Agent skill · marketing growth · aradotso
awesome-marketing-science-guide
Guide to curated marketing science resources including MMM, geo experiments, causal inference, attribution, and Bayesian methods
Why this skill is useful
Provides a curated list of niche marketing measurement libraries and methodologies that the AI wouldn't reliably generate on its own.
What it needs
Requires python, r installed locally. About 8k tokens when loaded. Last updated 2026-08-05. 8 stars on the source repository.
What this skill does
Awesome Marketing Science Guide Skill by ara.so — Marketing Skills collection. This skill provides guidance on the Awesome Marketing Science resource collection — a curated list of open-source libraries, papers, and methodologies for marketing measurement, including Media Mix Models (MMM), geo incrementality testing, multi-touch attribution (MTA), causal inference, and Bayesian approaches. Overview Awesome Marketing Science is a comprehensive resource hub covering: Media Mix Modeling (MMM): Measure channel-level ROI and optimize budget allocation Geo Experimentation: Test incrementality through matched markets and geo lift Attribution: Multi-touch and algorithmic attribution models Causal Inference: Difference-in-differences, synthetic control, uplift modeling A/B Testing & Experimentation: Online controlled experiments and variance reduction Ghost Ads & Platform Incrementality: Measure true ad effectiveness Repository Structure The collection is organized into: 1. Start Here / Must Read: Essential papers and packages for building measurement foundations 2. Open Source Libraries: Production-ready code organized by discipline 3. Papers & Research: Academic and industry research 4. Blogs & Tutorials: Practical implementation guides Key Libraries by Category Media Mix Modeling (MMM) Robyn (Meta) LightweightMMM (Google) PyMC-Marketing Geo Experimentation GeoLift (Meta) Matched Markets (Google) Multi-Touch Attribution PyChattr ChannelAttribution (R) Causal Inference CausalPy DoWhy EconML (Microsoft) A/B Testing & Experimentation CUPED Implementation Common Workflows Building a Marketing Measurement Stack Running Geo Experiments End-to-End Configuration & Best Practices MMM Hyperparameter Tuning Prior Elicitation for Bayesian MMM Troubleshooting MMM Model Not Converging Geo Test Power Issues Attribution Model Interpretation Environment
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aradotso/awesome-marketing-science-guide