Mixed Marketing Modeling Approach for attribution?

This page summarizes the projects mentioned and recommended in the original post on /r/PPC

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  • 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. (by facebookexperimental)

  • With all this talk about Google and other platforms deprecating 3P tracking in favor of more aggregate "tracking", my team is considering a marketing mix modeling tool. One that comes to mind is this tool - Robyn

  • lightweight_mmm

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

  • 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.

  • InfluxDB

    Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

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NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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