This section establishes a common baseline for current channel health. It combines total views (scale), average views per video (efficiency), revenue (monetization), and engagement distribution (retention quality and consistency) so decisions are based on both volume and quality, not just one metric.
What this chart shows: Total views and average views per video for each content type in one side-by-side view.
How to read it: Use the Total panel to see overall volume and the Average panel to see per-upload performance, then compare formats across both.
What this chart shows: How views and revenue move over time across the reporting period.
How to read it: Look for rises, drops, and turning points to see when performance accelerated or slowed.
What this chart shows: Total estimated revenue by content type for videos published in each month.
How to read it: This is a publish-month cohort view (lifetime revenue-to-date per cohort), not cash earned only inside that calendar month.
What this chart shows: Per-video Average View % distribution by content type, with top-quartile videos highlighted.
How to read it: Boxes show baseline performance and consistency by format; highlighted points show standout videos within each format.
How to use this in two contexts:
1. Average View % distribution context (format reliability): Use the median line and box spread to decide which content type is most consistently retaining viewers.
2. Successful video context (replicable winners): Use highlighted points to identify high-performing outlier videos, then review titles/topics to replicate winning patterns.
This section shows momentum: whether performance is improving, flattening, or declining over time. By tracking views and revenue together, it becomes easier to spot turning points early and separate audience demand shifts from monetization changes that a single-period snapshot might hide.
What this chart shows: How estimated revenue changes over time for each content type.
How to read it: Compare trajectories to identify which formats are contributing more monetization over time.
What this chart shows: How total views change over time for each content type using the same trend format as the revenue chart above.
How to read it: Compare month-to-month direction and volatility by format, then read alongside revenue to see whether monetization changes are backed by view demand.
This section explains who is watching and how that audience is changing month to month. Age and gender share trends provide a stable baseline for creative positioning, partnership fit, and identifying where the audience is expanding or contracting.
What this chart shows: Audience share trends over time by age and gender segments.
How to read it: Track which segments are growing, shrinking, or staying stable month to month.
This section turns the performance signal into operating choices for upcoming uploads. Timing, collaboration, topic, and tag analyses focus on controllable levers so teams can prioritize high-impact experiments and make clearer trade-offs in the next publishing cycle.
What this chart shows: Per-video distributions of views and revenue for weekend vs weekday uploads.
How to read it: Each dot is a video; compare medians, mean points, and spread to see whether one day type is typically stronger or more consistent.
What this chart shows: Share of total views and revenue attributed to each weekday.
How to read it: Larger bars mean that weekday contributes a bigger portion of overall performance.
What this chart shows: Average views per video and average revenue per video for collaborative vs non-collaborative uploads.
How to read it: Compare bar heights to see which approach performs better per upload, not just by total volume.
What this chart shows: Performance comparison by topic using both views and revenue.
How to read it: Higher values indicate topics that contribute more overall performance.
What this chart shows: Average views per video for each top tag (how many views a tagged video gets on average).
How to read it: Taller bars indicate tags that tend to perform better per tagged upload, independent of total posting volume.