marci 0.1.0

Marci: Marketing Science utilities for campaign analysis and simulation

marci

Marci: Marketing Science utilities for campaign analysis and simulation

A comprehensive Python package for marketing campaign modeling, including elasticity curves, seasonality patterns, conversion delays, and campaign simulation tools.

Installation

pip install marci

Quickstart

import pandas as pd
from marci import Campaign, Elasticity, Seasonality

# Create a campaign simulation
campaign = Campaign()
results = campaign.sim_outcomes()

# Analyze elasticity curves
elasticity = Elasticity(elasticity_coef=0.5, saturation_rate=0.8)
roas = elasticity.roas([0.5, 1.0, 1.5])

# Generate seasonal patterns
seasonality = Seasonality(seed=42)
values = seasonality.values(pd.date_range("2023-01-01", periods=365, freq="D"))

Development

• Create and activate a virtual environment
• Install dev tools

python -m venv .venv
# Windows PowerShell
. .venv/Scripts/Activate.ps1

pip install -U pip build pytest
pip install -e .
pytest -q

Release

1. Bump version in src/marci/_version.py
2. Commit and tag

git commit -am "chore: release v0.1.0"
git tag v0.1.0

3. Push tags to GitHub; the publish workflow will upload to PyPI when a release is created (or manually run the workflow).

Alternatively, publish locally:

python -m build
python -m twine upload dist/*

License

MIT