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[Online] PyMC-Marketing vs. Google Meridian: A Scientific Benchmark for MMM

2025-10-01 โ€“ 2025-10-01 Meetup Visit website โ†—

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๐ŸŽ™๏ธ Speaker: Dr. Luca Fiaschi, Teemu Sรคilynoja, Jake Piekarski \| โฐ Time: 14:00 UTC / 7:00 AM PT / 10:00 AM ET / 4:00 PM Berlin

Many teams building Marketing Mix Models (MMMs) today must decide between open source tools, and two of the top contenders are PyMC-Marketing and Googleโ€™s Meridian. In this webinar, the PyMC Labs team will share the results of a rigorous, apples-to-apples benchmark between the two: default priors, model structures, and synthetic datasets that simulate everything from startups to global enterprises.

You will learn:

  • How the two libraries compare in speed, accuracy, and scalability
  • Where PyMC-Marketing offers advantages, and in which situations Meridian might still make sense
  • Concrete performance trade-offs backed by data across multiple company sizes and data complexities
  • Best practices and recommendations for selecting the right tool based on your teamโ€™s priorities

Join us if you want a clear, evidence-based guide to choosing the MMM library that will best serve your modelling goals, without surprises down the road.

๐Ÿ“œ Outline of Talk / Agenda:

  • 5 min: Intro to PyMC Labs and speakers
  • 45 min: Presentation, panel discussion
  • 10 min: Q&A

๐Ÿ’ผ About the speaker:

  1. Dr. Luca Fiaschi (PyMC Labs Partner, Gen AI Vertical) Luca helps organizations unlock the value of data and AI. With 15+ years of experience, heโ€™s led and scaled teams at Mistplay, HelloFresh, Alibaba, and Stitch Fix, driving breakthroughs in personalization, marketing optimization, and causal modeling. He holds a PhD in Computer Science from Heidelberg University.

๐Ÿ”— Connect with Luca: ๐Ÿ‘‰ Linkedin: https://www.linkedin.com/in/lfiaschi/ ๐Ÿ‘‰ Github: https://github.com/lfiaschi

  1. Teemu Sรคilynoja (Researcher at PyMC Labs)

Teemu specializes in Bayesian modeling, marketing analytics, and trading analytics. Heโ€™s completing a Doctor of Science degree on model calibration and holds a Masterโ€™s in mathematics. An active open-source contributor, he develops tools in Python and R for probabilistic programming and model evaluation.

๐Ÿ”— Connect with Teemu: ๐Ÿ‘‰ GitHub: https://github.com/TeemuSailynoja ๐Ÿ‘‰ LinkedIn: http://www.linkedin.com/in/teemu-sailynoja

  1. Jake Piekarski (Data Science at PyMC Labs)

Jake specializes in advanced statistical modeling, including Marketing Mix Modeling (MMM), incrementality testing, and Bayesian statistics. He focuses on turning complex data into actionable insights that help optimize marketing strategies and support data-driven decisions.

๐Ÿ”— Connect with Jake: ๐Ÿ‘‰ GitHub: https://github.com/JakePiekarski314 ๐Ÿ‘‰ LinkedIn: www.linkedin.com/in/jake-piekarski-715b6a267

๐Ÿ’ผ About the Host:

  1. Evan Wimpey (Director of Analytics at PyMC Labs) Evan helps clients design Bayesian solutions tailored to their goals, ensuring they understand both the how and why of inference. With masterโ€™s degrees in Economics and Analytics, he focuses on delivering clear value throughout projects and brings a unique twist with his background in data comedy.

๐Ÿ”— Connect with Evan: ๐Ÿ‘‰ Linkedin: https://www.linkedin.com/in/evan-wimpey/ ๐Ÿ‘‰ GitHub: https://github.com/ewimpey

๐Ÿ“– Code of Conduct: Please note that participants are expected to abide by PyMC's Code of Conduct.

๐Ÿ”— Connecting with PyMC Labs: ๐ŸŒ Website: https://www.pymc-labs.com/ ๐Ÿ‘ฅ LinkedIn: https://www.linkedin.com/company/pymc-labs/ ๐Ÿฆ Twitter: https://twitter.com/pymc_labs ๐ŸŽฅ YouTube: https://www.youtube.com/c/PyMCLabs ๐Ÿค Meetup: https://www.meetup.com/pymc-labs-online-meetup/

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Benchmark: PyMC-Marketing vs Meridian for Marketing Mix Modeling (MMM)

2025-10-01
webinar
Jake Piekarski (PyMC Labs) , Dr. Luca Fiaschi (PyMC Labs)

A rigorous apples-to-apples benchmark between PyMC-Marketing and Googleโ€™s Meridian for Marketing Mix Modeling, covering default priors, model structures, and synthetic datasets that simulate everything from startups to global enterprises. Includes speed, accuracy, scalability comparisons and practical recommendations for choosing the MMM library.