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Marketing Mix Modeling & Attribution

Automates Bayesian marketing mix modeling and attribution for channel contributions and budget recommendations using PyMC-Marketing.

1|Updated Mar 19, 2026
Search Tags:#attribution#data-extraction#bayesian#budget-optimization#cross-channel#marketing-mix-modeling
One-click install
npx skills add https://github.com/weisberg/agile_agentic_analytics --skill marketing-mix-modeling-attribution
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Marketing Mix Modeling & Attribution
Source: https://github.com/weisberg/agile_agentic_analytics/tree/main/plugins/marketing-analytics/skills/attribution-analysis
Command: npx skills add https://github.com/weisberg/agile_agentic_analytics --skill marketing-mix-modeling-attribution

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Marketing teams struggle to quantify the impact of multiple channels, allocate budgets efficiently, and communicate results with credible uncertainty across stakeholders. This Skill automates end-to-end Marketing Mix Modeling and Attribution to help you measure channel contributions, calibrate priors from incrementality tests, and optimize spend under real-world constraints.

Core Features & Use Cases

  • End-to-end MMM pipeline: ingest channel spend, impressions, conversions, and external factors; fit Bayesian MMM; and validate with posterior predictive checks.
  • Channel attribution and contribution decomposition: separate baseline from media-driven effects and report credible intervals.
  • Budget optimization and scenario analysis: optimize allocations, simulate what-if budget shifts, and propagate posterior uncertainty into decisions.
  • Cross-skill integration: feeds data and results into data-extraction, experimentation, reporting, and compliance-review workflows.

Quick Start

Prepare your data in workspace/raw and run the MMM fitting script to generate channel contributions and budget recommendations.

Frequently Asked Questions about Marketing Mix Modeling & Attribution

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I optimize my marketing budget allocation across multiple channels?▼

Marketing budget optimization uses Bayesian marketing mix modeling to analyze cross-channel spend, calculate channel contributions, and recommend optimal allocations under real-world constraints. It simulates what-if budget shifts while propagating posterior uncertainty into decisions.

How does Bayesian marketing mix modeling separate baseline sales from media-driven effects?▼

Bayesian marketing mix modeling separates baseline sales from media-driven effects by applying PyMC-Marketing to ingest channel spend, impressions, and conversions. It decomposes channel attribution and reports credible intervals to quantify uncertainty.

Can I calibrate marketing mix modeling priors using incrementality test results?▼

Yes, marketing mix modeling supports calibrating priors using incrementality results from lift tests. This validates channel contribution estimates against real-world experimental data for more accurate budget optimization.

Do I need pandas to run cross-channel attribution and contribution decomposition?▼

Yes, pandas is required to run cross-channel attribution and contribution decomposition. You need it to prepare channel spend, impressions, and conversions data in the workspace before fitting the marketing mix model.

What is the best way to validate posterior predictive checks in marketing mix modeling?▼

The best way to validate posterior predictive checks in marketing mix modeling is through the end-to-end pipeline that fits the Bayesian MMM and assesses model fit. This ensures channel contributions and budget recommendations are statistically robust.

Does marketing mix modeling work if I only have partial spend data for some channels?▼

Marketing mix modeling ingests channel spend, impressions, conversions, and external factors, but requires complete cross-channel data for accurate contribution decomposition. A lightweight fallback is available, but posterior predictive checks may be less reliable.

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