The subscriber myth
Competitors · VidCC Research · 7 Oct, 2026
It is easy to assume bigger channels make videos that hold viewers better. In our data they didn't. Retention was about the same for channels with a few hundred subscribers and channels with hundreds of thousands.
Introduction
When creators benchmark themselves, they often look up to the largest channels in their space. We wanted to know whether those channels actually keep viewers better, or whether retention is something a channel of any size can match.
Key takeaways
- Subscribers and average retention were unrelated: ρ = 0.04 (95% CI −0.15 to 0.23).
- Subscribers and retention at 0:30 showed at most a weak link: ρ = 0.14 (95% CI −0.05 to 0.31).
- Channels under 10,000 subscribers averaged 56% retention; channels with 10,000 to 100,000 averaged 59%; channels over 100,000 averaged 56%.
- Retention measures craft, not size. A small channel can hold viewers as well as a large one.
The data behind this study
This study uses 158 YouTube Studio audience retention graphs that creators chose to post publicly, mostly on X, between 2015 and 2026. We matched each graph to its YouTube video and channel, then traced the curve into 100 points, one for every 1% of the video's runtime, exactly as Studio plots it.
The set covers 146 channels: 123 long-form videos (longer than 60 seconds) and 35 Shorts. This study uses the 122 long-form videos with a known subscriber count. We report results only in aggregate. No individual video or channel is identified.
Definitions
- Average retention
- The mean of the retention curve across the whole video. It approximates YouTube's average percentage viewed.
- Retention at 0:30
- Absolute retention 30 seconds into the video.
- Subscribers
- The channel's subscriber count when the dataset was assembled in 2026.
No retention advantage for bigger channels
The three size groups were within a few points of each other on every measure. The largest group is small, eight channels, so its figures are the least certain, but the overall correlation across all 122 videos is close to zero.
| Channel size | Videos | Retention at 0:30 | Average retention | Retention at end |
|---|---|---|---|---|
| Under 10,000 | 81 | 68% | 56% | 35% |
| 10,000–100,000 | 33 | 70% | 59% | 34% |
| Over 100,000 | 8 | 70% | 56% | 36% |
Average retention vs subscribers: ρ = 0.04 (95% CI −0.15 to 0.23).
What this means for your channel
Size helps a video get clicked. A well-known name makes people more willing to try a video, so big channels can get away with things smaller ones can't. But once someone is watching, they judge what is on screen, not who made it. That is where the advantage disappears.
Subscriber count is a poor guide to which channels to learn from. A channel's real scale depends on its niche, its experience, its team and its budget, and none of those guarantee a better-held audience.
Benchmark against your niche
Compare your videos with channels making the same kind of video for the same kind of viewer, whatever their size.
Compare like with like
Match videos by length and format before comparing their curves. See our study on length.
Study openings, not subscriber counts
When a competitor's video holds viewers, look at how its first 30 seconds confirm the promise and set a direction.
Methodology
Sample. 163 publicly posted Studio retention graphs, each matched to its YouTube video. We excluded four whose video had been re-cut after the graph was posted (the graph's length no longer matched the video) and one whose trace could not be verified, leaving 158 videos from 146 channels. 109 come from Japanese-language channels, 45 from English-language channels and 4 from others. Channels range from 6 to 1.75 million subscribers, with a median of 4,090.
Tracing. Each graph was traced into 100 points of absolute retention, as plotted, and every trace was checked visually against an overlay of the original screenshot. 149 of the 158 traces were rated high confidence and 9 medium.
Measures. Retention at 0:30 is read from the curve at 30 seconds for long-form videos. Average retention is the mean of the curve and approximates average percentage viewed. A rewatch rise is an increase of at least 3 points over the lowest value in the preceding 4% of the runtime, outside the first and last 5%. Views and subscribers were recorded when the dataset was assembled in 2026, not when each graph was posted.
Statistics. We report medians and Spearman rank correlations (ρ). 95% confidence intervals come from a bootstrap with 2,000 replicates, resampling videos with replacement and recomputing each statistic.
Caveats. This is an observational study, not an experiment, so it shows association, not cause. Creators chose which graphs to post, the sample leans towards smaller and Japanese-language channels, and tracing from screenshots can be off by about a point. Treat the results as a description of these 158 videos, and a strong hint about many others.
About this research
VidCC Research keeps a private library of 2,151 findings on retention, storytelling, sound, packaging and analytics. Each one is tied to the source it came from and tagged by topic, and the library informs how we build and test VidCC's analysis. This article explains one part of it in our own words.
Where we cite numbers, they come from our analysis of 158 publicly posted YouTube Studio retention graphs. Studies that rely on them include the full methodology.