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

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

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

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