MMM Skills in Claude: The BlueAlpha Marketing Plugin

The BlueAlpha MMM skills put a team of senior measurement analysts inside Claude. Ten skills connect directly to your Meridian marketing mix model and handle the jobs that turn a model into decisions: health-checking the model itself, reading weekly performance, mapping channel saturation, projecting launch timing, simulating budget shifts, comparing scenarios, routing channels into "act / test / fix" buckets, building quarterly test roadmaps, reconciling MMM with platform ROAS, and going deep on any single channel. Each skill follows the same loop: Analyze, Decide, Plan, Act, Prove. This page is the index. Pick the skill that matches your job-to-be-done and jump to the prompt that runs it.

If you've spent six figures on a marketing mix model, you've felt the gap. You have a beautiful posterior. You have channel ROIs with credible intervals. You have a model your data science team is proud of. And every Monday morning, the planner asks: "so what do I actually do this week?" and the model goes silent. Because a model is not a decision.

The BlueAlpha MMM skills close that gap. Each one is an agent that turns one slice of the model into a real call: trust the channel or test it, scale it or cut it, launch it now or wait, ship the bold plan or the defensive one. The skills don't replace your data scientist. They extend the model's reach so the entire growth team can act on it without re-asking the data scientist every time.

All Ten MMM Skills in the BlueAlpha Marketing Plugin

Read what's happening:

• mmm-health-check: Trust-grades the model itself across convergence, prior dominance, decomposition coherence, recency, and fit. Lead any conversation with the grade; never act on a model nobody has audited.
  
  

• mmm-performance-digest: The Monday-morning MMM read. Scoreboard, period-over-period change, per-channel ROI table, three-paragraph narrative.
  
  

• mmm-channel-deep-dive: Single-channel report card. Average ROI, marginal ROI, saturation position, adstock decay, trust diagnostics, and a clear cut / hold / scale / test verdict for one channel at a time.

Decide where the money goes:

• mmm-saturation-report: Maps where every channel sits on its response curve. Surfaces the headroom triplet (where the next dollar earns the most) and the pruning triplet (where the current dollars are wasted).
  
  

• mmm-launch-timing: "If I launch this on Monday, when do I see the impact?" Combines adstock half-life with saturation curves to project a week-by-week impact ramp, time-to-50%, time-to-80%, and steady-state per channel.
  
  

• mmm-budget-reallocator: Simulates a proposed budget shift against the model's posterior and projects revenue lift with credible intervals. The closed-loop "should I actually move this money?" workflow.
  
  

• mmm-scenario-planner: Runs 3-5 candidate budget plans side-by-side with comparison-matrix output. The quarterly planning artifact you bring to the leadership review.

Decide what to trust:

• mmm-trust-router: Per-channel classifier: Trust MMM (act directly), Validate first (test before acting), or Model insufficient (fix before deciding). Tells you which channels' reads are decision-grade and which need an incrementality test.
  
  

• mmm-test-roadmap: Turns the trust-router output into a sequenced quarterly testing calendar: which tests run in which weeks, packed by priority and budget, with pre-staged expected lifts as test priors.
  
  

• mmm-attribution-reconciler: Cross-references MMM channel ROI against platform-reported ROAS (Google Ads, Meta, etc.). Tags each channel as Agree, Platform overclaims, or Platform underclaims, and routes disagreements straight into the test roadmap.

Analyze > Decide > Plan > Act > Prove

Every MMM skill follows the same loop, the same one the Google Ads skills, Meta Ads skills, LinkedIn Ads skills, and TikTok skills follow:

1. Analyze. The agent pulls from your live Meridian posterior: ROI, marginal ROI, saturation curves, adstock parameters, prior-posterior comparisons, reconciled performance tables. You don't go digging through the model; the agent brings the relevant slice to you.

2. Decide. The agent produces a recommendation with the signal that backs it. Every recommendation includes the credible interval, the trust caveat, and the rationale.

3. Plan. Every recommendation comes with a sequenced action plan that separates what to do now from what to test first. Budget moves, creative swaps, and structural changes are ordered by impact and risk so nothing ships out of sequence.

4. Act. The agent produces a deployment-ready output: a reallocation table you can hand to the activation layer, a test card you can hand to the incrementality runner, a one-page channel verdict you can hand to the channel owner. Not a slide in a deck; a spec you can ship the same day.

5. Prove. After changes ship, the loop closes with a measurement check: did the move produce the predicted lift? Geo holdout results, MMM refits, and period-over-period reads feed back into the next cycle so every subsequent recommendation is sharper than the last.

A single Head of Growth with the BlueAlpha MMM skills connected manages what used to require a measurement team plus a dedicated planner plus the eternal back-and-forth with the data scientist who actually understands the model.

Who the MMM Skills Are For

• You're a Head of Growth and you have an MMM your data team is proud of, but every planning conversation still starts with "let me get the latest export from Maria."

• You're a CMO and you need to defend the marketing budget to a CFO who keeps asking "are you sure?" and you want causal evidence underneath every answer, not a screenshot from Meta Ads Manager.

• You're an MMM analyst tired of rebuilding the same reallocation memo every quarter, with the same caveats hand-written every time.

• You're a measurement scientist who built the model and want the rest of the org to be able to use it without you in the room.

BlueAlpha delivers the plugin through forward-deployed engineering. A senior growth partner embeds with your team, builds the measurement and execution stack directly, and trains your team to run the agents independently. You keep the seat. The agents eliminate the hours. The knowledge base stays yours.

How to Pick the Right Skill

How Does the BlueAlpha Plugin Connect to Your Meridian MMM?

Every MMM skill in the BlueAlpha Marketing Plugin is backed by an MCP connector that gives Claude direct, permissioned access to your Meridian model artifacts. MCP-native means the whole system lives inside Claude, Codex, or any AI workspace your team already uses. No separate dashboard to check, no model export to email around.

The BlueAlpha MMM connector gives Claude the ability to:

• List every Meridian model registered in your workspace (production models plus sandbox)

• Pull model summaries: channels, training time range, MCMC config, convergence diagnostics

• Read average ROI, marginal ROI, saturation curves, adstock parameters, and prior-posterior comparisons for any channel

• Compute reconciled performance tables matching what's in your BlueAlpha dashboard

• Run budget reallocation simulations against the posterior and return projected revenue lifts with 90% credible intervals

• Generate response curves at any operating point, with KPI-to-revenue conversion via the registered RPK schema

• Surface weekly channel contribution histories across the training window for trend and decomposition analysis

The connector handles authentication, model loading, posterior caching, and reconciliation against your registered actuals. One sign-in, no model pickles to share, no config files.

Getting Connected

There are two pieces to install: the BlueAlpha MCP connector (so Claude can read your Meridian model and your Google Ads data) and the BlueAlpha Marketing Plugin itself (the skills that put that data to work). Total setup time is about a minute.

Step 1: Install the BlueAlpha MCP connector

1. Open Settings in the Claude desktop app

2. Go to Connectors > Add custom connector

3. Name it: BlueAlpha MCP

4. URL: https://mcp.bluealpha.ai/mcp

5. Click Connect and sign in with your BlueAlpha account

6. 
   

Step 2: Install the plugin

Option A: Cowork (drag-and-drop)

1. Go to github.com/bluealpha-labs/bluealpha-plugins

2. Click Releases on the right rail and open the latest release

3. Expand Assets and click bluealpha-marketing-plugin.plugin to download

4. Drag the downloaded file into an open Cowork session and click Install when prompted

Option B: Claude Code (slash commands)

/plugin marketplace add <https://github.com/bluealpha-labs/bluealpha-plugins.git>

/plugin marketplace add <https://github.com/bluealpha-labs/bluealpha-plugins.git>

Step 3: Try a skill

"Run a health check on my net_sales MMM and tell me if I can trust it for this quarter's planning."

Skill Reference: Example Prompts

Every skill is invoked with a sentence. Below is a copy-paste prompt for each one, plus a screenshot from a real run on a production MMM model (net_sales, 10 channels, 104 weeks of training data) so you know what output to expect.

mmm-health-check

Trust-grade my net_sales MMM. Tell me whether the model is decision-grade, what's prior-dominated, and what the data scientist needs to fix before this quarter's planning.

The skill produces an overall A/B/C/F grade plus a subgrade breakdown across seven dimensions: convergence, recency, holdout validation, prior independence, decomposition coherence, reconciliation gap, and channel-level plausibility.

Real example from net_sale: the model landed at B. Convergence and Holdout flagged "Cannot verify" because sample_statsand holdout strategy weren't persisted in the trace; Prior Independence failed because 10 of 10 channels were prior-dominated on the adstock parameter; Reconciliation Gap passed cleanly. Result: usable model, but every downstream recommendation needs the prior-dominance caveat attached.

mmm-performance-digest

Generate the monthly performance digest for net_sales. Trailing 4 weeks vs prior 4 weeks.

Real example: trailing 4 weeks showed paid spend up 41% while paid revenue declined 5.7%, reconciled ROI compressing from 3.9x to 2.6x. The narrative led with that story and named Meta (31% of paid spend, 0.34x ROI in the window) as the primary contributor.

mmm-channel-deep-dive

Give me the full read on Meta in net_sales: ROI, marginal ROI, saturation position, adstock decay, trust diagnostics, and a clear verdict.

Real example: Meta ran an average ROI of 0.38x with marginal ROI at 0.17x, sub-breakeven at the margin. 78.8% saturated. Adstock half-life 2.0 weeks, but the adstock parameter is prior-dominated. Verdict: Cut, by 30-40% as a first move, with reallocation to Google Ads (Shopping). Run an incrementality test on the cut to confirm Meta isn't picking up halo effects the MMM is missing.

mmm-saturation-report

Show me where every channel in net_sales sits on its response curve.

Real example: every channel ran above 60% saturation, so there is no "headroom" in the strict sense. Best scale candidate: Google Ads (Shopping) at 79% saturation but 0.73 mROI (the highest in the mix). Strongest cut candidate: Meta at 79% saturation and 0.17 mROI (the lowest). Verify-first: TikTok and Agentio, small bases with wide credible intervals.

mmm-launch-timing

If I ramp TikTok from $7.6K/week to $12K/week starting Monday in net_sales, when will I see the impact?

Real example: TikTok's adstock half-life of 1.0 week means 50% of steady-state lift in week 1, 87% by week 3, full ~98% by week 6. Steady-state projected weekly contribution at $12K spend: ~$57K (vs. ~$38K today). Caveat: TikTok's adstock parameter is prior-dominated, so the timing read is directional.

mmm-budget-reallocator

Run a defensive reallocation in net_sales: cut Meta by 36%, cut TikTok by 21%, redirect to Google Ads (Shopping). Same total spend plus or minus 5%.

Real example: projected +$12.6K/week revenue (+2.4%) at virtually flat total spend ($85K to $89K). The 90% credible interval crossed zero on the low side, meaning directionally favorable but not bet-the-farm confident. Skill recommendation: run smaller pilot version first.

mmm-scenario-planner

Compare three scenarios in net_sales: status quo, defensive reallocation, and bold expansion (+43% spend).

Real example: Status Quo $494K/week. Defensive $507K (+2.4% at flat spend). Bold $530K (+7.2% but at +$36K spend, incremental dollars buying revenue at roughly breakeven). Recommendation: Defensive. Better cost-adjusted bet, no exposure to additional spend on prior-dominated channels.

mmm-trust-router

Route every channel in net_sales into Trust MMM / Validate first / Model insufficient.

Real example: 0 Trust MMM / 3 Validate first / 7 Model insufficient. Validate-first: Meta, Google Ads (Shopping), Google Ads (Search). Model insufficient: the other seven, with wide ROI CIs combined with prior-dominated response curves and moderate-to-high channel correlations. This routing is consistent with the B health-check grade.

mmm-test-roadmap

Build me a Q1 test roadmap for net_sales. Test budget $200K, concurrency cap 2.

Real example: Q1 roadmap slotted three tests: Meta cut validation (weeks 1-6, $35K), Google Ads (Shopping) scale validation (weeks 3-12, $42K), TikTok structured spend ramp (weeks 9-13, $30K). Total Q1 spend $107K of $200K; $93K reserved as buffer.

mmm-attribution-reconciler

Reconcile what Google Ads is saying about ROAS vs what the MMM says for every channel in net_sales.

Illustrative example: Branded Search where Google says 6.2x ROAS but MMM says 2.1x: Platform overclaims by 66%, trust MMM, cut the platform-led budget recommendation. YouTube where Google says 0.8x but MMM says 3.4x: Platform underclaims by 325%, validate with a test before scaling. Display where both sources land near 1.5x: Agree, ship the decision either way.

Why BlueAlpha MMM Skills Beat Another Measurement Deck

There are two kinds of products in marketing measurement, and BlueAlpha is neither.

The first is rigorous but passive: the bespoke MMM consultancy that produces a beautiful posterior, hands you a slide deck, and disappears for the quarter. The math is right; nothing happens with it.

The second is actionable but shallow: the platform attribution stack that produces fast recommendations from last-touch math that doesn't survive iOS privacy or a skeptical CFO.

BlueAlpha is the third option: causal measurement underneath, real agents producing deployment-ready specs, MCP-native. Not a measurement vendor. Not a model in a slide deck. The decision layer underneath the model.

The difference shows up in results. When BlueAlpha ran incrementality tests for Cann, AppLovin's own dashboard swung between 2x and 16x ROAS and the third-party tool Triple Whale showed 4.77x, but the measured incremental lift was zero, saving Cann $480K per year. For Klover, measurement cut Meta iOS spend by 50% with zero lost conversions. For beehiiv, causal measurement caught a channel overreporting performance by 345%. For Pettable, nine months of causal measurement reduced blended CAC by 14%, unlocking $2.12M in annualized savings.

FAQ

Do I need to bring my own MMM?

If you have an MMM already, the BlueAlpha MMM skills work against it via the Meridian standard. If you don't, BlueAlpha can build one for you.

What if my MMM isn't built in Meridian?

The current connector reads Meridian model artifacts. If you're running a different framework (Robyn, internal Stan, vendor model), reach out. We've ported other formats and the skill layer is model-agnostic.

Will the skills change my MMM?

No. The skills are read-only against the model itself. They read the posterior, compute derived metrics, run simulations against the posterior, and produce recommendations. They do not retrain, modify, or write back to the model.

Will the skills make changes to my ad accounts automatically?

Not currently. The MMM skills produce reallocation recommendations, scenario comparisons, test cards, and channel verdicts. You execute the spend changes through your activation layer. On-platform execution is on the roadmap.

How often should I run each skill?

mmm-health-check and mmm-performance-digest: weekly or monthly.
mmm-saturation-report and mmm-trust-router: monthly or quarterly.
mmm-test-roadmap: quarterly.
mmm-channel-deep-dive, mmm-launch-timing, mmm-budget-reallocator, and mmm-scenario-planner: event-triggered.
mmm-attribution-reconciler: quarterly.

My MMM has wide credible intervals. Can the skills still produce a decision?

Yes, and the skills surface the uncertainty rather than hiding it. The trust router will route uncertain channels into Validate-first, the budget reallocator and scenario planner attach the 90% CI to every projection, and the health-check skill will tell you which subgrades are failing.

Can I use the skills with multiple MMMs?

Yes. The plugin supports any number of registered Meridian models. You specify the model name in your prompt and the skill routes against that posterior.

Do the MMM skills work with the Google Ads, Meta, LinkedIn, and TikTok skills?

Yes. They ship in the same plugin (bluealpha-marketing-plugin) and are designed to compose.
The natural planning loop is: mmm-health-check > mmm-trust-router > mmm-attribution-reconciler > mmm-test-roadmap > mmm-scenario-planner > mmm-budget-reallocator > mmm-launch-timing > hand off to incrementality-test-runner for any test scheduled in the roadmap.

Where can I find the full setup documentation?

The complete installation walkthrough, authentication scopes, troubleshooting, and the full tool reference live on the BlueAlpha MCP documentation page.

My current MMM only updates monthly. How is this different?

The BlueAlpha MMM skills run against a model that refits weekly. When you run mmm-performance-digest on Monday, you're reading last week's data through last week's posterior, not a quarterly snapshot. The budget-reallocator and scenario-planner project forward from the most recent refit. If your model updates monthly, BlueAlpha can refit it on a weekly cadence as part of the engagement.

How does BlueAlpha compare to other MMM software?

For a side-by-side comparison of MMM platforms, see Best Marketing Mix Modeling Software. The BlueAlpha MMM skills are not a standalone MMM tool. They are the skill layer that sits on top of a fitted Meridian model and turns the posterior into decisions.

Your Next Step

Pick the skill that matches the question you're trying to answer today, copy the prompt above, and run it in Claude. If you're new to the MMM skills, start with mmm-health-check. It's the right first move on any model you haven't audited, and it produces the trust grade that every downstream skill's caveats hang on.

If your model is fresh from a B-grade or better, mmm-trust-router is the second prompt to run. It tells you which channels you can act on directly and which need an incrementality test before you bet meaningful budget on them. From there, the planning loop runs itself.

If you haven't installed the plugin yet, setup takes about a minute. To see the full platform, visit the product page. If you'd rather see the skills run on your model before installing, book a walkthrough.

The same plugin ships Google Ads skills, Meta Ads skills, LinkedIn Ads skills, and TikTok Ads skills. For a side-by-side comparison of all MCP servers, see the full MCP comparison.

For deeper walkthroughs: How to operationalize your MMM with Claude and MCP and How to build your first MMM (no code required).