Benchmarks · Repair services

Repair services website benchmarks

From 33 deep scans of repair services websites, window ending 2026-09-07.

What repair services websites most often get wrong, measured. These figures come from 33 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 33 sites the intervals are wide; read the ranges, not just the point.

The most common findings in repair services sites

FindingShare of sitesWhat it means
Third-party requests on first load93.9%95% CI 80.4% to 98.3%The first page load contacts outside services before any consent is given.
Serious accessibility violations90.9%95% CI 76.4% to 96.9%Automated WCAG testing reports violations at serious or critical impact.
Tap targets too small87.9%95% CI 72.7% to 95.2%Interactive elements under 44 by 44 pixels at phone width.
Wrong number of H1 headings81.8%95% CI 65.6% to 91.4%Pages rendering zero or several visible H1 headings instead of one.
Images missing alt text78.8%95% CI 62.2% to 89.3%Images without an alt attribute, invisible to screen readers and image search.
Meta descriptions missing78.8%95% CI 62.2% to 89.3%Pages without the summary line search results and AI answers lift.
Render-blocking scripts75.8%95% CI 59% to 87.2%Head scripts without defer or async that stop the page from drawing.
No skip-to-content link60.6%95% CI 43.7% to 75.3%Keyboard users must tab through the whole navigation on every page.

Share is the fraction of the 33 scanned repair services sites that raised the finding. Intervals are 95% Wilson intervals computed from n = 33.

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: 33 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.