Searching for sound off ad benchmarks by industry usually means you want a number to aim for: how many people should keep watching a muted ad, how fast do captions need to land, what counts as a 'good' hook for a skincare brand versus a software tool. The honest answer is that no single public benchmark set covers every industry reliably, because platforms don't publish that data by category and most of what circulates online is secondhand or outdated. What you can build, instead, is a repeatable framework for setting your own sound-off benchmarks by industry, using directional starting points you test and refine with your own data.
Why a single number for sound off ad benchmarks by industry is misleading
Industries differ in how much visual proof they can show without words. A phone stand can demonstrate its function in two seconds flat on screen. A B2B software tool usually needs a few seconds of UI and a caption explaining the benefit before a viewer understands what they're looking at. A skincare serum relies on texture, before/after framing, and on-screen claims. Because the starting point for comprehension is different, the retention curve for a muted ad looks different too, even with identical production quality.
On top of that, 'industry' is a loose category. Two brands selling supplements can have opposite audiences, price points, and purchase triggers, so their sound-off performance can diverge even with the same product type. Any benchmark you find online was measured on someone else's audience, creative style, and platform mix. Treat it as a reference point, not a target you must hit.
The metrics worth tracking before you chase a benchmark
Before comparing yourself to an industry number, make sure you are tracking the metrics that actually describe sound-off performance. Three matter most:
- 3-second hold rate — the share of viewers still watching past the first few seconds, when a muted viewer decides whether the ad is worth their attention.
- Caption-driven comprehension — whether a viewer who never turns on sound still understands the offer, measured indirectly through click-through or add-to-cart from muted sessions where available.
- Drop-off at the offer or CTA — where in the timeline viewers stop watching, which tells you whether the problem is the hook, the pacing in the middle, or the ask itself.
If you are not isolating these, an industry benchmark is noise. For a deeper look at how to pull apart where exactly viewers leave, see video ad drop-off rate analysis tips and the broader numbers to compare against in muted video ad engagement benchmarks.
A framework for setting your own sound-off benchmarks by industry
Instead of importing a number, build a baseline from your own account in four steps.
- Pull your last 10 to 20 video ads and sort by spend or impressions so you have a real sample, not one lucky winner.
- Mute every one of them and watch the first 3 seconds only. Note which ones you'd keep watching without sound, and why — a visual change, a bold on-screen claim, motion, a face.
- Tag each ad by variable, not just by industry: hook type (demo, claim, before/after, question), caption density (heavy, light, none), and pacing (cut every second, cut every 3 seconds).
- Compare retention and conversion across those tags, not across competitors. This gives you a benchmark that is actually yours, built from your product category and your audience.
This is slower than copying a number from an article, but it holds up under scrutiny when you report results, and it keeps improving as you add more ads to the sample.
Directional starting points by industry
The table below is not a published data set. It's a set of reasonable first-test targets based on how much visual proof each category can typically show without sound, and what tends to cause early drop-off. Use it to pick a starting hypothesis, then replace it with your own numbers once you have a sample.
| Industry category | Common cause of early drop-off | Caption/visual emphasis to test | A starting 3-second hold target to test against |
|---|---|---|---|
| Beauty and skincare | Hook opens on packaging instead of result | Lead with texture or before/after, captions reinforcing the claim | Aim for roughly 55–65% as a first test benchmark |
| Apparel and accessories | Static product shot with no motion | Show the product worn or in use within the first second | Aim for roughly 50–60% as a first test benchmark |
| Home and gadgets | Function isn't obvious without narration | Demonstrate the mechanism visually, captions label what's happening | Aim for roughly 55–65% as a first test benchmark |
| Supplements and wellness | Claim-heavy opening with no visual proof | Pair any claim with a visual cue (pour, dose, routine shot) | Aim for roughly 50–60% as a first test benchmark |
| Software and apps | Interface shown without context for a new viewer | Caption the problem before showing the screen | Aim for roughly 45–55% as a first test benchmark |
| Events and nightlife/promotions | Static poster with no motion or urgency cue | Animate the key detail (date, name, offer) first | Aim for roughly 55–65% as a first test benchmark |
These ranges are deliberately conservative starting points, not performance guarantees. Some accounts will beat them easily, some won't, depending on audience fit, offer strength, and how much the product benefits from motion. If you want niche-level comparisons rather than broad industry categories, UGC ad performance benchmarks by niche breaks this down further.
How to test your ad against those targets
Once you have a starting number, run a structured test rather than a single launch-and-hope campaign.
- Keep the offer and audience constant; change only the hook across two or three versions.
- Mute your own screen and watch each version cold, as if you'd never seen the product, before you ever spend on it.
- Launch all versions with similar budget and let them run long enough to clear early noise before comparing hold rate.
- Record the winner's structure (not just its result) so you can reuse the pattern, not just the one ad.
A full step-by-step process for this kind of check, including what to look for frame by frame, is covered in how to test if your ad works without sound. If you're still building the ad itself, how to design ads for sound-off viewing walks through the structural choices that affect these numbers before you even get to testing.
Common mistakes that skew sound-off benchmarks
A few patterns quietly distort benchmark comparisons, even when the testing process looks rigorous:
- Comparing across placements. A feed placement and a Stories placement don't behave the same muted, so mixing them in one benchmark blurs the result.
- Ignoring caption style. Word-by-word captions and full-sentence captions produce different comprehension speeds; comparing ads with different caption styles as if they were equal skews the number. See caption styles for sound-off video ads for how style alone changes retention.
- Treating one winning ad as the new baseline. A single strong performer can be an outlier driven by a timely offer, not a repeatable creative pattern.
- Skipping the rebuild of silent audio cues. Sound effects and music timed to visual beats still shape pacing even when sound is off, because they influenced how the edit was cut; see product video ad sound design tips for why this matters even in a silent view.
- Not refreshing the benchmark. Creative fatigue and shifting attention patterns mean a target set a year ago may already be stale.
Where FrameNotion fits
If part of your benchmarking problem is simply producing enough ad variations to test hooks, pacing, and caption density at volume, that's the gap FrameNotion is built to close. You paste in a product link, and FrameNotion AI writes and renders a 30-second vertical ad from scratch, built to work with sound on or fully muted, with word-by-word captions included by default. A finished ad takes about 10 to 20 minutes, which makes it realistic to produce the two or three hook variants a proper benchmark test requires instead of relying on a single ad and guessing. Ads also render in 4:5, 1:1, and 16:9 alongside the main 9:16, so the same tested creative can run across placements without a separate edit. You can see finished examples at examples or check how the process works on features.
FrameNotion does not publish or track performance data, and there's no built-in analytics or direct publishing to ad platforms — the benchmarking and testing described above still happens in your ad accounts. What FrameNotion removes is the production bottleneck that keeps most accounts from testing enough variants to build a real benchmark in the first place.
For teams running this across multiple product lines, plans start on pricing, with one-off packs available if you just need a handful of test variants before committing to a monthly plan.
Frequently asked questions
Is there an official source for sound off ad benchmarks by industry?+
No. Platforms don't publish retention or hold-rate data broken down by industry category, so any number you see in an article or deck is an estimate from someone's own account, not an official figure. Use published ranges as a starting hypothesis and validate with your own tests.
How often should I update my sound-off benchmark?+
Revisit it every few months, or sooner if you notice overall engagement dropping across your account. Creative fatigue, seasonal attention shifts, and changes in your own audience mix can move your baseline faster than you'd expect.
Should I set one benchmark for my whole account or one per product line?+
Per product line, if the products differ enough in how they demonstrate value visually. A skincare serum and an accessory sold by the same store will likely have different realistic hold-rate targets, so a single blended benchmark can hide underperformance in one line.
What sample size do I need before trusting my own benchmark?+
There's no fixed number, but comparing fewer than 8 to 10 ads per variable (hook type, caption style, pacing) tends to be too small to separate a real pattern from noise. Build up the sample over a few testing cycles rather than deciding after one batch.
Does a strong sound-off benchmark mean the ad will also perform with sound on?+
Not necessarily. Sound-off performance measures visual clarity and pacing; sound-on performance adds voiceover timing, music, and audio hooks into the mix. Testing both separately is covered in sound on vs sound off ad performance comparison.