Machine Learning in Marketing - Juan Orduz DataTalks.Club

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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