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AI Product Tools / MIF Explorer / Library / UX
Truth Layer
DirectionalLeading 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.
Depth of Use
The number of distinct features or meaningful actions a user engages with in a session or time period.
Evaluation method
distinct_features_used / total_available_features
Signal type
leading
What it is best for
Understanding whether users are discovering the full value of the product
What it tells you +
Whether users are exploring the product broadly or sticking to one narrow path.
What it does not tell you +
Tell you whether breadth of use equals satisfaction or whether depth is even desirable for all user types.
When to use it +
- Understanding whether users are discovering the full value of the product
- Identifying features that power users rely on versus those ignored by most
When not to use it +
- For simple, single-purpose tools where broad feature usage is not expected
- To compare products with different feature set sizes
How leaders misuse it +
- Assuming broader feature usage is always better — focused use can indicate efficiency
Anti-patterns +
- Forcing feature tours or mandatory exploration to inflate depth metrics
Companion entries +
This entry is stronger when paired with:
Instrumentation or evaluation guidance +
Count distinct feature areas, not raw click counts. Define what qualifies as a "feature" consistently.
Sample events
feature_used Examples +
A design tool shows that retained users engage with 5+ features per month while churned users used only 1-2. The team invests in guided feature discovery for new users.
Suggested decisions +
- If depth is low but retention is high, users may be satisfied with core features
- If depth is low and retention is low, users may not be discovering enough value