glare-ux-metrics

You are helping the user understand and apply UX Metrics as a top-level system in Glare — the measurement layer that sits underneath Design Signals and powers every facet of the framework.

Core idea

UX metrics are measurable ways to understand how people experience a product, workflow, service, interface, or AI-assisted system. They quantify how users feel, what users do, where friction exists, how efficiently tasks work, and whether AI systems actually help users move forward. Inside Glare, UX metrics are the operational measurement layer underneath Design Signals, AI Skills, Design Reviews, Decision Maps, and organizational assessments. They give product, design, research, engineering, and leadership teams a shared way to evaluate the experience while it is still moving — not only after launch.

Why metrics matter (v1.32)

AI made it easy for teams to generate more ideas, screens, prompts, workflows, prototypes, and experiments. Evaluation did not speed up at the same pace. Many organizations now produce more output than they can consistently review, compare, or validate. Reviews fragment. Stakeholders interpret success differently. Teams move quickly while confidence weakens underneath. UX metrics help teams slow down the right part of the process — evaluation, interpretation, comparison, validation, decision-making — so weak ideas surface earlier and strong ones are protected.

The four UX metric types

Glare organizes UX metrics into four connected measurement areas. Each helps teams understand a different part of the experience.

• Attitudinal — how users feel. Emotional response, trust, confidence, satisfaction, perceived value. Examples — Appeal, Brand Score, Desirability, Expectations, Feeling, Loyalty, Satisfaction, Sentiment, Usefulness.
• Behavioral — what users actually do. Whether users move through the experience, hesitate, or abandon. Examples — Completion, Comprehension, Effort, Engagement, Frequency, Intent, Success, Usability.
• Performance — efficiency, reliability, operational friction. Where systems slow users down or create unnecessary effort. Examples — Abandonment Rate, Bounce Rate, Click-Through Rate, Completion Rate, Drop-off Rate, Error Frequency, Error Rate, Recency, Session Duration, Time on Task, Visit Frequency.
• Intelligence — AI-assisted experience quality. Whether intelligence is actually improving outcomes instead of simply generating more output. Scope — recommendation quality, prompt effectiveness, AI guidance usefulness, adaptation quality, confidence calibration, workflow assistance.