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GitHub - deejayrusso/Marketing-Mix-Model
GitHub - deejayrusso/Marketing-Mix-Model

How To Create A Marketing Mix Model With LightweightMMM | Forecastegy
How To Create A Marketing Mix Model With LightweightMMM | Forecastegy

Three free Marketing Data Science Tools that let your eyes glow | by Florian Grüning | kuwala-io | Medium
Three free Marketing Data Science Tools that let your eyes glow | by Florian Grüning | kuwala-io | Medium

How Google LightweightMMM Works - Recast
How Google LightweightMMM Works - Recast

GitHub - sibylhe/mmm_stan: Python/STAN Implementation of Multiplicative Marketing Mix Model, with deep dive into Adstock (carry-over effect), ROAS, and mROAS
GitHub - sibylhe/mmm_stan: Python/STAN Implementation of Multiplicative Marketing Mix Model, with deep dive into Adstock (carry-over effect), ROAS, and mROAS

How-to: Facebook Robyn marketing mix modeling | R-bloggers
How-to: Facebook Robyn marketing mix modeling | R-bloggers

Using R to build a simple marketing mix model (MMM) and make predictions | Towards Data Science
Using R to build a simple marketing mix model (MMM) and make predictions | Towards Data Science

GitHub - sibylhe/mmm_stan: Python/STAN Implementation of Multiplicative Marketing Mix Model, with deep dive into Adstock (carry-over effect), ROAS, and mROAS
GitHub - sibylhe/mmm_stan: Python/STAN Implementation of Multiplicative Marketing Mix Model, with deep dive into Adstock (carry-over effect), ROAS, and mROAS

GitHub - sibylhe/mmm_stan: Python/STAN Implementation of Multiplicative Marketing Mix Model, with deep dive into Adstock (carry-over effect), ROAS, and mROAS
GitHub - sibylhe/mmm_stan: Python/STAN Implementation of Multiplicative Marketing Mix Model, with deep dive into Adstock (carry-over effect), ROAS, and mROAS

GitHub - sibylhe/mmm_stan: Python/STAN Implementation of Multiplicative Marketing Mix Model, with deep dive into Adstock (carry-over effect), ROAS, and mROAS
GitHub - sibylhe/mmm_stan: Python/STAN Implementation of Multiplicative Marketing Mix Model, with deep dive into Adstock (carry-over effect), ROAS, and mROAS

marketing-mix-modeling · GitHub Topics · GitHub
marketing-mix-modeling · GitHub Topics · GitHub

GitHub - sibylhe/mmm_stan: Python/STAN Implementation of Multiplicative Marketing Mix Model, with deep dive into Adstock (carry-over effect), ROAS, and mROAS
GitHub - sibylhe/mmm_stan: Python/STAN Implementation of Multiplicative Marketing Mix Model, with deep dive into Adstock (carry-over effect), ROAS, and mROAS

Using R to build a simple marketing mix model (MMM) and make predictions | Towards Data Science
Using R to build a simple marketing mix model (MMM) and make predictions | Towards Data Science

GitHub - sibylhe/mmm_stan: Python/STAN Implementation of Multiplicative Marketing Mix Model, with deep dive into Adstock (carry-over effect), ROAS, and mROAS
GitHub - sibylhe/mmm_stan: Python/STAN Implementation of Multiplicative Marketing Mix Model, with deep dive into Adstock (carry-over effect), ROAS, and mROAS

GitHub - Garve/mamimo: A package to compute a marketing mix model.
GitHub - Garve/mamimo: A package to compute a marketing mix model.

GitHub - sibylhe/mmm_stan: Python/STAN Implementation of Multiplicative Marketing Mix Model, with deep dive into Adstock (carry-over effect), ROAS, and mROAS
GitHub - sibylhe/mmm_stan: Python/STAN Implementation of Multiplicative Marketing Mix Model, with deep dive into Adstock (carry-over effect), ROAS, and mROAS

GitHub - sibylhe/mmm_stan: Python/STAN Implementation of Multiplicative Marketing Mix Model, with deep dive into Adstock (carry-over effect), ROAS, and mROAS
GitHub - sibylhe/mmm_stan: Python/STAN Implementation of Multiplicative Marketing Mix Model, with deep dive into Adstock (carry-over effect), ROAS, and mROAS

GitHub - sibylhe/mmm_stan: Python/STAN Implementation of Multiplicative Marketing Mix Model, with deep dive into Adstock (carry-over effect), ROAS, and mROAS
GitHub - sibylhe/mmm_stan: Python/STAN Implementation of Multiplicative Marketing Mix Model, with deep dive into Adstock (carry-over effect), ROAS, and mROAS

GitHub - sibylhe/mmm_stan: Python/STAN Implementation of Multiplicative Marketing Mix Model, with deep dive into Adstock (carry-over effect), ROAS, and mROAS
GitHub - sibylhe/mmm_stan: Python/STAN Implementation of Multiplicative Marketing Mix Model, with deep dive into Adstock (carry-over effect), ROAS, and mROAS

GitHub - Better-Boy/Market-Mix-Modelling: This repository contains the code and explanation for market mix modelling technique in economics
GitHub - Better-Boy/Market-Mix-Modelling: This repository contains the code and explanation for market mix modelling technique in economics

GitHub - data-crat/Machine-Learning: This gold mine contains beginner friendly (* maybe), traditional and advanced/sophisticated modelling techniques to solve problems in diverse Industries
GitHub - data-crat/Machine-Learning: This gold mine contains beginner friendly (* maybe), traditional and advanced/sophisticated modelling techniques to solve problems in diverse Industries

Media Mix Modeling: How to Measure the Effectiveness of Advertising with Python & LightweightMMM | by Hajime Takeda | Towards Data Science
Media Mix Modeling: How to Measure the Effectiveness of Advertising with Python & LightweightMMM | by Hajime Takeda | Towards Data Science

GitHub - google/lightweight_mmm: LightweightMMM 🦇 is a lightweight Bayesian Marketing Mix Modeling (MMM) library that allows users to easily train MMMs and obtain channel attribution information.
GitHub - google/lightweight_mmm: LightweightMMM 🦇 is a lightweight Bayesian Marketing Mix Modeling (MMM) library that allows users to easily train MMMs and obtain channel attribution information.

Productionizing A Marketing Mix Model | by Gabriel John Dusing | Medium
Productionizing A Marketing Mix Model | by Gabriel John Dusing | Medium

GitHub - sibylhe/mmm_stan: Python/STAN Implementation of Multiplicative Marketing Mix Model, with deep dive into Adstock (carry-over effect), ROAS, and mROAS
GitHub - sibylhe/mmm_stan: Python/STAN Implementation of Multiplicative Marketing Mix Model, with deep dive into Adstock (carry-over effect), ROAS, and mROAS