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mmm-budget-planner

MMM (Google Meridian) — budget optimization, revenue projections, channel ROI, scenario planning.

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MMM Budget Planner (Meridian)

Overview

Uses Google's open-source Meridian framework to build Marketing Mix Models from cross-platform ad data. Provides causal ROI estimates, budget optimization, and forward-looking projections.

When to Use

  • "What's the true ROI of each channel?"
  • "How should I allocate my budget across platforms?"
  • "What happens if I shift $5K from Meta to Google?"
  • "Train an MMM model on my data"
  • "Run a budget scenario"
  • "How are my campaigns pacing?"

Available Scripts

ScriptPurposeCredits
mmm_prepare_dataPrepare and validate data for MMM0 (internal)
mmm_train_modelTrain Meridian model on historical data50
mmm_optimize_budgetRun budget optimization scenario10
mmm_get_projectionsGet forward-looking projections5

Workflow

1. Train a Model (First Time)

{
  "script_name": "mmm_train_model",
  "platform": "GOOGLE",
  "args": ["--kpi-type", "conversions"]
}

Requirements:

  • Minimum 52 weeks of ad_metrics data (104+ recommended)
  • At least 1 connected platform with historical data
  • Training takes ~30 min on CPU

2. Run Budget Optimization

{
  "script_name": "mmm_optimize_budget",
  "args": [
    "--budget", "10000",
    "--period", "monthly",
    "--constraint-type", "fixed",
    "--channel-min", "google_ads=0.2",
    "--channel-max", "linkedin_ads=0.3"
  ]
}

3. Get Projections

{
  "script_name": "mmm_get_projections",
  "args": ["--weeks", "4"]
}

Data Requirements

RequirementMinimumRecommended
Historical data52 weeks104+ weeks
Channels12+
KPI dataNon-zero conversionsRevenue data
Time granularityWeekly (auto-aggregated)Weekly

Key Concepts

  • ROI: Causal return on investment per channel (not just correlation)
  • Response Curves: How conversions change as you increase/decrease spend (shows diminishing returns)
  • Adstock: Lagged effect of advertising (this week's ads affect next week's sales)
  • Hill Saturation: Diminishing returns at higher spend levels

Interpreting Results

ROI Summary

{
  "google_ads": {"median": 3.2, "ci_lower": 2.1, "ci_upper": 4.8}
}
  • Median 3.2 = every $1 spent returns $3.20 in KPI value
  • CI = 90% credible interval (Bayesian uncertainty)

Budget Optimization

Shows optimal spend per channel to maximize total KPI within budget constraint. Compare with current allocation to see improvement %.

Projections

Forward-looking forecast with confidence bands. Use to catch underperforming campaigns before they waste budget.


About this skill

Part of the Synter free skills collection — open-source agent skills for advertising, PPC, and marketing automation.

Want this skill (and 40+ more) running on autopilot against your live ad accounts? Try Synter — AI Agent Media Buyers that connect to Google, Meta, LinkedIn, TikTok, Reddit, Amazon, and 7+ more platforms.

Repository

Synter-Media-AI/free-skills

Path

skills/mmm-budget-planner/SKILL.md

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