Built for teams who can't afford to find out a video failed after the fact.
Two modules. One decision: publish with confidence or fix first.
Two tools. Both answer the same question: is this ready to publish?
Maps every 8-second segment of your video for engagement drop-off risk before the video goes live.
- 8-second segment resolution — not averages
- Format-specific model (YouTube, Shorts, Reels, TikTok, streaming)
- Labeled timestamps + fix recommendation per drop-off segment
- Subscriber vs. non-subscriber retention split
- Predicted completion rate improvement post-fix
Ranks up to 3 thumbnail variants by predicted click-through against your specific audience segment — before upload.
- Audience-segment CTR prediction, not platform-wide averages
- Text-title contrast analysis
- Face placement and emotional signal scoring
- Competitor category benchmarking
- Color saturation relative to category set
What Segment Scorer analyzes
The model scores each 8-second window across five signal dimensions trained on format-specific audience behavior patterns.
- Pacing — edit rhythm and scene-change density relative to format baseline
- Dialogue density — talk density vs. engagement falloff for your format
- Scene transitions — abrupt vs. smooth cuts and how they track with retention
- B-roll proportion — establishing shot length at typical viewer dropout thresholds
- Audio energy variance — music / silence / speech energy relative to engagement
Ranked before the video goes live
Upload up to 3 thumbnail variants. Fanlytiq ranks them by predicted click-through against your audience profile — not platform-wide averages.
Model inputs: audience segment behavior (subscriber vs. algorithmic discovery), category competitive set, text-to-visual ratio, emotional register signal, and color saturation relative to your content category's competitive thumbnail set.
Each format has different drop-off physics
Long-form YouTube, short-form social, and streaming series all have different audience behaviors. Fanlytiq uses format-specific models.
Viewers front-load decisions in first 30 seconds. Pacing and dialogue density are the primary drop-off predictors. B-roll establishing sequences are the most common recovery point.
Hook density in first 3 seconds determines whether viewers watch to end. Loop-back rate is the primary engagement signal — model weights audio and visual novelty differently than long-form.
Caption-text integration with visual rhythm is a stronger engagement predictor than audio alone. Drop-off patterns differ significantly between discovery-feed and follower-feed distribution.
Trend-audio matching amplifies retention differently than original audio. Novelty signal in first 2 seconds is weighted higher than any other format. Comment engagement correlates with segment rewatch rate.
Cross-episode retention is scored differently from single-video drop-off. Cold-open length and mid-episode pacing are the primary predictors of both completion and next-episode continue rates.
Teams with custom post-production pipelines can integrate Fanlytiq scoring directly via API. Available on Studio plan.
Four ways to get a video into Fanlytiq
Browser upload, YouTube OAuth, Vimeo connect, or API for teams with custom post-production pipelines. No new infrastructure for most teams.
Start Free Trial — analyze 3 videos at no cost.
No credit card required. Contact us if your team publishes 20+ videos per month.