Total Authentication Success Rate

Total Authentication Success Rate (TASR) measures the share of authentication attempts that end with the user reaching the intended authenticated state, regardless of which authentication method was used. It aggregates all login attempts across all methods, whether passkeys, passwords, OTPs, magic links, or any other method. It is the fastest way to answer, at scale, whether users can actually get into the product, and whether the authentication layer is helping or blocking growth.

We measure Total Authentication Success Rate from the moment a user clearly signals intent to authenticate, for example they submit an identifier or tap Sign in until they either reach an authenticated session or they fail or abandon. Measurement boundary, we start at the first attempt start event, and we end at either a success event or an attempt timeout that we classify as abandonment.

The funnel applies to any authentication method. Whether the user authenticates with a passkey, enters a password, receives an OTP, or uses a magic link, the flow is the same: offer, start, complete on client, verify on server, succeed. The Total Authentication Success Rate aggregates all these method paths into a single metric.

flowchart LR
    OA(("Offer<br/>Auth"))
    OM(("Offer<br/>Method"))
    SM(("Start<br/>Method"))
    CM(("Complete<br/>Method<br/><i>client</i>"))
    VM(("Verify<br/>Method<br/><i>server</i>"))
    SuM(("Success<br/>Method"))
    SuA(("Success<br/>Auth"))

    OA --> OM --> SM --> CM --> VM --> SuM --> SuA

    OA -. "ENGAGEMENT RATE" .-> SM
    OM -. "METHOD CONVERSION" .-> SM
    SM -. "COMPLETION RATE" .-> CM
    CM -. "VERIFICATION RATE" .-> VM
    SM -. "METHOD SUCCESS RATE" .-> SuM
    SM -. "AUTH SUCCESS RATE" .-> SuA

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We calculate Total Authentication Success Rate per attempt, where we count one attempt once per session, even if the user retries multiple times or switches methods before succeeding. If a user starts with a password, fails, then succeeds with a passkey, that counts as one started attempt and one successful attempt. All methods contribute to the same aggregate metric.

• Auth Attempts started is the count of deduped sessions that entered the authentication flow, regardless of method.
• Auth Attempts succeeded is the count of those sessions that reached the authenticated state within the attempt window, regardless of which method ultimately succeeded.

Typical ranges vary by audience and method mix.

Count an attempt as succeeded when the user reaches the intended authenticated session, including completion of any required step up such as MFA. The method used does not matter for this metric, a success via passkey, password, OTP, or magic link all count equally. If a user tries one method, fails, then switches to another method and succeeds, the session is still one success.

Do not count sessions that only verify an email, issue a reset link or pass an intermediate checkpoint without granting authenticated access.

Count an attempt when the user enters the auth flow with clear intent, such as submitting an identifier, launching a passkey prompt, or submitting credentials. All method entry points count: password form submissions, passkey ceremonies, OTP requests, or magic link clicks.

Exclude known automated testing, health checks and detected bot flows. Deduplicate rapid retries within a short window so a single struggling session does not inflate the denominator.

Use Total Authentication Success Rate to connect authentication quality directly to growth, cost and risk outcomes.

• More successful sign ins: Diagnose which segment drops first, then simplify the highest volume path, then validate via sustained lift by segment for at least a full business cycle.
• Fewer user abandonments: Diagnose long time to complete or confusing prompts, then reduce steps and clarify recovery, then validate via higher success with stable attempt volume.
• Lower support contacts: Diagnose spikes in lockouts, resets, and error messages, then improve recovery UX and reduce false lockouts, then validate via fewer contact reasons tied to access.
• Lower fraud and abuse exposure: Diagnose high failure with high risk enforcement, then move to phishing resistant methods and better risk signals, then validate via fewer compromised accounts with stable success.

• Intent and selection bias: If you start counting only after a user chooses a method, you can miss users who bounce earlier and you will overstate success.
• Missing telemetry: If abandonments are not logged consistently, TASR can look healthy while users silently give up.
• Method mix shifts: A shift in which methods users attempt can change TASR even if no single method regressed. For example, more users trying passkeys on unsupported devices will lower the aggregate rate.
• Mix shifts across segments: A shift toward harder cohorts, such as new devices or new geographies, can drop TASR even if nothing broke.
• Retries counted as new attempts: If every retry is counted, TASR can fall due to counting artifacts rather than real access problems.