VidPropel turns useful aggregate creator patterns into benchmarks without publishing individual customer histories or inventing impressive-looking numbers before the evidence exists.
A periodic privacy-safe view of creator momentum signals across qualifying anonymous cohorts.
Minimum cohort: 50+ qualifying entities · Metric version: youtube-core-v1
Storage: Aggregated cohort rollups only; no public user-level history.
No public number is shown until the minimum cohort, metric-level sample, freshness and aggregation gates are all satisfied.
Aggregate packaging patterns from qualifying thumbnail checks, reported only when the cohort is large enough to avoid exposing individual creators.
Minimum cohort: 50+ qualifying entities · Metric version: thumbnail-clarity-v1
Storage: Aggregate score buckets and counts only.
No public number is shown until the minimum cohort, metric-level sample, freshness and aggregation gates are all satisfied.
Aggregate readiness patterns from pre-spend Video Doctor checks, without claiming that a score guarantees distribution or views.
Minimum cohort: 50+ qualifying entities · Metric version: launch-readiness-v1
Storage: Aggregate readiness buckets and counts only.
No public number is shown until the minimum cohort, metric-level sample, freshness and aggregation gates are all satisfied.
A cohort-level view for smaller channels once enough comparable observations exist.
Minimum cohort: 75+ qualifying entities · Metric version: small-channel-v1
Storage: Cohort aggregates only; no public channel identities.
No public number is shown until the minimum cohort, metric-level sample, freshness and aggregation gates are all satisfied.
✓ No invented customer counts, CTR lifts, view gains or conversion claims.
✓ Cohorts and individual metrics must clear minimum sample and freshness thresholds before public reporting.
✓ One freshest qualifying observation per channel prevents high-volume accounts from dominating the cohort.
✓ Benchmarks describe distributions, not guaranteed outcomes.
✓ Long-term public learning favours compact aggregates over raw event histories.
✓ Methodology, metric version and limitations stay visible beside the result.
Public benchmarks appear only when they qualify. The free tools are available immediately.