🤖 AI for Engineers - Build an MMM Solution with Bayesian AI

Companies across industries are investing heavily in multiple marketing channels, online and offline, yet many still struggle to understand what truly drives sales and how to optimally allocate their marketing budgets.

Classic attribution models and heuristic approaches often fall short:
❌ They fail to capture delayed effects
❌ They struggle with interaction across channels
❌ They break down when data is sparse or noisy

This is where Bayesian Marketing Mix Modeling (MMM) shines.
But moving from marketing data to a reliable, interpretable, and decision-ready MMM requires more than just fitting a regression model.
How do you design, build, and use a Bayesian hierarchical MMM that marketing and business teams can actually trust?

In this 60-minute interactive webinar, we’ll walk through the end-to-end design and implementation of a Bayesian Marketing Mix Model, with a strong focus on practical modeling choices, interpretability, and real business impact.

The session will include a hands-on Python & PyMC walkthrough, using a concrete real-world-inspired use case based on the Rossmann sales dataset, to demonstrate how Bayesian models can be used to optimize marketing budget allocation across channels.

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### You’ll learn

📊 Core Concepts of Marketing Mix Modeling (MMM)
What MMM really is, and why Bayesian approaches have become the industry standard:

• Why attribution models fail
• Incrementality, saturation, and carry-over effects
• When MMM is the right tool (and when it isn’t)

🏗️ Designing a Bayesian Hierarchical MMM
How to design a production-ready MMM using PyMC:

• Channel response curves and adstock effects
• Hierarchical priors across stores, regions, or time
• Model diagnostics and validation

🐍 Python & PyMC Implementation Walkthrough
A practical, code-focused session:

• Data preparation for MMM
• Building the Bayesian model step by step in PyMC
• Sampling, convergence, and posterior analysis
• Interpreting model outputs for business users

💰 From Model to Decision: Budget Optimization
How to turn model outputs into concrete actions:

• Estimating channel ROI and uncertainty
• Simulating budget re-allocations
• Understanding trade-offs and risk using posterior distributions

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📅 Duration: 60 minutes
🛠️ Tech Stack: Python, PyMC, Bayesian modeling