Tracking login and session activity
- Monitors how frequently a user logs into the platform across devices.
- Session duration indicates user engagement and interest in content.
- Time spent on browsing versus actual viewing is analyzed.
- Patterns like weekend spikes or binge sessions are identified.
- Inactivity over time helps flag users at risk of churn.
Analyzing content consumption patterns
- Tracks completion rates of shows, movies, or episodes.
- Records skipped content, rewatches, and fast-forward behavior.
- Measures popularity of specific genres, formats, and language preferences.
- Viewers who drop out early are marked for engagement interventions.
- Long-term watch history provides loyalty indicators.
Monitoring subscription and billing data
- Identifies auto-renewal users versus those who cancel or pause plans.
- Tracks trial-to-paid conversion and plan upgrade trends.
- Cancellation reasons are collected through feedback forms.
- Payment failures or inactive billing cycles indicate at-risk users.
- Discounts and offers tested against their effect on renewal rates.
User interaction with recommendations
- Measures clicks on personalized carousels or promoted banners.
- Identifies the success rate of AI-driven content suggestions.
- View-through rates of trailers influence retention scoring.
- Engagement with “Continue Watching” and “Watchlist” reflects interest.
- Poor interaction may trigger retargeting or UX redesign.
Feedback and support engagement
- Tracks frequency and nature of user complaints or support queries.
- Negative feedback on app experience may lower retention likelihood.
- Satisfaction ratings after customer support sessions are analyzed.
- In-app surveys and reviews help gauge user sentiment.
- Resolved issues often correlate with extended user retention.