Causal Measurement Agent
An actionable MMM that makes your next dollar work harder.
The Causal Measurement Agent runs a Marketing Mix Model on your data, refreshes it weekly, and tells you exactly where your next dollar should go, and where to pull back.
A Bayesian MMM, refit weekly on your data, calibrated by incrementality, and queryable in plain English.
Most "MMMs" you've been sold are a quarterly PDF. This one is a live model the agent reads from and writes to every week.

Bayesian approach
Built on the Bayesian MMM framework — the same modelling foundation used by best-in-class measurement teams, with channel-specific adstock and saturation curves and explicit hierarchical priors. No black-box scoring; every recommendation traces back to a coefficient.

Your model isn't a snapshot. The agent re-fits on the latest 18+ months of spend and conversion data every week, skewed for recency, so regime changes - a competitor entering, a creative refresh, a pricing move - show up in the response curves within days.

Geo holdouts and platform-side experiments update the posteriors directly. A +37% lift result on TikTok in Q2 narrows the channel's confidence interval and shifts every subsequent recommendation. The Testing Agent picks the next test to shrink the biggest remaining uncertainty.

The MMM isn't locked inside a vendor dashboard. The agent exposes the contribution decomposition, the response curves, and the posteriors over MCP — so you can ask "what's saturated this week?" from Claude and get an answer pulled live from your own model.

Install the BlueAlpha MCP to query the Causal Measurement Agent from any AI assistant — pull this week's reallocation queue, inspect any channel's response curve, ask why a contribution shifted, and push approved moves straight back. Zero friction.
Questions
What teams ask us first.
Don't I already get this from my MMM vendor?
Why Meridian specifically?
How is this different from the optimizer inside an ad platform?
What if my model is wrong?
Can the agent move money automatically?

Best Marketing Mix Modeling Software in 2026 (15 Tools)
Matthias Stepancich
Best Marketing Mix Modeling Software in 2026 (15 Tools)
Fifteen MMM platforms compared on methodology, incrementality testing, refit cadence and price. Includes where each one is a poor fit.

The Hard Truth About MMM and Incrementality
Peter Grafe
The Hard Truth About MMM and Incrementality
MMM and incrementality testing can't tell you what to do next. Learn why measurement without orchestration leaves money on the table.

Marketing Measurement: What Growth-Focused CMOs Need to Know in 2026
Matthias Stepancich
Marketing Measurement: What Growth-Focused CMOs Need to Know in 2026
Essential marketing measurement strategies and advanced techniques for growth-focused CMOs navigating the evolving landscape in 2026.

What Every CMO Must Know Before Investing in a Marketing Mix Model
Peter Grafe
What Every CMO Must Know Before Investing in a Marketing Mix Model
Essential insights for CMOs considering MMM investment, including Bayesian approaches, incrementality testing, and avoiding common implementation pitfalls.

Why Your Marketing Agency Needs MMM and Incrementality Testing
Matthias Stepancich
Why Your Marketing Agency Needs MMM and Incrementality Testing
Strategic guide for marketing agencies to differentiate with MMM and incrementality testing, delivering superior client value and results.

What Is Media Mix Modeling (MMM)?
Peter Grafe
What Is Media Mix Modeling (MMM)?
Complete introduction to Media Mix Modeling fundamentals, benefits, and implementation strategies for competitive marketing advantage.

How to Grow When Your Strongest Channel Looks Saturated
Peter Grafe
How to Grow When Your Strongest Channel Looks Saturated
Meta description: A four-part diagnostic framework for growth teams: prove whether your dominant channel is truly saturated before committing to a budget reallocation.

How to Measure Influencer Marketing Incrementality
Matthias Stepancich
How to Measure Influencer Marketing Incrementality
Klover proved YouTube sponsorships drive incremental conversions with a geo holdout test. This playbook shows how to build the same measurability system.

How to Diversify Your Channel Mix with MMM Testing
Matthias Stepancich
How to Diversify Your Channel Mix with MMM Testing
Klover cut Meta iOS dependency from 75% to under 45% and improved blended CAC by 12% using MMM and geo holdout testing. A four-phase channel diversification framework.

How to Measure OOH Advertising: Geo Holdouts, MMM, and Incremental CPA
Matthias Stepancich
How to Measure OOH Advertising: Geo Holdouts, MMM, and Incremental CPA
BlueAlpha proved beehiiv's $300K subway campaign drove ~100,000 incremental users at $4 CPA. This playbook shows how to run the same geo holdout test for any OOH campaign.
