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Karpiu is a package designed for marketing mix modeling by calling Orbit from the backend. Karpiu is still in its beta version. Please use it at your own risk.

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# Karpiu

**Karpiu** is a package designed for Marketing Mix Modeling (MMM) by calling [Orbit](https://github.com/uber/orbit) from the backend. Karpiu is still in beta, please use it at your own risk.

# Tutorials & Documentation

For usage, please refer to this [documentation](https://edwinng.com/karpiu/).

# Installation

To access the development version, please follow the instructions below or use `make install-dev` 
after cloning the repository. This package only supports `orbit>=1.1.4` which requires `cmdstanpy`.

```bash
$ git clone https://github.com/edwinnglabs/karpiu.git
$ cd karpiu
$ pip install cmdstanpy
$ pip install -r requirements.txt
# required by mkdocs
$ pip install poetry
$ pip install -r requirements-test.txt
$ pip install -r requirements-docs.txt
$ pip install -e .
```

# Related Work

## Codebase

1. [Robyn](https://github.com/facebookexperimental/Robyn) - Robyn is an automated Marketing Mix Modeling (MMM) package. It aims to reduce human bias by means of ridge regression and evolutionary algorithms. It enables actionable decision making, provides a budget allocator, diminishing returns curves and allows ground-truth calibration to account for causation.

2. [Lightweight (Bayesian) Marketing Mix Modeling](https://github.com/google/lightweight_mmm) - LMMM is a python library that helps organisations understand and optimise marketing spend across media channels.

3. [Orbit](https://github.com/uber/orbit) - A Python package for Bayesian forecasting with an object-oriented design and probabilistic models under the hood.

## Research
- [Ng, E., Wang, Z. & Dai, A. (2021). Bayesian Time Varying Coefficient Model with Applications to
Marketing Mix Modeling](http://papers.adkdd.org/2021/papers/adkdd21-ng-bayesian.pdf)
- [Jin, Y., Wang, Y., Sun, Y., Chan, D., & Koehler, J. (2017). Bayesian Methods for Media Mix Modeling with Carryover and Shape Effects. Google Inc.](https://research.google/pubs/pub46001/)
- [Chan, D., & Perry, M. (2017). Challenges and Opportunities in Media Mix Modeling.](https://research.google/pubs/pub45998/)
- [Sun, Y., Wang, Y., Jin, Y., Chan, D., & Koehler, J. (2017). Geo-level Bayesian Hierarchical Media Mix Modeling.](https://research.google/pubs/pub46000/)

## Community Articles

- [Are Marketing Mix Models Useful? I Spent My Own Money To Find Out](https://forecastegy.com/posts/marketing-mix-models/) by Mario Filho.
- [How Google LightweightMMM Works](https://getrecast.com/google-lightweightmmm/) by Mike Taylor.
- [
Bayesian Media Mix Modeling for Marketing Optimization](https://www.pymc-labs.io/blog-posts/bayesian-media-mix-modeling-for-marketing-optimization/)
by Benjamin Vincent.

# Fun Fact

**Karpiu** is a term invented by the author's daugther when she had just reached her terrible two's. In the early development of this project, "Karpiu" became the dominating term in the author's home office. The author later dedicated this term to the package.

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