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AI Product Tools  /  MIF Explorer  /  Library  /  UX

Truth Layer

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.

KPI UX DirectionalLeadingVanity Risk

Onboarding Completion Rate

The percentage of new users who complete all required onboarding steps.

Category: Adoption
Measurement class: KPI

Measurement Class

A measurement class tells you what kind of measure something is, not just what topic it covers.

Why it matters: It stops teams from building a stack full of only KPIs while ignoring value, governance, or AI signals.

Example: Governance Metric and AI Signal are two different measurement classes.

Frequency: Weekly
Back to library

Evaluation method

users_completing_all_steps / users_starting_onboarding × 100

Signal type

leading

What it is best for

Identifying which onboarding steps cause the most drop-off

What it tells you +

Whether the onboarding sequence is appropriately scoped and friction-free.

What it does not tell you +

Guarantee that users who complete onboarding actually understand or value the product.

When to use it +
  • Identifying which onboarding steps cause the most drop-off
  • Comparing onboarding variants in A/B tests
When not to use it +
  • As a standalone success metric without pairing it with activation or retention
  • When onboarding is optional or users can skip it entirely
How leaders misuse it +
  • Shortening onboarding to inflate completion rates without checking downstream impact
  • Treating 100% completion as a goal rather than a signal
Anti-patterns +
  • Removing important setup steps just to improve the completion number
AI interpretation risks +

Scenario: AI assistant guides users through each onboarding step

What happens: Completion rate increases because the AI resolves confusion in real time

What it really means: Users completed onboarding but may not retain the knowledge needed to use the product independently.

Recommendation: Track unassisted task success after AI-guided onboarding to verify knowledge transfer.

Companion entries +

This entry is stronger when paired with:

Instrumentation or evaluation guidance +

Track per-step drop-off to identify the exact friction points. Consider whether all steps are truly necessary.

Sample events

onboarding_step_1, onboarding_step_2, onboarding_complete
Examples +

A B2B tool has 71% onboarding completion. Step 3 (data connection) has a 40% drop-off. Adding a CSV upload alternative raises overall completion to 84%.

Suggested decisions +
  • If below 60%, examine per-step drop-off for the biggest friction point
  • If above 90% but activation is low, onboarding may be too shallow