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marketing-mix-modeling-mmm-pipeline

Automate Marketing Mix Modeling with Python for data decomposition, adstock, saturation, OLS regression, and budget optimization.

8|Updated May 16, 2026
Search Tags:#python#budget optimization#channel analysis#ROI calculation#marketing mix modeling
One-click install
npx skills add https://github.com/Aradotso/marketing-skills --skill marketing-mix-modeling-mmm-pipeline
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: marketing-mix-modeling-mmm-pipeline
Source: https://github.com/Aradotso/marketing-skills/tree/main/skills/marketing-mix-modeling-mmm-pipeline
Command: npx skills add https://github.com/Aradotso/marketing-skills --skill marketing-mix-modeling-mmm-pipeline

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, statsmodels, scipy, jupyter, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the creation of a Marketing Mix Modeling (MMM) pipeline, enabling efficient analysis of marketing channel effectiveness, budget allocation optimization, and ROI calculation.

Core Features & Use Cases

  • Channel Effectiveness Analysis: Decompose historical sales into contributions from each marketing channel.
  • Budget Optimization: Optimize marketing budget allocation using regression analysis and optimization techniques.
  • ROI Calculation: Calculate marketing ROI by channel to improve decision-making.
  • Use Case: A marketing manager can use this Skill to analyze the effectiveness of various marketing channels, optimize the budget for the upcoming quarter, and calculate the expected ROI for different spending scenarios.

Quick Start

Build a marketing mix model using the marketing-mix-modeling-mmm-pipeline skill.

Frequently Asked Questions about marketing-mix-modeling-mmm-pipeline

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

FAQPage Schema
How do I optimize marketing budget allocation across different channels using Python?▼

Marketing budget allocation is optimized by applying OLS regression and scipy optimization to decompose historical sales, analyzing adstock and saturation to calculate the highest ROI per channel.

What is the best way to calculate marketing ROI by channel for historical sales data?▼

Marketing ROI by channel is calculated by decomposing historical sales data using statsmodels OLS regression, isolating individual channel contributions to determine the exact return on investment.

How does adstock and saturation transformation work in a Marketing Mix Modeling pipeline?▼

Adstock and saturation transformations work by mathematically modeling the delayed carryover effect and diminishing returns of marketing spend, allowing OLS regression to accurately attribute sales.

Can I use pandas and statsmodels to build an MMM pipeline for channel effectiveness analysis?▼

Yes, you can use pandas and statsmodels to build an MMM pipeline, leveraging data manipulation for sales decomposition and OLS regression to quantify individual channel effectiveness.

Do I need Jupyter to run marketing mix modeling and budget optimization scripts?▼

Jupyter is a listed dependency for running the marketing mix modeling scripts, providing an interactive environment to execute the Python pipeline and visualize channel analysis and budget optimization.

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