The problem: three teams, zero shared view
Authentication sits at the intersection of security (threat monitoring), product (conversion funnels), and identity infrastructure. Each team has partial visibility. Product sees "login page viewed" but not why users abandon. Security sees attack patterns but not how strict policies block legitimate customers. Identity teams log error codes that mean nothing to the CFO asking why support tickets spiked 40%.
The trade-offs are real. A CTO at a B2B SaaS firm recently told us their MFA rollout looked successful in security dashboards while conversion dropped 12% because SMS codes weren't arriving on corporate networks. They only caught it after two weeks of support escalations.
What actually matters: the core metrics
Practical measurement starts with five buckets:
- Reliability: Authentication Success Rate (successful logins / total attempts). For passkey implementations, track Passkey Authentication Success Rate separately.
- Friction: Authentication Drop-Off Rate ((funnel starts - completions) / starts) and Time to Authenticate (completion timestamp - start timestamp).
- Adoption: Passkey Enrollment Rate vs. Passkey Usage Rate. Enrollment means nothing if users revert to passwords.
- Recovery cost: Password Reset Volume (resets / active users over time period) and Authentication Support Ticket Rate.
- Risk: Account Takeover Rate where relevant (compromised accounts / active accounts × 10,000).
Notably, mobile vs. web drop-off patterns differ significantly. Mobile users hitting biometric failures or app switcher friction show 15-30% higher abandonment than desktop password managers in recent vendor benchmarks, though industry-standard drop-off rates remain elusive.
The data stitching problem
You need three sources:
- IdP logs: The authoritative backend record of attempts, challenges, and provider-specific errors.
- Frontend analytics: Client-side signals like "login button clicked" that never reach the server when JavaScript fails or networks drop.
- Observability tools: Latency distributions and anomaly detection.
The hard part isn't dashboards. It's normalizing events across platforms where the same credential-manager conflict appears as five different error codes. A common event schema looks like:
auth_viewed → auth_method_selected → auth_attempt → auth_challenge_served → auth_challenge_completed → auth_success | auth_failure
Separating "viewed" from "attempted" catches silent drop-offs that backend logs miss entirely.
Why no standard tool exists
Authentication analytics remains fragmented because OS and browser updates constantly break flows in new ways. The same passkey implementation that works on Chrome 120 fails silently on Safari 17.2. Academic research flagged this years ago: early authentication metrics were too easily manipulated (key compromises) and ignored user consent patterns.
The practical reality: authentication analytics is an ongoing discipline, not a one-time dashboard project. Teams that ship it successfully treat event taxonomy and error classification as living documents, updated each quarter as platforms evolve.