DataTalks.Club

Machine Learning in Marketing - Juan Orduz

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We talked about:

  • Juan’s background
  • Typical problems in marketing that are solved with ML
  • Attribution model
  • Media Mix Model – detecting uplift and channel saturation
  • Changes to privacy regulations and its effect on user tracking
  • User retention and churn prevention
  • A/B testing to detect uplift
  • Statistical approach vs machine learning (setting a benchmark)
  • Does retraining MMM models often improve efficiency?
  • Attribution model baselines
  • Choosing a decay rate for channels (Bayesian linear regression)
  • Learning resource suggestions
  • Bayesian approach vs Frequentist approach
  • Suggestions for creating a marketing department
  • Most challenging problems in marketing
  • The importance of knowing marketing domain knowledge for data scientists
  • Juan’s blog and other learning resources
  • Finding Juan online


    • Links: 

    • Juan's PyData talk on uplift modeling: https://youtube.com/watch?v=VWjsi-5yc3w
    • Juan's website: https://juanitorduz.github.io
    • Introduction to Algorithmic Marketing book: https://algorithmic-marketing.online
    • Preventing churn like a bandit: https://www.youtube.com/watch?v=n1uqeBNUlRM


      • MLOps Zoomcamp: https://github.com/DataTalksClub/mlops-zoomcamp

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