YouTube Sponsorship Benchmarks by Usual Views

Channel size is more useful when it is based on repeatable views instead of subscriber count. In Creators Agency's study of 10,000 sponsored YouTube videos, we grouped channels by current usual views.

The public research brief on YouTube sponsorships by channel size includes the table, method, and limits behind these comparisons.

This is an all-category sponsorship sample, not a finance-only rate card. It does not include creator fees, sales, or return on investment. It shows where activity happened and where the same brand later appeared again.

How the 10,000 videos split by usual views

• Under 50,000 usual views: 4,986 sampled videos, or 49.9%, across 1,057 channels.
• 50,000 to 249,000 usual views: 3,097 videos, or 31.0%, across 621 channels.
• 250,000 or more usual views: 1,890 videos, or 18.9%, across 391 channels.

Current usual views were missing for 27 videos. The key point is that half of sampled sponsorship activity appeared on channels below 50,000 usual views. Brands were not using YouTube sponsorships only for large-reach buys.

The middle view band had the highest observed renewal rate

Among pairs old enough for a full-year check, observed renewal rates were:

• Under 50,000 usual views: 23.1%, or 186 of 806 eligible brand-channel pairs.
• 50,000 to 249,000 usual views: 32.7%, or 203 of 620 eligible pairs.
• 250,000 or more usual views: 27.2%, or 142 of 522 eligible pairs.

Using the underlying counts, the middle group was 9.7 percentage points above the under-50,000 group and 5.5 points above the 250,000-plus group. The relationship was not a straight line: the largest channels did not lead this measure.

Observed renewal means the same brand appeared on the same channel again within 365 days and more than 60 days after the prior sampled appearance. It does not prove a new contract, sales, or return on investment.

What the size bands mean for a media plan

Under 50,000 usual views: This group offered the broadest pool in the sample. It can help a brand spread risk and test specific audiences, but the observed renewal rate was the lowest of the three groups.

50,000 to 249,000 usual views: This group combined meaningful reach with the highest observed renewal rate. It is a strong place to build the center of a test, while still checking topic and audience fit.

250,000 or more usual views: This group offers scale per placement. Its renewal rate was close to the overall 27.3% benchmark, so scale alone should not be treated as proof of a better repeat-buying outcome.

Set price and structure from the campaign, not the band

The dataset does not contain fees, CPMs, or contract terms. A view band cannot tell a creator what to charge or a brand what a placement is worth.

Use current usual views as the reach baseline. Then account for audience fit, placement, creative work, usage rights, exclusivity, timing, and the value of the result to the brand. If a performance component is part of the deal, agree on tracking and payment rules before filming.

How brands should use these benchmarks

• Build a test across at least two usual-view bands when budget allows.
• Compare sponsored-video views with the channel's usual level, not subscriber count.
• Keep the offer and success measure consistent across the test.
• Use customer results to decide where to renew; use the public benchmark as context.

How creators should use these benchmarks

Show brands your current usual views and how past sponsored videos compared with that level. Add audience fit, verified clicks or conversions when available, and clear terms for usage or exclusivity. That is more useful than placing a subscriber number at the top of a media kit without context.

Our take

Half of sampled sponsorship activity happened below 50,000 usual views, but the middle group had the strongest repeat-buying signal. A sensible plan uses smaller channels for breadth, middle-sized channels as a strong testing center, and larger channels when the brand needs scale.

No band should win the budget by default. Channel size sets the shape of the test. Audience fit and the brand's own customer data should decide what comes next.