Authentication Error Rate
Authentication Analytics Whitepaper
Practical guidance, rollout patterns and KPIs for passkey programs.
What is the Authentication Error Rate?
Authentication Error Rate is the percentage of authentication attempts that end with an explicit error outcome that we can log and classify. It matters because explicit errors tell us what broke, where it broke and which fixes will reduce user friction and support load.
Key facts on Authentication Error Rate
- What it captures: The share of attempts that end with an error message or error state
- Primary use: Diagnose reliability and UX failures by error type, platform and method
- Interpretation: Lower is better, many teams target under 5% overall and 2% to 5% is common when passkeys are widely used
Where does the Authentication Error Rate fit in the login funnel?
We measure Authentication Error Rate from the moment an authentication attempt starts to the moment it ends in either a success or an explicit error. We only count outcomes that produce an explicit error event and we exclude silent abandonment where no error is emitted.
Analyze your Authentication Performance with Real Numbers
10 KPIs that connect authentication performance to revenue. Track adoption, friction & conversion impact.
How to calculate the Authentication Error Rate?
We calculate Authentication Error Rate per authentication attempt. We count an attempt once, even if it includes retries inside the same flow, as long as it maps to a single start and a single terminal outcome.
Authentication Error Rate = Auth Attempts with Explicit Errors
Total Auth Attempts
Typical ranges vary by method mix and platform coverage.
| Level | Typical range |
|---|---|
| Industry average | 10% to 15% |
| Top performers | Under 5% |
| With strong passkey adoption | 2% to 5% |
Numerator: Auth Attempts with Explicit Errors
Counts when we record a terminal error event for the attempt, such as invalid credentials, timeout, browser incompatibility, user cancelled, account locked, suspended or not found. Does not count silent exits where we never receive an error event.
Denominator: Total Auth Attempts
Counts each authentication attempt that started within the measurement window. Does not include background token refreshes or already authenticated session checks, unless we explicitly model those as user facing authentication attempts.
Errors often cluster on specific platforms or implementations. Social logins and passkeys can fail on certain Android or iOS versions due to SDK incompatibilities, WebView limitations or missing OS-level support.
How to use Authentication Error Rate to improve outcomes
We use Authentication Error Rate to prioritize fixes that reduce explicit failures that block sign in. We can achieve the following business outcomes:
- Higher successful sign-ins
- Diagnose which error category drives most failures
- Change method ordering, add passkeys where supported and improve fallback design
- Validate by a sustained error reduction and stable completion volume
Blindspots and common pitfalls of Authentication Error Rate
- Intent and selection bias: attempts include bots, fraud and confused users, so the rate can rise even if real users are fine
- Missing telemetry: if error events fail to log on crashes or network loss, explicit errors look better while users still fail
- Inconsistent logging: different clients may map the same issue to different error types, which breaks trend accuracy
Authentication Analytics Whitepaper. Practical guidance, rollout patterns and KPIs for passkey programs.