/user-satisfaction-signals

name: user-satisfaction-signals
description: Interpreting implicit and explicit feedback — edits, regenerations, abandonment.

User Satisfaction Signals

Users rarely tell you directly whether they're satisfied. Most satisfaction signals are implicit — buried in behavior patterns that you have to design systems to capture and interpret.

Explicit Satisfaction Signals

These are signals users give intentionally:

• **Thumbs up/down**: Direct quality rating
• **Star ratings**: Graded satisfaction
• **Written feedback**: Comments about what worked or didn't
• **NPS or satisfaction surveys**: Periodic overall assessment
• **Feature requests**: Signals of engagement even when expressing a gap

Implicit Satisfaction Signals

These are behavioral signals that indicate satisfaction or dissatisfaction: **Positive signals:**

• Using the output as-is (no edits)
• Copying the output
• Returning to use the feature again
• Increasing usage over time
• Trying more advanced features

**Negative signals:**

• Regenerating the response (asking the AI to try again)
• Editing the output heavily
• Rephrasing the same request multiple times
• Abandoning mid-task
• Decreasing usage over time
• Switching to manual methods

**Ambiguous signals:**

• Long sessions (engaged or struggling?)
• Many turns (deep work or frustrated iteration?)
• Silence after a response (satisfied or confused?)

Designing Signal Collection

• **Instrument the product**: Track edits, regenerations, copy events, session duration, and return patterns
• **Minimise explicit feedback burden**: Don't ask for ratings on every response
• **Contextualise signals**: A regeneration during creative brainstorming means something different than a regeneration during fact-finding
• **Segment by task type**: Satisfaction patterns vary by what the user is trying to do
• **Combine signals**: No single signal is reliable. Look for patterns across multiple signals.

From Signals to Insights

Raw signals need interpretation:

• **Signal clustering**: Which negative signals appear together? That pattern indicates a specific problem.
• **Trend analysis**: Are signals improving or degrading over time?
• **Cohort comparison**: Do new users show different signals than experienced users?
• **Correlation with outcomes**: Which signals best predict task success or retention?

Design Artefacts

• Signal inventory (explicit and implicit) with collection methods
• Signal interpretation guidelines
• Satisfaction dashboard specifications
• Signal-to-insight analysis frameworks
• Feedback collection touchpoint map