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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.
Day 30 Retention
The percentage of users who return to the product on day 30 after their first use.
Evaluation method
users_active_on_day_30 / users_who_signed_up_on_day_0 × 100
Signal type
lagging
What it is best for
Evaluating product-market fit
What it tells you +
Whether the product delivers sustained value beyond the initial excitement period.
What it does not tell you +
Explain why users left or what would bring them back.
When to use it +
- Evaluating product-market fit
- Understanding long-term value delivery
- Measuring impact of major product improvements across cohorts
When not to use it +
- For products with naturally longer or shorter use cycles
How leaders misuse it +
- Using D30 retention alone without D7 retention for context on where drop-off happens
Anti-patterns +
- Relying on win-back campaigns to inflate the number rather than improving the product
Companion entries +
This entry is stronger when paired with:
Instrumentation or evaluation guidance +
Cohort-based measurement essential. Aggregate numbers mask important trends.
Sample events
session_start Examples +
A SaaS platform has D7 retention of 35% but D30 retention of 8%. The steep drop between week 1 and month 1 reveals the product runs out of value after initial exploration.
Suggested decisions +
- Below 10%: severe retention issue. Product is not delivering sustained value.
- Between 10-25%: investigate the gap between D7 and D30 to find the value drop-off point.
- Above 25%: healthy retention. Focus on deepening engagement and expanding use cases.