awesome-marketing-science-guide

Curate marketing science resources for MMM, geo experiments, attribution, causal inference, and Bayesian methods.

8|Updated May 16, 2026

Search Tags:#attribution #causal inference #marketing science #media mix modeling #geo experimentation

Ready to run

npx skills add https://github.com/Aradotso/marketing-skills --skill awesome-marketing-science-guide

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: awesome-marketing-science-guide
Source: https://github.com/Aradotso/marketing-skills/tree/main/skills/awesome-marketing-science-guide
Command: npx skills add https://github.com/Aradotso/marketing-skills --skill awesome-marketing-science-guide

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pymc-marketing, lightweight-mmm, geolift, pychattr, causalpy, econml, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill helps marketing professionals navigate and implement advanced marketing measurement tools and methodologies, saving time and improving accuracy.

Core Features & Use Cases

Comprehensive Resource Hub: Offers curated resources for media mix modeling, geo experimentation, attribution, causal inference, and Bayesian methods.
Guided Implementation: Provides practical examples and code snippets for building and deploying marketing measurement stacks.
Use Case: If you're looking to set up a media mix model for your marketing campaigns, this Skill provides the foundational knowledge and tools to get started.

Quick Start

Use the 'awesome-marketing-science-guide' skill to learn about Media Mix Modeling (MMM) and its applications in marketing.

Frequently Asked Questions about awesome-marketing-science-guide

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

FAQPage Schema

How do I implement media mix modeling for marketing measurement?▼

Media mix modeling implementation is guided through this Skill using Python libraries like pymc-marketing and lightweight-mmm. It provides foundational knowledge, practical code snippets, and curated resources to build and deploy MMM stacks for marketing campaigns.

What is geo experimentation and how does it apply to marketing campaigns?▼

Geo experimentation is a marketing science technique used to measure causal impact of campaigns in specific geographic areas. This Skill provides resources and implementation guidance using the geolift library to design and analyze geo-based tests.

How do I set up attribution models using causal inference?▼

Attribution models using causal inference can be implemented using libraries like causalpy, pychattr, and econml. This Skill offers curated resources and practical examples to help build measurement tools applying these methodologies to marketing data.

Do I need programming skills to use marketing science tools and methodologies?▼

Yes, basic programming skills are required to implement marketing science tools and methodologies. Familiarity with marketing concepts and Python is necessary, as this Skill provides code snippets and guidance for libraries like pymc-marketing, lightweight-mmm, and econml.

What's the best way to compare Bayesian methods for media mix modeling?▼

Comparing Bayesian methods for media mix modeling is supported through curated resources covering libraries like pymc-marketing and lightweight-mmm. This Skill provides practical examples and foundational knowledge to evaluate and implement different Bayesian approaches.

When should I use causalpy versus econml for marketing measurement?▼

Causalpy and econml are both provided as resources for causal inference in marketing measurement. This Skill offers guidance on their applications, helping you navigate which library fits your specific marketing science implementation needs.