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UID:pretalx-pyconde-pydata-2026-EL7X8C@pretalx.com
DTSTART;TZID=CET:20260415T161500
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DESCRIPTION:In every marketing project\, teams strive to find more data\, a
  longer timeframe\, and more detailed splits\, just to fix noisy channel a
 ttribution.\n\nBut what if structure played a bigger role than size and vo
 lume? \nIn this talk\, we try to prove this. Using a simple toolkit like A
 rviz and PyMC\, we show you a simple hierarchical mix model\, and how\, by
  applying partial pooling\, we can stabilize important KPIS like ROAS esti
 mates across sparse channels- without the need for more data.\nWe will go 
 through the code\, transformation\, and the real-life practices that allow
  us to get as close to the truth\, to be able to have a meaningful impact 
 in the marketing world.\nThe approach will be centered around marketing mi
 x models\, different transformations\, and how useful it will be for the b
 usiness.
DTSTAMP:20260523T180013Z
LOCATION:Palladium [2nd Floor]
SUMMARY:Hierarchical Models in MMM: Can Structure beat data size? - Mohamed
  Amine Jebari
URL:https://pretalx.com/pyconde-pydata-2026/talk/EL7X8C/
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