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