BehaveIQ Research Lab
Transparent, reproducible data studies analyzing YouTube recommendation distribution, view velocity dynamics, and packaging performance across thousands of public channels.
All research stems from verified public YouTube Data API v3 endpoints. We never fabricate numbers or synthesize fake channel metrics.
We study public cohort distributions. Viewer usernames, emails, and commenter identifiers are never collected or stored.
Formulas, sampling cohorts, and outlier thresholds are openly published so analysts can verify and reproduce findings.
Active & Ongoing Data Studies
YouTube Outlier Benchmark 2026
An empirical multi-cohort investigation examining how outlier multipliers (2x to 50x) distribute across 5 channel size tiers and 10 content verticals over a 90-day observation window. Establishes the first open mathematical baseline for organic recommendation breakout velocity. Current empirical dataset: 0 videos (results pending data collection).
The 320px Mobile Thumbnail Readability Audit
Analyzing word counts, luminance contrast ratios, and visual clutter across 10,000 public thumbnails to correlate mobile readability with 72-hour click-through velocity.
Title Character Length vs. Browse Recommendation Pacing
Investigating whether title truncation (under 50 characters vs 70+ characters) creates a measurable difference in Browse impression testing across informational niches.
Citing BehaveIQ Research
Journalists, newsletter authors, and researchers are encouraged to cite BehaveIQ data assets and methodologies with attribution to:
"BehaveIQ Creator Intelligence Research Lab (behaveiq.in/research)"