user-satisfaction-signals

Agent Building

Interpreting implicit and explicit feedback — edits, regenerations, abandonment.

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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