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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.
Funnel Drop-off Rate
The percentage of users who leave a multi-step process at each specific step.
Evaluation method
users_exiting_at_step / users_entering_step × 100
Signal type
leading
What it is best for
Diagnosing conversion bottlenecks
What it tells you +
Exactly where users give up. The most actionable conversion diagnostic.
What it does not tell you +
Tell you why users left at that step. Requires qualitative investigation.
When to use it +
- Diagnosing conversion bottlenecks
- Prioritizing UX improvements by impact
- A/B testing specific funnel steps
When not to use it +
- When funnel steps are not clearly defined or instrumented
How leaders misuse it +
- Focusing only on the highest drop-off step without considering volume at each step
Anti-patterns +
- Removing steps to reduce drop-off without checking if the information was needed
Companion entries +
This entry is stronger when paired with:
Instrumentation or evaluation guidance +
Track per-step, not just overall. Include time-at-step to distinguish quick exits from deliberate abandonment.
Sample events
funnel_step_1_entered, funnel_step_1_exited, funnel_step_2_entered Examples +
A SaaS signup funnel loses 55% of users at the "connect your data source" step. Adding a skip option with sample data reduces drop-off to 28%.
Suggested decisions +
- Step with >40% drop-off: investigate immediately with session replay or user testing
- If drop-off is high but conversion quality is good, the step may be filtering correctly