August 19, 2026

Agentic MMM: Run Marketing Mix Modeling From Claude

By Gabriel Mohanna · 6 minute read

About the Author

Gabriel is Head of AI Strategy at ScanmarQED, bringing deep expertise across the full marketing measurement stack — MMM, multi-touch attribution, and incrementality experiments. He has spent his career translating complex models into decisions that CMOs and growth teams can actually act on, bridging the gap between data science rigour and business clarity. As the founder of MMM Labs — a SaaS platform built on leading open source frameworks like Robyn, PyMC-Marketing, and Meridian — he has been through the full arc of building and scaling a measurement product, which ScanmarQED acquired to accelerate its MMM offering. At ScanmarQED, Gabriel leads AI strategy company-wide — defining how AI transforms product, marketing, sales, and operations, and moving the organization from AI-curious to AI-native.

MMM Labs Is Now Available Directly in Claude

MMM Labs, ScanmarQED's multi-engine marketing mix modeling platform, now connects directly to the AI harness you already use through a Model Context Protocol (MCP) connector. Analysts can run the full MMM workflow from a chat with Claude Cowork or OpenAI CodeX: check whether a dataset is ready for modeling, run and compare multiple modeling engines, fold in known ROI evidence as a business prior, and optimize next year's budget, all without leaving Claude or ChatGPT.

Every number that comes back is generated by MMM Labs' own modeling backend, not guessed by the language model, and every action shows up in the MMM Labs app in real time.

ScanmarQED calls this Agentic MMM. It's the same modeling engine, data hub, and multi-engine comparison that MMM Labs' web app runs, now reachable through natural language, with an audit trail behind every claim.

What is the MMM Labs connector for LLMs?

The MMM Labs connector is an MCP integration that lets Claude (or ChatGPT, or Gemini) call MMM Labs' modeling backend directly, using the same permissions and data as the account you're already logged into.

ScanmarQED describes this as “three doors” into one platform:

● Door 1: the app

Point-and-click, visual, the interface MMM Labs' own consulting team has used daily for the past year.

● Door 2: the in-app AI assistant

Natural language inside the app itself, with a choice of underlying model (Claude, GPT, or Gemini).

● Door 3: your own Claude or ChatGPT

The MCP connector, so you can work from the assistant you already use, alongside your own files and skills.

Zero Drift

All three doors read and write the same backend, with the same permissions and the same audit trail. A project created through the Claude connector appears in the web app immediately, with identical numbers, what ScanmarQED calls zero drift. Nothing you do in chat lives only in chat.

Why not just ask Claude to build a marketing mix model on its own?

Because a language model that estimates a marketing mix model from scratch will give you an answer with the same confidence whether it's right or badly wrong. Nothing in the output tells you which.

Failure mode demonstrated

To give an example that slightly on the extreme side, in ScanmarQED's own demonstration of this failure mode, an unguided model reported a TV ROI of 4.2x and recommended shifting 40% of next quarter's budget into TV, delivered as a clean number with no hesitation. What it didn't say: the model's R² was -8.56 (worse than guessing the average), and TV and search spend were 0.94 correlated, meaning no regression can cleanly separate their effects. The coefficient is unstable and could be assigned to either channel at random. None of that shows up unless someone knows to go looking for it, and a plausible-sounding recommendation reads identically whether the diagnostics behind it are solid or nonexistent.

The harder part of MMM is getting to a model you can trust and being able to prove it after the fact.

How MMM Labs keeps Agentic MMM from hallucinating

The Claude connector doesn't let the language model run the statistics itself. It commissions the computation from MMM Labs' backend and reports back only what the backend returns.

In practice, that means:

The model calls one of 50+ typed MMM Labs tools with defined inputs and outputs, rather than writing its own statistical code.
Server-side validation rejects malformed requests before they run, instead of the connector guessing and retrying.
Bayesian sampling runs on MMM Labs' GPU cluster, so pooled models across 100+ cross-sections complete without timing out.
Every ROI, R², and contribution figure traces back to a specific tool call, never to the model's memory.
Consequential actions still require human approval, and every call is logged to an audit trail.

If a tool call fails, the model is expected to say so rather than fill the gap with a plausible-sounding number.

What you can do with Agentic MMM:

Start with data you own

Data ingestion

Create an MMM Labs project with my attached sales and marketing data.

Enrich your data

External signals

Add a monthly data source with the same min/max dates covering average temperature (°F), CPI inflation rate (YoY %), unemployment rate (%), effective fed funds rate (%), UMich consumer sentiment index, and retail sales growth (YoY %).

Multiple models in one shot

Multi-engine GPU

Run all available models in MMM Labs and activate GPU computations for speed on my panel data. Compare the results and tell me what is working.

Add calibration priors

Business priors

ROI for OOH contradicts our experimentation. Incorporate an ROI range of [0.2 – 4.0].

Budget effectively

Optimization

Given the best calibrated model, help me understand what is my annual budget should be.

Scenario planning

What-if

Add $20.0MM to my budget in $1.0MM increments and report on changes in allocations and ROI’s.

One prompt, or four — two ways to run the same workflow

Every step above can also be issued as a single instruction: connect to the dataset, categorize variables and drop the noise, run multiple engines and flag any broken fits, drill into marginal ROI, run a budget-neutral optimization, fold in business priors, and write up a one-page recommendation citing every tool call. ScanmarQED calls this the master prompt.

	Step-by-step	Master prompt
Best for	Learning the workflow, steering mid-analysis	Repeat runs once you trust the process
What you see	Every guardrail firing in real time	A completed readout with citations attached
Trade-off	More back-and-forth	Less visibility into intermediate decisions

Most teams start with step-by-step to see the guardrails work, then move to the master prompt for routine updates.

How to connect Claude to MMM Labs

The MMM Labs connector is listed in Anthropic's Claude connector directory, so anyone using Claude can find and add it directly, no invite, custom URL, or developer setup required:

In Claude, go to Settings → Connectors.

Search the directory for "MMM Labs" and add it.

Being listed in the directory means the connector is publicly discoverable. Any Claude user can add it in a couple of clicks. Using it, though, still requires a valid MMM Labs account: the connector authenticates against your MMM Labs login, so adding it without an account gets you to the sign-in prompt and no further. For ChatGPT or Gemini, where MMM Labs isn't listed in a first-party directory yet, the connector is added the same way as any custom MCP connector, via its MCP URL, provided by your ScanmarQED contact.

Important: Permissions follow your user role across all three doors, nothing escalates by going through chat, and every action still lands in the same audit trail as the app.

The connector also extends what the platform can ingest: inside a chat, you can ask it to research and merge in macroeconomic or weather data, or connect to an ad platform to pull spend data directly, and MMM Labs' data hub handles the resulting frequency mismatches (turning quarterly figures into weekly ones, for instance) using the same defaults it applies anywhere else in the platform.

MMM Labs website: https://mmmlabs.ai

Key takeaways

MMM Labs now connects to Claude, ChatGPT, and Gemini through an MCP connector that reads and writes the same backend as the MMM Labs app, so results never drift between chat and app.

The connector doesn't let the AI compute statistics itself. It commissions validated tool calls against MMM Labs' backend and reports only what comes back, with a full audit trail.

Four core workflows are available today: data readiness triage, multi-engine model comparison, business-prior calibration, and budget-neutral optimization.

Business Priors, MMM Labs' newest feature, lets you fold in ROI evidence from experiments or past models as a statistical prior instead of a manual override, in one demo run, it changed which modeling engine was best.

The connector is listed in Anthropic's Claude connector directory, so any Claude user can find and add it in a couple of clicks, a valid MMM Labs account is still required to run anything through it.

Frequently Asked Questions

Does the MMM Labs Claude connector hallucinate results?

Every number it reports comes back from a specific MMM Labs tool call, not from the language model's memory. If a call fails, the design intent is for the assistant to say so rather than fill the gap with a plausible-sounding figure. Server-side validation and human approval on consequential actions back this up, alongside a full audit trail of every call made.

Is my data safe when I connect Claude to MMM Labs?

The connector uses OAuth tied to your existing MMM Labs login, so the assistant acts with your account's own role and permissions, and every action it takes is logged to the same audit trail as the web app. Data handling and storage settings for the in-app AI assistant are configurable per account, including for EU-based customers.

Do I have to use Claude specifically, or can my team use whatever assistant it already has?

The MCP connector works with Claude, ChatGPT, or Gemini, whichever assistant your team already uses. The in-app AI assistant (Door 2) also lets you pick a model. Anyone with MMM Labs credentials can connect any supporting assistant.

Can I add new data sources, like weather or a new ad channel, just by asking?

Yes. You can ask the connector to research and merge external data like macroeconomic or weather series into your file, or use MCP connectors to pull spend data directly from an ad platform. MMM Labs' data hub then handles frequency conversion and other data management automatically, using the same defaults available in the app.

Do I need to be a data scientist to use this?

No. The platform runs on smart defaults for variable categorization, priors, and transformations, and the AI assistant will flag when it's used a default and offer to change it. Advanced users can still adjust every parameter directly in the app or through chat.

Do I need an invite or developer setup to add the MMM Labs connector to Claude?

No. The connector is listed in Anthropic's Claude connector directory, so it's publicly discoverable. Anyone can search for it in Claude's Settings → Connectors and add it directly. Adding the connector doesn't grant access on its own: you still need to sign in with a valid MMM Labs account before it can run anything.