Benchmarks · Restaurant
Restaurant website benchmarks
From 39 deep scans of restaurant websites, window ending 2026-09-07.
What restaurant websites most often get wrong, measured. These figures come from 39 deep scans of publicly reachable small-business sites in Go Voltic's research corpus, sector-labelled, scanned at up to 40 pages per site during the 30 days ending 2026-09-07. No business is named and none can be identified: aggregates only. With 39 sites the intervals are wide; read the ranges, not just the point.
The most common findings in restaurant sites
| Finding | Share of sites | What it means |
|---|---|---|
| Third-party requests on first load | 97.4%95% CI 86.8% to 99.5% | The first page load contacts outside services before any consent is given. |
| Meta descriptions missing | 94.9%95% CI 83.1% to 98.6% | Pages without the summary line search results and AI answers lift. |
| Wrong number of H1 headings | 92.3%95% CI 79.7% to 97.3% | Pages rendering zero or several visible H1 headings instead of one. |
| Images missing alt text | 87.2%95% CI 73.3% to 94.4% | Images without an alt attribute, invisible to screen readers and image search. |
| Serious accessibility violations | 84.6%95% CI 70.3% to 92.8% | Automated WCAG testing reports violations at serious or critical impact. |
| Tap targets too small | 84.6%95% CI 70.3% to 92.8% | Interactive elements under 44 by 44 pixels at phone width. |
| No canonical URL | 76.9%95% CI 61.7% to 87.4% | Templates that never state their own preferred address. |
| Render-blocking scripts | 69.2%95% CI 53.6% to 81.4% | Head scripts without defer or async that stop the page from drawing. |
Share is the fraction of the 39 scanned restaurant sites that raised the finding. Intervals are 95% Wilson intervals computed from n = 39.
How this compares
The site-wide benchmarks draw on a much larger population of four-page scans. Deep scans read up to ten times more pages per site, so the two populations are measured at different depths and are never pooled. Compare the pattern of findings, not the raw shares.
Method
Population: 39 sector-labelled deep scans from our research sweep in the 30 days ending 2026-09-07. Sector labels are assigned conservatively; a site whose trade is unclear is bucketed as unknown rather than guessed, and unknowns are excluded here. Each finding is a deterministic check, identical to the checks in our paid reports. Sites that refused the scan are excluded and counted, never scored as zero. More: methodology.
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Related reading: why AI assistants skip a business and why a site loses customers.