Definition
Onboarding completion rate is the % of new users who finish your onboarding milestones within a defined window.
Formula
Onboarding completion = completed / started
Example
If 400 users start onboarding and 260 finish, completion rate is 65%.
How to use it
- Use it as an early leading indicator for activation and retention.
- Measure completion time as well as completion rate (speed matters).
- Segment by persona and acquisition channel to improve onboarding flows.
- Tie completion to activation outcomes to confirm value.
- Track where users drop off to prioritize onboarding fixes.
Common mistakes
- Counting completion without verifying downstream activation.
- Changing milestones mid-period and breaking trend comparisons.
- Optimizing completion at the expense of time-to-value.
Measured as
Onboarding completion = completed / started
Misused when
- Counting completion without verifying downstream activation.
- Changing milestones mid-period and breaking trend comparisons.
- Optimizing completion at the expense of time-to-value.
Operator takeaway
- Use it as an early leading indicator for activation and retention.
- Measure completion time as well as completion rate (speed matters).
- Segment by persona and acquisition channel to improve onboarding flows.
- Keep Onboarding Completion Rate consistent by cohort, segment, and period before you use it as a decision signal in planning or reporting.
- Interpret the metric alongside retention, margin, or payback so one ratio does not hide the real operating trade-off.
Next decision
- Read PLG metrics hub: activation, trial conversion, stickiness, and adoption if the decision depends on interpretation, policy, or trade-offs beyond the raw formula.
- Decide whether Onboarding Completion Rate is a growth, retention, or efficiency signal before you set targets around it.
Where to use this on MetricKit
Guides
- PLG metrics hub: activation, trial conversion, stickiness, and adoption: A practical hub for product-led growth metrics: activation rate, trial-to-paid, DAU/MAU and WAU/MAU stickiness, feature adoption, and PQL-to-paid conversion.