Netflix Churn Analysis Dashboard -- 2

I have a cleaned Netflix user dataset and want to uncover why subscribers leave the platform. The single outcome I care about is understanding — and ultimately reducing — customer churn.

To get there, I’d like you to explore three angles of watch-time behaviour (daily totals, week-to-week shifts, and broader seasonal patterns) and relate them to average session duration, content genres, subscription tier, and any other signals you believe are meaningful.

What I need from you
• A concise exploratory analysis (Python, R or SQL are all fine) that surfaces the strongest behavioural predictors of churn
• Clear explanatory visuals of those drivers
• An interactive Power BI dashboard that lets me slice by genre, subscription type, date range and churn status, with clean DAX measures behind every card
• A short, practical recommendation memo highlighting quick wins and longer-term retention ideas backed by the data

Acceptance criteria
• All figures in the dashboard must update automatically when new data is appended in the same schema
• Churn prediction model (if built) must achieve at least reasonable baseline performance — please include accuracy, precision and recall
• Deliverables must be packaged so a stakeholder without coding knowledge can refresh and navigate the report with a single click

If this sounds like the kind of data puzzle you enjoy, let’s talk timelines and next steps.