A command-line Python tool for calculating, evaluating, and reporting core UX research metrics from usability study data.
Author: Susan E. Aldridge
Language: Python 3 — no dependencies, no installs required
GitHub: github.com/saldridge1
Paste your study data in and run it. The dashboard calculates every metric, rates performance against industry benchmarks, generates contextual insights, and outputs a clean formatted report — all in one command.
| Metric | What It Measures |
|---|---|
| System Usability Scale (SUS) | Perceived usability — scored 0–100 with adjective rating and percentile rank |
| Task Completion Rate | % of tasks successfully completed vs. industry benchmark (78%) |
| Time on Task | Mean, median, min, max, std deviation — compared against your goal time |
| Error Rate | Errors as a proportion of error opportunities vs. 5% threshold |
| Net Promoter Score (NPS) | Promoter/passive/detractor breakdown — scored –100 to +100 |
════════════════════════════════════════════════════════════════
UX METRICS DASHBOARD
Meridian Analytics — Predictive Insights Panel Redesign
Generated: April 02, 2026 03:14 PM
════════════════════════════════════════════════════════════════
SYSTEM USABILITY SCALE (SUS)
────────────────────────────────────────────────────────────────
✅ Average SUS Score 81.2 / 100
✅ Adjective Rating Excellent
✅ Percentile Rank ~80th percentile
SUMMARY SCORECARD
════════════════════════════════════════════════════════════════
SUS Score 81.2 / 100 Excellent
Task Completion Rate 95.0% Excellent
Mean Time on Task 0m 57s Significantly faster than goal
Error Rate 5.00% Acceptable
Net Promoter Score +62.5 Excellent
════════════════════════════════════════════════════════════════
1. Download the file
git clone https://github.com/saldridge1/ux-metrics-dashboard.git2. Open ux_metrics_dashboard.py in any text editor
3. Replace the sample data with your study data
# SUS responses — one list of 10 per participant (1-5 scale)
SUS_DATA = [
[4, 2, 4, 1, 4, 2, 5, 1, 4, 2], # P01
[5, 1, 4, 2, 5, 1, 5, 1, 5, 1], # P02
# add more participants...
]
# Task completion
COMPLETION_DATA = {
"completed": 38, # tasks successfully completed
"attempted": 40, # total tasks attempted
}
# Time on task — in seconds
TIME_DATA = {
"times": [48, 62, 55, 41, 88, 73, 52, 44],
"goal_seconds": 90,
}
# Error rate
ERROR_DATA = {
"errors": 6,
"opportunities": 120,
}
# NPS responses (0-10)
NPS_DATA = [9, 10, 8, 9, 7, 10, 9, 8]4. Run it
python3 ux_metrics_dashboard.pyNo pip installs. No virtual environments. No dependencies. Just Python 3.
The System Usability Scale uses 10 alternating positive/negative questions rated 1–5. This tool applies the standard scoring formula automatically.
| Score Range | Adjective Rating | Percentile |
|---|---|---|
| 84.1 – 100 | Best Imaginable / Excellent | Top 10% |
| 71.4 – 84.0 | Good | 55th–80th |
| 62.7 – 71.3 | OK | 30th–55th |
| 51.0 – 62.6 | Poor | 13th–30th |
| Below 51.0 | Awful / Worst Imaginable | Bottom 13% |
Industry passing threshold: 68
- SUS — Brooke, J. (1996). SUS: A quick and dirty usability scale.
- SUS Adjective Ratings — Bangor, Kortum & Miller (2009)
- SUS Percentile Norms — Sauro & Lewis (2016)
- Task Completion Benchmark — Nielsen Norman Group industry data
- NPS — Reichheld, F. (2003). The One Number You Need to Grow.
If this framework has been useful for your GovCon pursuits, consider buying me a coffee. It helps me keep building open source tools for the design and GovCon community.
- ux-case-study — Enterprise SaaS Feature Adoption Case Study
- accessibility-case-study — Cognitive Accessibility Design Case Study
- benchline-framework — Design Intelligence Measurement Framework