FlixStream: Customer Churn Analysis Project Type: End-to-End Data Analysis (Python)

Tools Used: Python (Pandas, NumPy), Plotly (Interactive Viz), Google Colab

📌 Business Problem "FlixStream," a fictional streaming service, is experiencing a decline in user retention. My goal was to analyze a dataset of 1,000 users to identify the specific behaviors that predict when a customer is about to cancel their subscription.

🛠️ The Technical Approach Data Generation: Used Python to simulate a realistic dataset with programmed behavioral patterns (usage vs. support friction).

Exploratory Data Analysis (EDA): Leveraged Pandas to calculate the overall churn rate and segment users by activity levels.

Visualization: Created an interactive Plotly box plot to visualize the "Engagement Gap" between loyal and churned users.

📊 Key Insights The 12-Hour Threshold: Users who watch fewer than 12 hours of content per month are 80% more likely to cancel.

Support Friction: There is a direct correlation between high support ticket volume (3+) and churn, regardless of the subscription plan price.

💡 Recommendations Implement a "Re-engagement Trigger": Automated emails or discounts for users whose monthly watch time drops below the 15-hour mark.

Proactive Support: Reach out to users with 2+ open tickets to resolve friction before they hit the "Cancel" button.