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?
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