lightweight_mmm
trimmed_match
lightweight_mmm | trimmed_match | |
---|---|---|
5 | 1 | |
797 | 55 | |
4.6% | - | |
5.5 | 2.7 | |
about 2 months ago | 12 months ago | |
Python | Python | |
Apache License 2.0 | Apache License 2.0 |
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lightweight_mmm
- Lightweight (Bayesian) Marketing Mix Modeling (Google Unofficial)
- Show HN: Marketing software for solopreneurs who don't like marketing
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Mixed Marketing Modeling Approach for attribution?
Other packages to consider are LightweightMMM (from Google), which takes a Bayesian approach and is in Python (vs. R for Robyn). It's not as fully featured as Robyn (you don't get all the nice one-pagers and graphs output so easily), but it's still a great option, especially if you have some data scientists on the team who understand MMM.
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Is Hierarchical Bayesian Modelling used in industry?
LightweightMMM - python package
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Two More Years
Interestingly, Google did introduce an MMM framework: LightweightMMM 🦇 is a lightweight Bayesian Marketing Mix Modeling (MMM) library that allows users to easily train MMMs and obtain channel attribution information. https://github.com/google/lightweight_mmm
trimmed_match
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Is Hierarchical Bayesian Modelling used in industry?
Trimmed Match - paper, python package
What are some alternatives?
Robyn - Robyn is an experimental, AI/ML-powered and open sourced Marketing Mix Modeling (MMM) package from Meta Marketing Science. Our mission is to democratise modeling knowledge, inspire the industry through innovation, reduce human bias in the modeling process & build a strong open source marketing science community.
GeoexperimentsResearch - An open-source implementation of the geo experiment analysis methodology developed at Google. Disclaimer: This is not an official Google product.
mta - Multi-Touch Attribution
statsmodels - Statsmodels: statistical modeling and econometrics in Python
matched_markets - Matched Markets is a Python library for design and analysis of Geo experiments using Matched Markets and Time Based Regression.
emukit - A Python-based toolbox of various methods in decision making, uncertainty quantification and statistical emulation: multi-fidelity, experimental design, Bayesian optimisation, Bayesian quadrature, etc.