Reference
AI, measured data, and confidence
Know which Retensis outputs are observed, predicted, grounded, or deterministic.
LiveAll plans
Four data types
| Label | Meaning | Examples |
|---|---|---|
| Measured | Read from a supported platform or stored observation | Views, comments, subscribers, experiment metrics |
| Predicted | Estimated by an AI model | Retention curve, virality, CTR tier, content brand-safety review |
| Grounded | Generated with external search or YouTube research context | Trends, gaps, plan sources |
| Deterministic | Recomputed by application logic | Opportunity weighting, comparison deltas, experiment winner, estimated revenue ranges |
Responsible use
- Treat predictions as hypotheses.
- Prefer repeated patterns over one isolated score.
- Use measured platform data to validate important decisions.
- Review grounding sources when recency matters.
- Keep brand, legal, medical, financial, and safety judgment with a qualified human.
Related documentation
Understanding analysis results
Read scores, retention, drop-offs, timelines, speech coaching, and the action plan correctly.
Prediction Accuracy
See how close our retention predictions land to your real audience data, and let Retensis calibrate future predictions to your channel.
Monetization
Estimate what your content type tends to earn, how ready you are for brand deals, and what to improve, grounded in your real channel numbers.