Marketing Measurement: A Complete Guide

Three self-contained Jupyter notebooks covering the full marketing measurement stack — MMM, MTA, and Triangulation — from theory to implementation, designed for data scientists.

Notebooks

1. MMM_Complete_Guide.ipynb — Marketing Mix Modeling (4–6 hrs)

1. Foundational Theory — History, key concepts, and the science behind MMM
2. Mathematical Framework — Adstock, saturation curves, and the complete MMM equation
3. Implementation — Frequentist and Bayesian MMM built from scratch
4. Advanced Topics — Time-varying effects, hierarchical models, and two-sided marketplace considerations
5. Business Applications — ROI calculation, budget optimization, and scenario planning

2. MTA_Complete_Guide.ipynb — Multi-Touch Attribution (4–6 hrs)

1. Rule-Based Models — Last-click, first-click, linear, time-decay, and position-based attribution
2. Algorithmic Models — Markov chains, Shapley values, and logistic regression-based attribution
3. Survival Analysis — Time-to-conversion modeling and hazard-based attribution
4. Incrementality & Causal Inference — PSM, geo-lift, ghost ads, and conversion lift studies
5. Privacy & Production — Cookie deprecation, iOS 14.5+, cross-device tracking, building pipelines

3. Triangulation_Guide.ipynb — Measurement Triangulation (3–4 hrs)

1. Why Triangulation — The limits of any single measurement method
2. MMM vs. MTA — Mapping their complementary strengths and blind spots
3. Unified Measurement Framework — Combining all three methodologies
4. Calibrating MMM with Experiments — Using incrementality results to set Bayesian priors
5. Reconciliation Playbook — Diagnosing and resolving model vs. platform disagreements

Recommended order: MMM → MTA → Triangulation.

Prerequisites

• Python 3.10+
• Basic statistics (regression, probability distributions)
• Familiarity with marketing concepts (helpful but not required)

Getting Started

# 1. Clone the repo
git clone https://github.com/mpabon10/A_Full_MMM_Guide.git
cd A_Full_MMM_Guide

# 2. Create and activate a virtual environment
python3 -m venv .venv
source .venv/bin/activate   # Windows: .venv\Scripts\activate

# 3. Install dependencies
pip install -r requirements.txt

# 4. Launch a notebook
jupyter notebook MMM_Complete_Guide.ipynb

Dependencies

Package	Purpose
numpy / pandas	Data manipulation
scipy / statsmodels	Statistical modeling
scikit-learn	Machine learning utilities
matplotlib / seaborn	Visualization
networkx	Graph-based attribution (Markov chains)
tqdm	Progress bars
pymc / arviz (optional)	Bayesian modeling

See requirements.txt for pinned versions.