A glowing golden path winding through soft clouds, symbolizing clarity, insight, and the upward journey of measuring what moves users.
October 8, 2025

Measure What Moves Users

A playbook for UX metrics that actually change the business

Executive Summary

If you’re tired of metrics that look good in presentations but don’t drive business results, this playbook is for you.

In 2025, the competitive edge for product teams isn’t better pixels, it’s better proof. The best designers know how to measure the invisible: friction, effort, trust.

High-impact formulas covered:

  • ODI Score → ranks unmet needs by economic value.
  • Friction Index → a composite measure of user struggle.
  • Path Optimality → reveals when your UI forces detours.

All of them can be implemented today, and rolled up into a single scorecard you review weekly.

The Problem with UX Metrics Today

Most UX metric decks die in two places: the research folder or the exec review. Why? Because they don’t prove what moves users—or the business.

Designers default to NPS, SUS, or time on task because they’re easy. But executives don’t care if a flow is “5.2 vs 5.8 on SEQ”—they care if friction is down, conversion is up, and support tickets aren’t spiking.

If you can show a causal chain from “fewer field errors” to “more revenue,” your seat at the strategy table is secure.

Where Most Teams Get Stuck

Across the industry, a handful of metrics dominate UX reports:

  • NPS (Net Promoter Score): Easy to collect, but it’s a loyalty proxy, not a product-quality signal. It moves slowly, can be gamed by incentives, and often reflects brand perception more than UX.
  • SUS (System Usability Scale): Reliable at a broad level, but too coarse to diagnose specific issues or guide iterative changes.
  • Time on Task: Ubiquitous in usability testing, but highly context-dependent. Sometimes longer means confusion; sometimes it means engagement. Without pairing it with Path Optimality or Interaction Cost, it can mislead.
  • Single Ease Question (SEQ): Quick to ask, but averaging across tasks hides outliers and doesn’t connect to outcomes.
  • Click-through / Bounce rates: Often mistaken for UX metrics, but usually reflect traffic quality or marketing rather than the experience itself.

Why These Fall Short

  • They’re lagging indicators: telling you after the fact that something went wrong.
  • They’re not diagnostic: “NPS dropped 3 points” doesn’t tell you where to act.
  • They’re too generic: disconnected from the flows that actually drive revenue.
  • They’re biased or noisy: survey fatigue, cultural bias, or misinterpreted data.

That’s why we need a sharper playbook: metrics that are causal, sensitive, diagnostic, and actionable.

1. Build a Metric Tree (Not Just a List)

A metric tree forces you to define causality.

Example: Checkout Flow

  • Business outcome: Gross revenue ↑
  • Behavioural layer: Checkout conversion, repeat purchase
  • Experience layer: Task success, friction index, CES per step, UX-Lite

Formula:
Checkout Conversion Rate = Successful Orders ÷ Checkout Attempts

If a metric doesn’t connect to the outcome through a clear arrow, drop it.

Diagram showing a UX metric tree where Revenue links to Checkout Conversion, which branches into Task Success, Friction Index, and Customer Effort Score.

2. Go Beyond Satisfaction

Generic satisfaction scores (NPS, even SUS) are blunt instruments. Instead:

  • UX-Lite → 2 questions, captures usability + usefulness.
  • ODI Score (Outcome-Driven Innovation)

Formula:
Opportunity = Importance + max(Importance − Satisfaction, 0)

Example:

  • Task: Entering shipping info
  • Importance = 8.2 / 10
  • Satisfaction = 5.1 / 10
  • → Opportunity = 11.3 → high priority

This reframes research into economic value of fixing problems, not “users seemed annoyed.”

3. Make Friction Measurable

Instead of relying on anecdotes, create a Friction Index, a composite of event-level signals:

  • Rage-click rate
  • Backtrack rate
  • Dead-end rate
  • Validation failure rate

Formula:
Friction Index = Σ (standardized z-scores of each component) ÷ n

This gives you a leading indicator that moves before conversion does.

Pie chart showing the Friction Index as a composite of user struggle signals—35% rage-clicks, 25% validation errors, 20% backtracks, and 20% dead-ends.

4. Efficiency Beyond Time on Task

Time on Task is noisy. More useful:

  • Interaction Cost = clicks + keystrokes + scrolls
  • Path Optimality = Actual Steps ÷ Optimal Steps
  • Cognitive Load Proxy = single-item NASA-TLX at critical steps

Example:

  • Optimal checkout = 6 steps
  • Actual avg = 9 steps
  • Path Optimality = 1.5 → UI forces 50% extra work

 

Diagram comparing an optimal user path of six steps to an actual path of nine, illustrating the Path Optimality formula: Actual ÷ Optimal = 1.5.

5. Guardrails (Don’t Optimize into Harm)

Every experiment should monitor:

  • Accessibility conformance rate (p75)
  • Support contact rate (24h post-task)
  • Refund/chargeback rate
  • Core Web Vitals (LCP/INP @ p75)
  • Equity cuts (device, locale, segment)

Guardrails prevent “winning” experiments that quietly erode trust.

6. Modern Experiment Hygiene

If metrics are going to drive launch calls, the stats must hold.

  • Power analysis + minimum detectable effect (MDE)
  • CUPED for variance reduction
  • Sequential testing / Bayesian monitoring
  • Sample Ratio Mismatch (SRM) alarms

Example: checkout redesign powered for +0.7pp conversion MDE, with CUPED baseline correction.

7. In-Product Micro-Surveys

Move away from post-study surveys. Use event-triggered prompts:

  • After “payment submitted,” ask CES (“How easy was that step?”).
  • Randomize delivery to avoid bias.
  • Join survey response to telemetry for causal driver analysis.

8. Cohort & Stickiness Views

Go beyond point-in-time scores. Add:

  • Adoption = tried ÷ eligible
  • Stickiness = repeat usage (D7/D28) ÷ first-time users
  • Time to Value = signup → first successful outcome

These tell you if delight turns into durable behaviour.

9. Case Study: Checkout Metric Stack

Objective: increase paid orders without spiking support or fraud.

Leading indicators: Friction Index ↓, Validation failures ↓, Path optimality → 1.0
Primary UX metrics: Task success, Task CES, UX-Lite
Guardrails: Support rate stable, Accessibility >95%, Refunds steady
Outcome: Conversion ↑ 0.9pp, Friction ↓ 28%, CES ↑ 1.1 pts

Leading indicators moved first, then conversion followed.

10. The Selection Rubric

Metrics can be gamed if you don’t enforce discipline. That’s why a rubric matters: it creates alignment and prevents “feel-good” metrics that don’t drive outcomes.

Rate each candidate 1–5 on:

  1. Causality → tied to business outcome
  2. Sensitivity → moves within 1–2 weeks
  3. Diagnostic value → tells what to fix
  4. Cost → effort to measure
  5. Integrity → risk of bias or gaming

Pick a mix across Outcome / Behaviour / Experience / Guardrail.

11. One-Page UX Scorecard

Every scorecard row should link back to a metric tree, if it doesn’t, revisit your selection. A scorecard is only valuable if it’s reviewed weekly. Weekly cadence closes the loop between releases and results, keeping design accountable and iterative.

Example UX scorecard table displaying five metrics—Checkout Conversion, Friction Index, Task CES, Support Contact Rate, and Accessibility p75—with targets, trends, and status indicators.

 

Wrap Up

The most valuable UX metrics don’t live in a spreadsheet—they live in weekly conversations. A one-page scorecard makes UX part of business performance, not just “user happiness.”

When every metric is causal, sensitive, and actionable, design shifts from reporting to revenue. That’s the difference between being a good designer and being a designer leaders can’t ship without.

👉 Ready to apply this? Connect with me on LinkedIn if you’d like the templates or to swap notes on scorecard setups, I’d love to see how other teams are measuring what really moves users.