimentation, orchestrated agenically
  • Automating recurring measurement workflows so they run without you


Join the waitlist to get early access and priority enrollment for the May cohort of Marketing Science Bootcamp with Claude Code.


Gui Diaz-Berrio has led marketing measurement at companies spending €100M+ on advertising, including Kindred Group (Head of Marketing Analytics) and enterprise clients through Pinemarsh Consulting. He's built models from scratch and managed vendor relationships — both perspectives taught in this course.

Author of "Data Analytics for Marketing with Python" (Packt, 2024) — covering the practical frameworks taught in this course.

He's faced the challenges you're dealing with — messy data, skeptical CFOs, vendor black boxes, and the pressure to prove ROI with limited experimentation budget.

Previously at

Dampi Bowl 🥥🍍🌶
Kindred
BMW Group
@Packtpub

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