AI Inside
Recommendation models, personalization algorithms, behavioral prediction, and ranking optimization work together to surface the right content to the right viewer at the right time.
Churn prediction models identify at-risk subscribers before they leave. Reinforcement learning continuously improves every recommendation based on real viewer behavior. The system gets smarter with every interaction.
Inputs
- Published content and moments from Activate
- Enriched metadata and engagement scores from Enrich
- Historical viewer behavior data
- Real-time viewer interaction signals
Engage
Audience Intelligence Layer
Continuously learns from viewer behavior to personalize content experiences.
- Recommendation modeling
- Behavioral prediction
- Churn and retention scoring
- Content ranking optimization
- Real-time personalization
Output
Personalized Viewer Experiences
- Personalized storefronts
- Discovery feeds
- Video player recommendations
- Short-form content feeds
- Interactive viewing experiences
Capabilities and Features
Reccos
Storefronts
Optimization
Ranking
Optimization
Reference Applications
Personalization
Features
Experiences
Segmentation
Definition
Publishing
Export
How it orchestrates
Viewer behavior signals collected in Engage feed directly into Maximize, improving monetization decisions and ad targeting accuracy.

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