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

Join DataTalks.Club: https://datatalks.club/slack.html

Our events: https://datatalks.club/events.html