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Usability Goals

Here are some numbers to look out for to measure success when testing. 

My Story

1. Task Completion Rate (80–90%)

Source: Nielsen Norman Group (NN/g) — Usability Metrics

NN/g states that task completion is the #1 usability metric and that successful rates for well-designed systems typically fall between 80–95% depending on complexity.

  • NN/g: “Success rate is the most important usability metric.”

  • NN/g: “80% success is generally acceptable; <70% indicates serious usability issues.”

 

2. Error-Free Rate (70–80% acceptable)

Source: Usability.gov + ISO 9241-11

Usability.gov recommends tracking error frequency and error severity, with successful systems demonstrating high accuracy and low error rates in testing.

ISO 9241-11 defines usability as:

“Effectiveness, efficiency, and satisfaction in a specified context.”

Effectiveness = error-free task performance, usually expected to be in the 70–90% range for enterprise products.

 

3. First-Click Success (75–90%)

Source: Bob Bailey & NN/g — “First Click Testing” (original research)

Bailey’s research shows:

  • If users click the correct element first, 87% complete the task successfully.

  • If not, only 46% succeed.

Therefore, most companies adopt:

  • 75%+ first-click success = acceptable

  • 85–90% = excellent

 

4. Navigation/Findability (80%+)

Source: Baymard Institute (navigation & findability studies)

Baymard sets a benchmark that 80% of users should find the right pathway within their first attempt for navigation to be considered intuitive.

Although Baymard focuses on e-commerce, their navigation heuristics apply universally to:

  • Enterprise admin portals

  • Payroll tools

  • HCM dashboards

 

5. Time-on-Task Consistency (70% similar range)

Source: Nielsen Norman Group

NN/g recommends comparing time-on-task within a participant group.

The rule of thumb:

  • If 70%+ of users complete the task in a similar time range → design is consistent.

  • Large time variance indicates discoverability issues or unclear pathways.​

 

6. Cognitive Load / Hesitation (3+ seconds pause)

Source: NN/g + UX research on “Hesitation Indicators”

Multiple observational research studies show that:

  • A “hesitation pause” of 3–5 seconds indicates confusion or cognitive overload.

Designers and researchers at:

  • Google

  • IBM

  • Salesforce

  • ServiceNow
    use hesitation as a signal during prototype testing.

 

7. Label/Copy Comprehension (80%+)

Source: Content Design Standards (Gov.UK + NN/g + UX Content Collective)

These groups consistently use the benchmark:

“80% of users should interpret labels or instructions correctly.”

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8. Confidence Rating (4.0+ acceptable, 4.5+ ideal)

Source: System Usability Scale (SUS) Research

SUS uses 1–5 Likert scales.
In SUS scoring:

  • A mean score of 4+ correlates with acceptable usability

  • 4.5+ correlates with high usability

Internal UX teams use confidence as a SUS-adjacent metric.

 

9. “Rule of 3” (Three Users = Real Issue)

Source: Jakob Nielsen (NN/g) — “Why You Only Need to Test With 5 Users”

Nielsen’s research shows:

  • After 3 users, most major usability problems are detected.

  • After 5 users = diminishing returns.

So:

  • If 3+ people struggle → it’s a real UX issue.

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10. Confirmation / Microinteraction Visibility (70%+ noticing)

Source: NN/g & Luke Wroblewski’s UI feedback research

NN/g notes that:

  • Confirmation messages should be immediately perceived by at least 70% of users.

  • If fewer notice the confirmation → it's placed incorrectly or styled poorly.

This is used widely in:

  • Enterprise admin systems

  • Internal tools testing

  • Payroll confirmation screens

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SUMMARY OF SOURCES

These benchmarks are derived from:

Primary Authorities

  • Nielsen Norman Group (NN/g) – the industry standard for usability metrics

  • Usability.gov – U.S. government usability standards

  • ISO 9241-11 – international standard for usability

  • System Usability Scale (SUS) – gold standard UX score

  • Baymard Institute – expert in navigation, findability, and interaction patterns

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