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AI Product Tools / MIF Explorer / Library / UX
Truth Layer
MeaningfulLagging Truth Layer
The Truth Layer is the badge system that tells you how trustworthy, directional, or risky a measure is.
Why it matters: It helps teams separate meaningful signals from vanity, misuse, or AI distortion before they optimize the wrong thing.
Example: A metric can be Meaningful, Leading, or Vanity Risk.
Customer Satisfaction (CSAT)
A survey-based score measuring satisfaction with a specific interaction, feature, or experience, typically on a 1-5 or 1-7 scale.
Evaluation method
satisfied_responses (4-5 on 5-point scale) / total_responses × 100
Signal type
lagging
What it is best for
Measuring satisfaction with specific features or support interactions
What it tells you +
Whether a specific experience met user expectations. More targeted and actionable than NPS.
What it does not tell you +
Predict long-term loyalty or explain what specifically drove satisfaction or dissatisfaction.
When to use it +
- Measuring satisfaction with specific features or support interactions
- Before and after changes to key touchpoints
- Identifying which parts of the experience users value most
When not to use it +
- As a general product health metric without specifying what was being rated
How leaders misuse it +
- Aggregating CSAT across all interactions into one number, losing diagnostic value
Anti-patterns +
- Only surveying users who completed a task successfully, biasing results positive
Companion entries +
This entry is stronger when paired with:
Instrumentation or evaluation guidance +
Trigger immediately after specific interactions. Keep surveys short. Include optional free-text.
Examples +
CSAT for the search experience is 58% while CSAT for the dashboard is 89%. The team prioritizes a search redesign based on this gap.
Suggested decisions +
- CSAT below 70%: specific interaction needs investigation
- Identify the lowest-CSAT touchpoint and prioritize UX improvement there