Marketing Mix Modeling (MMM) — Ridge + Adstock + Saturation

MMM = Marketing Mix Modeling.
We simulate weekly KPIs (e.g., subscriptions) and channel spends, transform spends with adstock (carry‑over) and saturation (diminishing returns), then fit a ridge regression to attribute impact and explore budget reallocation.

Quickstart

# (Recommended) Python 3.11+
python -m venv .venv && source .venv/bin/activate  # Windows: .venv\Scripts\activate
pip install -r requirements.txt

# Run a small demo (simulate + analyze)
python scripts/run_demo.py

# Run tests
pytest -q

Run tests locally

Windows (PowerShell, current session)

# Expose repo root to Python for this shell session
$env:PYTHONPATH = (Get-Location).Path
pytest -q --maxfail=1 -ra

macOS / Linux (bash/zsh, current session)

# Expose repo root to Python for this shell session
export PYTHONPATH="$PWD"
pytest -q --maxfail=1 -ra

(Optional) Windows CMD

:: Expose repo root to Python for this CMD session
set PYTHONPATH=%CD%

:: Run tests (quiet; stop early; show failure summary)
pytest -q --maxfail=1 -ra

Structure

marketing-mix-model/
├─ src/
│  ├─ simulate.py     # synthetic weekly data
│  ├─ transforms.py   # adstock, saturation
│  ├─ features.py     # build design matrix
│  ├─ model.py        # ridge fit + attribution
│  └─ optimize.py     # simple greedy budget optimizer
├─ scripts/
│  └─ run_end_to_end.py
├─ tests/
│  ├─ test_transforms.py
│  └─ test_model.py
├─ data/
│  └─ README.md
├─ requirements.txt
├─ .github/workflows/ci.yml
└─ README.md

Concepts (expanded)

• Adstock: advertising effects persist over time; modeled via geometric decay.
• Saturation: diminishing returns; we use a Hill function (S‑curve).
• Ridge regression: L2 regularization for stable attribution when channels correlate.

Outputs

• Channel coefficients & normalized ROAS proxy.
• A toy budget optimization suggestion under a fixed total spend.
• Plots saved to outputs/ (optional).

This is intentionally light‑weight (no PyMC/Stan) to keep it interview‑ready.

Note: run pytest -q locally before pushing.

Get help / share results

• ❓ Ask a question → New Q&A
• 🎉 Share a result → New Show & Tell
• 🧰 Quick fixes / FAQ → FAQ thread