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.

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.

flowchart LR
    SA((Start<br/>Auth Attempt))
    OM((Offer<br/>Method))
    SM((Start<br/>Method))
    SU((Success<br/>Auth))
    ER((Explicit<br/>Error))

    SA --> OM --> SM
    SM --> SU
    SM --> ER

    SA -. "AUTH ERROR RATE" .-> ER

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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.

Auth Attempts with Explicit Errors are attempts that emit an explicit terminal error event such as invalid credentials, technical failure, user cancellation or account issue. Total Auth Attempts are all attempts that started within the measurement window.

Typical ranges vary by method mix and platform coverage.

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.

Counts each auth_attempt_started event once. Does not include background token refreshes or already authenticated session checks, unless we explicitly model those as user facing authentication attempts.

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
• Lower user friction
  • Diagnose spikes in user cancelled errors on specific screens
  • Change prompt copy, add clear choices and avoid surprise biometric prompts
  • Validate with lower cancellations and fewer repeat attempts per user
• Lower support ticket volume
  • Diagnose account locked and account not found rates
  • Change recovery guidance and self serve unlock flows
  • Validate with fewer error tagged support tickets and fewer repeated lockouts

• 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
• Mix shifts across segments: adding passkeys, changing method order or expanding to new devices can move the aggregate without real quality change

Keep a simple dashboard with overall Authentication Error Rate, plus breakdowns by method, error category, OS and browser, app version and new versus returning users. Add alerts for sudden increases in any segment over 10% so we catch compatibility regressions quickly.