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AI Product Tools / MIF Explorer / Library / UX
Truth Layer
MeaningfulLeading 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.
Feature Retention
The percentage of users who continue using a specific feature over time after first trying it.
Evaluation method
users_using_feature_in_period_2 / users_who_first_used_feature_in_period_1 × 100
Signal type
leading
What it is best for
Evaluating whether a new feature is delivering sustained value
What it tells you +
Whether a feature delivers enough ongoing value to warrant repeat use.
What it does not tell you +
Tell you about overall product retention or whether the feature is critical to the product’s value proposition.
When to use it +
- Evaluating whether a new feature is delivering sustained value
- Deciding which features to invest in, simplify, or deprecate
When not to use it +
- For features designed to be used once (setup wizards, migration tools)
How leaders misuse it +
- Expecting all features to have high retention without considering one-time use cases
Anti-patterns +
- Forcing repeated feature usage through workflow requirements
Companion entries +
This entry is stronger when paired with:
Instrumentation or evaluation guidance +
Track per-feature over consistent time windows. Compare across features to identify sticky vs one-time features.
Sample events
feature_used Examples +
A collaboration tool’s "shared workspace" feature has 62% 30-day retention, while "custom themes" has 8%. The team doubles down on shared workspace and sunsets theme customization.
Suggested decisions +
- Feature retention below 15%: the feature is not delivering repeat value. Investigate or consider deprecating.
- Feature retention above 40%: strong signal. Ensure the feature is easy to find and well-supported.