Open Engineering Standard

BehaveIQ Analytical Methodology

Full transparency into our mathematical formulas, public data collection pipelines, algorithm thresholds, and known analytical limitations.

Independent Engineering Disclosure & Platform Notice

BehaveIQ's methodologies, mathematical formulations, and diagnostic scoring models represent independent empirical engineering developed by BehaveIQ Creator Intelligence Technologies. They do NOT represent Google LLC's or YouTube's proprietary internal ranking algorithms, confidential neural network weights, or official platform endorsements.

Industry Knowledge & Official Protocols

1. How Public YouTube Data Is Collected

BehaveIQ operates strictly as an analytical layer over publicly accessible YouTube data. All metrics are retrieved via official Google YouTube Data API v3 endpoints:

Channel Records: Retrieved via channels.list using public channel IDs or custom handles.
Video Metadata: Retrieved via videos.list (title, publication timestamp, view counts, like counts, and public thumbnail URLs).
Public Comments: Retrieved ephemerally via commentThreads.list solely for linguistic sentiment and question clustering.
Zero Private Scraping: We do not use headless browser bots, unauthorized scraping, or private credential extraction.
Format Classification & Shorts Separation: The YouTube Data API v3 does not expose a universal boolean flag for Shorts. Shorts are excluded using available public metadata and format/duration rules (qualifying square/vertical videos up to 3 minutes, or explicit #shorts indicators). Videos that cannot be confidently classified as long-form are excluded from the benchmark or flagged for review.
Data Minimization & Privacy: Viewer usernames, profile pictures, and commenter IDs are scrubbed in memory prior to analysis and are never stored in our persistent database.

BehaveIQ Product Methodology

2. How View Velocity Is Calculated

Raw cumulative view counts are a misleading metric for creator growth because they bias heavily toward older videos. A video published four years ago with 100,000 views is often dormant, while a video published 48 hours ago with 15,000 views possesses immense algorithmic momentum.

Normalized Velocity Formula

Velocity (Views/Day) = Views / max(Video_Age_Days, 1.0)

Where Video_Age_Days = (Current_UTC_Timestamp - Published_UTC_Timestamp) / 86400. For newly published videos under 24 hours old, the denominator is floored at 1.0 day to prevent division anomalies, or computed hourly as Views / max(Video_Age_Hours, 1.0).

BehaveIQ Product Methodology & Thresholds

3. How Baselines & Outliers Are Identified

An "outlier" is not an arbitrary label. In BehaveIQ, it is defined relative to the specific channel's median performance over an observation window (default 90 days or recent 50 uploads):

Outlier Multiplier Formula

Outlier_Multiplier = Video_Views_Per_Day / max(Median_Channel_Views_Per_Day, 1.0)

We use the median rather than the arithmetic mean to establish the baseline because the median is resistant to skew caused by rare viral spikes.

Classification Tier	Multiplier Range	Algorithmic Meaning
Underperforming	< 0.50×	Lagging behind typical channel audience pickup.
Normal Catalog Baseline	0.50× to 1.49×	Typical subscriber and core audience engagement.
Strong Outlier	1.50× to 2.99×	Statistically significant reach beyond core subscribers into browse feeds.
Breakout Super Outlier	3.00× to 9.99×	Major recommendation breakout into broader interest cohorts.
Viral Platform Outlier	≥ 10.0× (up to 50×+)	Cross-niche platform-level distribution breakout.

Audience Intelligence & NLP Methodology

4. How Viewer Comments Are Mined & Classified

Rather than generic word clouds, BehaveIQ processes top-level public comments through a structured NLP pipeline:

Intent Parsing

Lexical and syntactic classifiers identify interrogative patterns (e.g. "how do you", "what about", "can this") to isolate high-intent audience questions.

Friction Clustering

Identifies negative friction terms, confusion expressions, and common objections to surface what topics require deeper explanation in follow-up videos.

Research Hypotheses & Vision Modeling

5. How Thumbnail Packaging Is Scored

Thumbnails are downsampled to a simulated 320px mobile viewport rendering scale (approximately 150×84 physical device pixels) to simulate smartphone feeds:

Luminance Contrast: Measures subject-to-background contrast ratio to guarantee visual separation.
Text Legibility: Evaluates word count (optimal: 2–4 words) and font weight at small viewports.
Focal Anchor: Verifies the presence of a clear focal subject rather than dispersed visual clutter.
Title-Thumbnail Harmony: Checks whether the title and thumbnail complement each other without verbatim repetition.

Known Limitations & Non-Claims

6. Known Limitations & What BehaveIQ Does NOT Claim

To maintain complete scientific and editorial integrity, we explicitly disclose the following limitations:

No Private YouTube Studio Search Volumes: The public YouTube Data API does not expose exact search volume queries or private creator dashboard impressions. We do not invent fake search volume numbers.
No Guaranteed Algorithm Promotion: While high outlier scores and packaging ratings optimize probability, final recommendation distribution is determined by YouTube's internal neural ranking models based on real-time audience engagement.
No Official Partnership: BehaveIQ is an independent SaaS creator intelligence platform compliant with YouTube API Developer Policies. We are not an official partner, subsidiary, or certified agent of YouTube or Google LLC.
No Private Viewer Tracking: We do not collect, store, or sell viewer personally identifiable information (PII).

Try Our Public Velocity Calculator

Test the view velocity and outlier multiplier formulas on any public video using our unauthenticated tool.

Launch Velocity Calculator