UX Metrics

UX metrics

The quantitative measurement layer for UX. Use it to choose meaningful metrics, instrument them, and read the results honestly — covering performance, automated quality scores, analytics events, and self-reported usability.

When to use

Trigger when the user wants to "measure UX", set up or "track LCP/INP/CLS", instrument "Core Web Vitals", interpret a "Lighthouse score", "design analytics events", compute a "SUS score", define "usability metrics", or asks "how do I quantify UX". Detect the project's stack first via ../ux-foundations/references/stack-detection.md and adapt to any analytics or perf library already present.

1. Core Web Vitals

Google's user-centric performance signals. Thresholds (the "good" boundary is the one to design for; "poor" begins past the second number):

INP replaced FID as a Core Web Vital in March 2024 — use INP.

Instrument with the bundled scripts/web-vitals-snippet.js (a copy-paste template using the web-vitals v4 library). It reports each metric to console and an endpoint.

Field vs lab data — distinguish the two and report both when you can:

• Field (RUM) — real users, real devices and networks. The source of truth for CWV (e.g. CrUX, your own web-vitals beacons). Aggregate at the 75th percentile across page loads, per metric.
• Lab — synthetic, controlled run (Lighthouse, WebPageTest). Reproducible and good for debugging regressions, but a single device/network — not the user-experienced number. Note: lab cannot measure INP (no real interactions); it reports TBT as a proxy.

2. Lighthouse / axe scores

• Lighthouse runs five categories: Performance, Accessibility, Best Practices, SEO, and PWA. The Performance score is a weighted blend of lab metrics (FCP, LCP, TBT, CLS, Speed Index).
• Limits — Lighthouse Performance is a lab proxy, not a field result; a 100 does not mean fast for real users, and a low score may just reflect a slow test device. The Accessibility score (axe-based) only covers automatable checks (~a third of WCAG) — a 100 is not a passed accessibility audit. For real conformance, run the accessibility-audit skill.
• Treat scores as trend indicators and regression alarms, not certificates.

3. Analytics event taxonomy

Design a consistent schema before adding tracking; retrofitting is expensive.

• Naming — pick one convention and enforce it. Object-Action is recommended: <object>_<action> (e.g. checkout_started, video_played, signup_completed). Use past tense, snake_case (or match the existing codebase), and never free-form per-developer names.
• Properties — attach structured context (e.g. plan: "pro", source: "nav", value: 49). Define a shared property dictionary. Never log PII (email, name, raw IP, precise location) in event payloads; use a stable hashed/opaque user id.
• Metrics to build from events:
  • Funnel / conversion — step-to-step drop-off through a flow.
  • Task success rate — % of attempts that reach the goal event.
  • Time on task — duration from start event to success event.
  • Error rate — error events ÷ attempts.
• Adapt to the project — if an analytics lib is present (Segment, PostHog, GA4, Amplitude, Mixpanel, etc.), use its track(event, properties) API and identify conventions rather than inventing a parallel system.

4. Self-reported usability (SUS)

The System Usability Scale — a validated 10-item questionnaire, fast and benchmarkable.

• Items (odd = positive, even = negative), each rated 1 (strongly disagree) – 5 (strongly agree):
  1. I think I would like to use this system frequently.
  2. I found the system unnecessarily complex.
  3. I thought the system was easy to use.
  4. I think I would need support of a technical person to use this system.
  5. I found the various functions in this system were well integrated.
  6. I thought there was too much inconsistency in this system.
  7. I imagine most people would learn to use this system very quickly.
  8. I found the system very cumbersome to use.
  9. I felt very confident using the system.
  10. I needed to learn a lot of things before I could get going.
• Scoring — odd items contribute (response − 1); even items contribute (5 − response); sum all ten and multiply by 2.5 → a 0–100 score (it is not a percentage).
• Interpretation — the average across products is ~68 (grade C). Use the Sauro–Lewis curved grade: ≥80.3 = A (excellent), ~68 = C (average / "OK"), <51 = F (poor). Pair with an adjective rating (e.g. "Good", "OK", "Poor").
• When to use — a quick post-task or post-study perception measure; needs ~5+ respondents to be meaningful and is best compared to your own prior scores or the 68 benchmark.
• Compute with node scripts/sus-score.mjs r1 r2 ... r10.

How to pick metrics

Don't measure everything. Tie each metric to a goal using the HEART framework (Google): Happiness, Engagement, Adoption, Retention, Task success. For each chosen dimension, define a Goal → Signal → Metric chain. Map metrics to specific user goals, and avoid vanity metrics (raw pageviews, total signups) that move without reflecting real value — prefer rates, percentiles, and per-user measures.

References

• references/metrics-glossary.md — thresholds and one-line definitions for CWV, lab metrics, Lighthouse, HEART, and common UX metrics.
• scripts/sus-score.mjs — SUS calculator with grade interpretation.
• scripts/web-vitals-snippet.js — copy-paste Core Web Vitals instrumentation template.