UX Metrics = numbers that tell you how your product is performing.
The problem: most teams track numbers that feel important but don't tell you if users are actually getting things done.
Easy metrics go up: clicks, time on site, and daily users. Everyone nods. The product quietly gets worse.
The rule: a metric that is easy to move is rarely the one that matters.
The 7 Metrics Lying to You
For each one: what it is in plain English, why it lies, and what to track instead.
1. Time on Site
What it is: How long users spend in your app.
The lie: More time could mean users love it, or they're completely lost and can't find anything.
Track instead: Task Completion Time vs. Benchmark, how long does it take to finish your top 5 tasks compared to a target? Falling time = improving design.
2. Page Views
What it is: How many screens users visit per session.
The lie: More pages could mean engaged exploring, or broken navigation, forcing users to wander.
Track instead: Navigation Efficiency Score, pages viewed divided by tasks completed. Score of 1 = perfect. Score of 8 = lost.
3. Daily Active Users (DAU)
What it is: How many people opened the app today?
The lie: A user who opens the app, sees nothing useful, and closes it still counts as a DAU.
Track instead: Active Value Days, days where the user actually completed something meaningful (sent a doc, finished a workflow, hit a goal).
4. Bounce Rate
What it is: Users who leave after viewing just one page.
The lie: A user who found the phone number they needed in 10 seconds and left = 100% bounce rate and a perfect session.
Track instead: Intent-Completion Rate by Entry Point, did users achieve what they came for, regardless of how many pages they visited?
5. NPS (Net Promoter Score)
What it is: "Would you recommend us?" on a scale of 0–10.
The lie: It tells you how people felt when you asked. Not what you should design differently.
Track instead: Customer Effort Score (CES) at task completion, one question: "How easy was that?" right after they finish something.
6. Feature Adoption Rate
What it is: How many users tried a feature.
The lie: A feature with 80% adoption and 80% abandonment after one use is a failure — not a win.
Track instead: Feature Retention at 30 Days, of users who tried it, how many came back? High retention = real value. High adoption + low retention = novelty.
7. Error Rate (Standalone)
What it is: How often errors occur.
The lie: A low error rate can mean great design, or that the product is so restrictive users can't do anything that might fail.
Track instead: Error Recovery Rate, when errors happen, do users successfully recover and complete their task? That's the real quality signal.
The Metrics That Actually Matter
Three tiers:
- Tier 1 — Outcome Metrics: Did users achieve their goal? (Task success rate, goal completion, confidence)
- Tier 2 — Experience Metrics: How did it feel? (Customer Effort Score, SUS score, error recovery)
- Tier 3 — Signal Metrics: Where should we investigate? (Navigation efficiency, 30-day retention, rage clicks)
Most teams only have Tier 3. They see signals but never connect them to outcomes.
The Replacement Map
| Replace | Track instead |
|---|---|
| Time on site | Task completion time vs. benchmark |
| Page views | Navigation efficiency score |
| DAU | Active Value Days |
| Bounce rate | Intent-completion rate by entry point |
| NPS | Customer Effort Score at task completion |
| Feature adoption | Feature retention at 30 days |
| Error rate | Error recovery rate |
AI Products Need New Metrics
Old metrics were built for predictable apps. AI is unpredictable.
New metrics to track:
- Edit Distance: How much did users change the AI's output? Low edits = AI understood context.
- Delegation Rate: Are users trusting the AI with more over time? Rising = growing trust.
- Intervention Frequency: How often do users stop or correct the AI? High = trust problem.
- Recovery Success Rate: When the AI fails, do users still complete their goal?
The 4-Question Audit
Before keeping any metric on your dashboard, ask:
- If this number went up 20%, would we make a different design decision? (If no — it's decorative.)
- Does this measure what users achieved, or just what they did?
- Can we tell if changes were caused by design or by outside factors?
- Does this align with what users told us they actually care about?
If a metric fails questions 1 or 2, remove it from your primary dashboard.
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