Login Success Rate

Login Success Rate (from starting a method) measures how often a user who starts a specific login method ultimately reaches the intended authenticated state. It tells us whether the method works end to end once a user commits to it, which makes it a strong signal of reliability and usability inside the method flow.

We measure Login Success Rate from the moment a user starts a specific method to the moment we create an authenticated session for that same attempt. Measurement boundary: start at the first server or client event that confirms a method attempt began, end at the first event that confirms an authenticated session was issued.

flowchart LR
    OM((Offer
Method))
    SM((Start
Method))
    CH((Method
Steps))
    SA((Authenticated
Session))

    OM --> SM --> CH --> SA

    SM -. "LOGIN SUCCESS RATE" .-> SA

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We calculate Login Success Rate per method attempt. We count an attempt once, at the moment the method is started. If a user retries, that creates a new attempt.

Logins Succeeded after Method Start is the number of method attempts that end in an authenticated session. Method Attempts Started is the number of method attempts that actually began.

Count an attempt in the numerator when we log a definitive success event like Auth Session Issued that is linked to the same attempt id as the Method Attempt Started event.

Do not count attempts that only reach an intermediate state, like code sent, challenge shown or device enrolled, if an authenticated session is not issued for that attempt.

Count an attempt in the denominator when we log Method Attempt Started for a specific method. This should happen when the user has taken an action that commits them to the method flow, not when the method is merely displayed.

Do not count passive impressions, preselected methods with no user action, or attempts missing an attempt id that prevents joining start to outcome.

We use Login Success Rate (from starting a method) to find where method flows fail after commitment, then remove friction or fix reliability issues.

We can improve the following business outcomes:

• Higher successful sign ins that reach the intended authenticated state
  • Diagnose: drops concentrated in one method or one platform
  • Change: fix client regressions, server validation bugs, or third party outages impacting that method
  • Validate: the KPI recovers for the impacted segment without an increase in retries per user
• Lower user drop off during authentication
  • Diagnose: failures cluster at specific step types like code entry or push approval
  • Change: reduce step count, improve message delivery speed, tighten error handling and recovery screens
  • Validate: improved KPI plus fewer abandoned attempts for the same cohort
• Lower support contacts caused by authentication issues
  • Diagnose: KPI declines aligned with increases in user reported lockouts or non delivery
  • Change: improve deliverability controls, clearer user messaging, safer rate limits, better self serve recovery inside the method
  • Validate: KPI improves and support contact rate falls for affected categories
• Lower fraud and abuse exposure that comes from weak or bypassed authentication
  • Diagnose: KPI rises because challenges are skipped, not because users succeed legitimately
  • Change: enforce step integrity, harden decisioning, verify bindings between attempt and session issuance
  • Validate: KPI stays healthy while fraud signals and chargeback like outcomes do not worsen

• Intent and selection bias: users who start a method are already a filtered group. If we change which users reach the start event, the KPI can move even if the method did not.
• Missing telemetry or inconsistent logging: if Method Attempt Started fires on some platforms but not others, or if success events are not linked by attempt id, the KPI becomes inflated or deflated in ways that look like product change.
• Mix shifts across segments: a shift toward easier devices, better networks, lower risk users, or a different method mix can increase the KPI while some segments degrade.
• Duplicate success events: if Auth Session Issued is emitted more than once per attempt, the numerator can exceed reality unless we dedupe by attempt id.

Break down by method, platform, app version, and region first. Then add network type, risk tier and new vs returning users. Always chart volume alongside the KPI to catch mix shifts and logging changes.