Accessibility analytics are metrics and visualizations that show the state of digital accessibility across a website, web app, mobile app, or portfolio of assets. They track issue counts, severity ratings, WCAG success criteria coverage, remediation progress, and conformance levels over time. The most reliable accessibility analytics are built from (manual) audit data because scans only flag approximately 25% of issues. Analytics derived from scan results give a partial picture at best. Analytics derived from a full audit give decision-makers an accurate read on where a product stands against WCAG 2.1 AA or WCAG 2.2 AA.
| Element | What It Shows |
|---|---|
| Data Source | Audit reports give accurate analytics. Scan results give partial data only. |
| Core Metrics | Issue count, severity, WCAG criteria affected, remediation status |
| Primary Use | Prioritize fixes, track progress, report to leadership, document conformance |
| Best Format | Platform dashboards with filters by project, page, criterion, and status |
| Key Limit | Analytics are only as accurate as the evaluation method behind them |
What Do Accessibility Analytics Measure?
At the most basic level, accessibility analytics count issues. But a count alone is not useful. Analytics become meaningful when issues are tied to severity, WCAG success criteria, page or screen location, and remediation status.
A strong analytics view answers questions like: How many critical issues remain open? Which WCAG criteria are affected most? Which pages carry the highest risk? How much progress has the team made this month?
These views turn raw audit findings into decisions. Without them, an audit report is a static document. With them, the same data becomes a working map for remediation.
Audit-Based Analytics vs. Scan-Based Analytics
This is the distinction that matters most. Scan-based analytics pull from automated checker output. Audit-based analytics pull from the findings of a human auditor who evaluated the product against every applicable WCAG success criterion.
Scans detect roughly 25% of accessibility issues. That means a dashboard built on scan data is missing about three-quarters of what an audit would identify. Metrics that look clean on a scan-based platform can hide significant conformance problems.
Audit-based analytics reflect the full picture. Issue severity is accurate. Criteria mapping is accurate. Progress toward WCAG conformance is measurable in a way that holds up to scrutiny.
How Are Accessibility Analytics Used?
Teams use analytics to prioritize remediation work. Risk Factor or User Impact prioritization formulas let a project lead filter the highest-severity issues and assign them first. Analytics make that triage fast.
Leadership uses analytics to track progress across a portfolio. When a company has dozens of digital assets, a dashboard that rolls up conformance status by product, region, or team becomes necessary for reporting.
Procurement teams use analytics internally to vet products before purchase. A vendor ACR tells part of the story. Ongoing analytics, if available, tell the rest.
What Metrics Should Accessibility Analytics Include?
Useful analytics cover a few core categories:
Issue volume: total open, total resolved, total validated
Severity breakdown: critical, high, medium, low
WCAG mapping: which success criteria are affected and how often
Remediation status: open, in progress, fixed, validated
Progress over time: trend lines showing issue reduction
Asset-level views: per page, per screen, per product
Beyond these basics, the better platforms layer in AI-generated insights that summarize patterns in the data and recommend next steps based on audit findings.
Where Do Accessibility Analytics Live?
Analytics can live in a spreadsheet, but spreadsheets get stale fast. A project management platform built for accessibility tracks issues, updates status in real time, and generates analytics automatically. The Accessibility Tracker Platform is one example, built specifically for audit-based data.
When analytics are tied to an accessibility audit report, every metric traces back to a documented issue with a WCAG reference, severity rating, and remediation guidance. That traceability is what makes analytics defensible in a compliance context.
Why Do Accessibility Analytics Matter?
Accessibility work is an ongoing program, not a one-time project. Products change. Content gets added. New issues appear. Analytics give teams a way to see change over time and respond before issues compound.
They also give leadership something concrete to review. “We resolved 142 critical issues this quarter” reads differently than “we’re working on accessibility.” The first is a result. The second is a plan without proof.
Accessible.org Labs is actively researching how AI can support analytics by surfacing patterns in audit data and guiding remediation order more efficiently. The focus stays on real AI applied to real audit findings, not automated claims of conformance.
Frequently Asked Questions
Can accessibility analytics prove WCAG conformance?
Analytics can show the absence of known issues against a specific standard like WCAG 2.1 AA, which supports a conformance claim. The underlying audit is what establishes conformance. Analytics visualize and track the state of that conformance over time.
What’s the difference between accessibility analytics and an audit report?
An audit report is the source document that identifies issues at a point in time. Analytics are the live view of those issues as the team works through them, including any new findings from follow-up evaluations.
Do automated scans produce reliable accessibility analytics?
Scans produce partial analytics. They flag about 25% of issues and miss the rest. Scan-based dashboards are useful for continuous monitoring of known issue types, not for measuring true WCAG conformance.
How often should accessibility analytics be reviewed?
Weekly reviews work well during active remediation. Monthly reviews are reasonable for maintenance phases. Quarterly reviews are the minimum for leadership reporting across a portfolio.
Can AI generate accessibility analytics automatically?
AI can summarize audit data, flag patterns, and recommend prioritization based on severity and WCAG impact. It cannot replace the human evaluation that produces the underlying data. Real AI makes analytics faster to interpret, not a substitute for the audit itself.
Accessibility analytics are only as good as the data feeding them. An audit built on thorough manual evaluation gives analytics that decision-makers can actually use.
Contact Accessible.org to discuss an accessibility audit and analytics for your product.
