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