Passkey Authentication Success Rate

Passkey Authentication Success Rate measures how often passkey sign-ins succeed once a user starts a passkey flow. We use it to validate that passkeys work reliably in real conditions, across devices, platforms and user behavior.

We start counting when the user initiates a passkey flow and the client requests a passkey credential. We stop counting when the session reaches the intended authenticated state or the user exits the passkey flow. Measurement boundary: we only evaluate sessions where a passkey attempt was started.

flowchart LR
  LP((Login page))
  SP((Start passkey authentication))
  UP((User verification: biometric or PIN))
  SV((Server verification))
  AS((Authenticated session))
  EX((Exit or cancel))

  LP --> SP --> UP --> SV --> AS
  UP -. cancel .-> EX
  SV -. error .-> EX

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We calculate Passkey Authentication Success Rate at the login session level. We count at most one started attempt and one outcome per session, even if the user retries within the same session.

Passkey Auth Attempts started counts sessions where we invoked the passkey credential request. Passkey Auth Attempts Succeeded counts sessions where server verification completed and we issued an authenticated session.

Count a success when the user completes user verification and the server verifies the response, then the user lands in the authenticated state.

Do not count sessions that end with user cancellation, timeouts, unsupported device errors or a switch to a different sign in method after starting passkeys.

Count a start when the user action and client code actually invoke the passkey flow, not when we merely render a button or suggestion.

Do not count sessions where passkeys were never offered due to capability checks or where the user chose another method without starting the passkey flow.

We use Passkey Authentication Success Rate to target the specific step where passkeys break, then fix that step and confirm the gain holds across segments.

We can improve the following business outcomes:

• Higher successful sign ins: If the KPI drops, we diagnose whether failures cluster in user cancellations or technical errors, we improve copy and capability checks, we validate via a stable lift in success across the same platforms.
• Lower user drop off during authentication: If cancellations rise, we diagnose unclear prompts or account selection confusion, we add clearer account naming and guidance, we validate by fewer cancels with unchanged traffic mix.
• Lower support contacts: If certain devices fail more, we diagnose OS or browser specific issues, we patch compatibility and error messaging, we validate with fewer repeated failures for the same device families.
• Better operational cost: If retries spike, we diagnose timeouts and flaky verification, we tune timeouts and retry handling, we validate with fewer repeated passkey starts per session and a higher success rate.

• Intent and selection bias: Early adopters and power users may use passkeys first, which can inflate results compared to broad rollout.
• Missing telemetry: If we fail to log passkey_start when the system UI opens, the denominator shrinks and the KPI looks artificially high.
• Inconsistent logging: If one platform logs cancel as failure and another drops the event, cross platform comparisons become misleading.
• Mix shifts: A change in traffic toward web cross device flows or older devices can lower the KPI even if nothing regressed.
• Fallback masking: If users start passkeys, fail, then succeed via another method, overall login outcomes look fine while passkey reliability is poor.

Report Passkey Authentication Success Rate by platform, OS version, browser, and flow type. Always split failures into user cancelled versus technical error, and keep a separate view for new users versus returning users.