Authentication Support Ticket Rate

Authentication Support Ticket Rate measures how much of our helpdesk volume is driven by login and authentication problems. It captures the operational burden of authentication friction, because every ticket represents a user who could not self serve their way back into an authenticated state.

Authentication Support Ticket Rate sits after a user fails to complete authentication and chooses to escalate to support instead of retrying or using self service recovery. Measurement boundary: we start when a support ticket is created and end when that ticket is classified for reporting, using the first stable category applied within our chosen window.

flowchart LR
    OA((Offer Auth))
    SA((Start Auth))
    SuA((Success Auth))
    FA((Fail Auth))
    ST((Create Support Ticket))

    OA --> SA --> SuA
    SA --> FA --> ST

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We calculate Authentication Support Ticket Rate on unique tickets, counted once per ticket identifier, within a fixed reporting period such as a week or month. We do not count replies, reopens or internal notes as additional tickets.

• Auth Related Tickets are tickets tagged as authentication or login issues within the period.
• Total Support Tickets are all valid support tickets created in the same period.

Typical ranges vary by product and user base, but these bands are often useful for triage.

Counts tickets where the primary user problem is account access, for example password reset help, lockouts, MFA code problems, authenticator app issues, confusing login errors or account recovery after device loss. Exclude tickets where authentication is only mentioned in passing and the core issue is billing, product usage or general troubleshooting.

Counts all inbound customer or employee support tickets created in the period. Exclude spam, duplicates created by tooling and test tickets. Keep inclusion rules stable over time so trend changes reflect reality, not process drift.

We use Authentication Support Tickets to target the highest cost sources of access friction and validate that changes reduce real support load.

We can improve the following business outcomes:

• Lower support cost: If the KPI rises, we diagnose which auth category is growing and whether self service completion dropped. We improve recovery flows and reduce preventable lockouts. We validate with a sustained drop in ticket share and absolute volume.
• Lower user drop off during sign in: If the KPI rises after a release, we diagnose error messages and client or server failures by platform. We fix UX, reliability, and messaging. We validate by seeing fewer tickets tied to the same error reasons.
• Reduce MFA driven frustration: If tickets cluster around codes and device changes, we diagnose challenge delivery and enrollment flows. We add more robust methods such as passkeys and clearer fallback. We validate by a reduced MFA ticket fraction.
• Reduce repeat contacts: If the same users open multiple tickets, we diagnose recovery loops and unclear next steps. We tighten resolution playbooks and in product guidance. We validate by fewer repeat ticketers per month.

• Intent and selection bias: Many users who fail authentication never contact support, they abandon. The KPI can look good while access is still broken.
• Missing telemetry and inconsistent logging: If tagging is manual or optional, classification quality changes the KPI more than the product does.
• Mix shift across segments: A shift toward more new users, more mobile users, or a new region can raise tickets without any auth regression.
• Process changes: Adding chat, deflecting to a bot, or changing routing can move volume between channels and distort comparisons.

At minimum, report Authentication Support Tickets by ticket type, auth method, passkey enrolled yes or no, platform, browser or app version, and top error reasons captured in the ticket form or logs.