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Class is in session
Marketing Science Bootcamp: MMM, Attribution & Incrementality
3 Weeks
·Cohort-based Course
Master marketing measurement with Python: Build MMM, attribution & incrementality frameworks to optimize spend and prove marketing ROI
Class is in session
Marketing Science Bootcamp: MMM, Attribution & Incrementality
3 Weeks
·Cohort-based Course
Master marketing measurement with Python: Build MMM, attribution & incrementality frameworks to optimize spend and prove marketing ROI
Previously at
Course overview
Move from basic metrics to data-driven marketing decisions with proven models
Ready to move beyond basic attribution models and last-click analytics? This intensive 3-week course teaches you to build and implement three essential marketing measurement frameworks: Media Mix Modeling (MMM), Multi-Touch Attribution, and Incrementality Testing.
You'll learn to:
- Build a complete Media Mix Model using PyMC Marketing / Google Merdian / Meta Robyn to optimize your channel mix
- Design and implement attribution frameworks that accurately track customer journeys
- Create robust incrementality tests to measure true marketing impact
- Learn how to conduct proper calibration with Geo Experiments and Lift Studies
- Combine all three approaches into a comprehensive measurement strategy
This is not just theory - you'll work with real data and Python implementations. Through hands-on projects and live workshops, you'll develop practical skills you can immediately apply to:
- Make data-driven budget allocation decisions
- Prove marketing ROI to stakeholders
- Understand the true impact of each marketing channel
- Build measurement frameworks that drive business growth
Perfect for Marketing Data Analysts and Data Scientists who want to go beyond basic metrics and build sophisticated measurement systems that drive decisions. By the end of this course, you'll have working models and frameworks you can adapt to your organization's needs.
Course includes:
- 6 live interactive sessions
- Hands-on projects with real data
- Python code templates and implementations
- Direct feedback on your work
- Community of marketing analytics professionals
Prerequisites:
- Basic Python knowledge
- Marketing fundamentals
- Access to Python environment (instructions provided)
Who is this course for
01
Analytics Managers who need to optimize channel spend and prove ROI to stakeholders, ready to implement data-driven measurement systems
02
Data Analysts struggling with accurate channel attribution who want to build robust testing frameworks and optimize marketing spend
03
Data Scientists who want to move beyond basic attribution and need technical skills to build and implement advanced measurement models
What you’ll get out of this course
Build and deploy a media mix model using real campaign data to optimize your marketing budget allocation across channels.
Build and deploy a complete media mix model using PyMC Marketing / Google Meridian / Meta Robyn. Handle real campaign data, implement adstock and saturation effects, and create actionable budget recommendations with confidence intervals.
Design and implement an attribution framework that accurately tracks customer journeys and validates channel performance
Build a complete attribution system that tracks full customer journeys. Compare first-touch to algorithmic models, validate channel performance, and create actionable reports for stakeholders.
Create and execute incrementality tests to measure the true causal impact of marketing activities on business metrics
Design statistically valid incrementality tests with proper power analysis and sample size calculations. Learn to control for multiple testing, validate results, and measure true lift across marketing channels.
Develop a comprehensive measurement strategy that combines MMM, attribution, and incrementality insights for stakeholder buy-in.
Develop a comprehensive measurement strategy that combines MMM, attribution, and incrementality insights for stakeholder buy-in.
What’s included
Live sessions
Learn directly from Guilherme Diaz-Berrio in a real-time, interactive format.
Lifetime access
Go back to course content and recordings whenever you need to.
Community of peers
Stay accountable and share insights with like-minded professionals.
Certificate of completion
Share your new skills with your employer or on LinkedIn.
Maven Guarantee
This course is backed by the Maven Guarantee. Students are eligible for a full refund up until the halfway point of the course.
Course syllabus
6 live sessions • 16 lessons • 6 projects
Week 1
Jun
17
MMM Foundations Workshop
Jun
19
MMM Implementation
Module 1: MMM Foundations
Module 2: Building Your MMM
Week 2
Jun
24
Attribution Framework Design
Jun
26
Advanced Attribution
Module 3: Attribution Fundamentals
Module 4: Advanced Attribution
Week 3
Jul
1
Experimentation Workshop
Jul
3
Integration & Presentation
Module 5: Experimentation Design
Module 6: Integrated Measurement
Meet your instructor
Guilherme Diaz-Berrio
Guilherme Diaz-Berrio
Marketing analytics author & leader with 10+ years building measurement systems
As a marketing analytics leader, I've built and scaled measurement frameworks that process millions of customer touchpoints for global organizations.
My experience includes implementing attribution systems, developing media mix models, and creating incrementality testing frameworks that prove true marketing ROI, from BMW Group to Kindred Group.
This practical experience shaped my book "Data Analytics for Marketing with Python" (Packt, 2024), where I share proven approaches to measurement that drive decisions.
I've faced the challenges you're dealing with – from messy data to skeptical stakeholders.
As co-founder at Pinemarsh Consulting, I help companies implement the same frameworks we'll cover in this course. My experience spans multiple industries, combining technical Python expertise with strong business acumen.
You won't just learn theory – you'll build working models using real data and learn how to communicate results effectively to stakeholders.
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Marketing Science Bootcamp: MMM, Attribution & Incrementality
Course schedule
4-6 hours per week
Tuesdays & Thursdays
5:00pm - 7:00pm GMT
Live sessions every Tuesday and Thursday for 3 weeks. Each 90-minute session combines theory and hands-on practice. Tuesday sessions introduce concepts, Thursday sessions focus on implementation. All sessions include breakout rooms and Q&A.
Weekly projects
2 hours per week
Weekly projects with clear deadlines and templates provided. Office hours available for 1 hour before each live session for implementation support. Continuous feedback via Slack channel. Expect 2-3 hours weekly for project work.
Learning is better with cohorts
Active hands-on learning
This course builds on live workshops and hands-on projects
Interactive and project-based
You’ll be interacting with other learners through breakout rooms and project teams
Learn with a cohort of peers
Join a community of like-minded people who want to learn and grow alongside you
Frequently Asked Questions
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