If you ship login, you ship a funnel. Users arrive with intent, choose a method, go through a flow that can fail in many ways, and either end up authenticated or leave. The fastest way to improve outcomes is to stop treating “login” as one black box and measure it like any other high-impact conversion journey.
Below is a practical overview of the core authentication KPIs that help teams diagnose reliability, reduce friction, increase passkey adoption, lower support load, and track real security impact. (I’ll keep this high-level on purpose. The full KPI library goes deeper into benchmarks, instrumentation patterns, and segmentation strategies.)
Different KPIs attach to different points. That matters because “success rate” can look fine while users churn earlier, or while passkeys fail and users fall back to passwords.
See our Authentication KPI Library here
Reliability KPIs: what breaks and where
Authentication Error Rate
Authentication Error Rate is the share of authentication attempts that end in a logged, explicit error (as opposed to silent drop-off). Think: invalid credentials, technical failures, user-cancelled, account locked, unsupported device.
Why it’s useful: explicit errors tell you what broke and are often the quickest path to fixes (bad validation, flaky dependencies, SDK incompatibilities, confusing prompts). It becomes especially powerful when segmented by platform, OS version, browser/WebView, and method.
Read the full article about the Authentication Error Rate here
Login Success Rate (from starting a method)
Login Success Rate measures whether a user who starts a specific method reaches an authenticated session. This is a method-level health metric: “Once someone commits to OTP/passkey/password, does it work end-to-end?”
It’s your early-warning system for regressions. A sudden drop often points to a release issue, backend validation change, third-party outage (SMS, email), or client-side logging bugs. The key is to define “start” as a real commitment action, not just showing the method.
Read the full article about the Login Success Rate here
Passkey Authentication Success Rate
Passkey Authentication Success Rate is the same idea, but specifically for passkeys: once a passkey flow starts, how often does it end with an authenticated session?
Passkeys fail differently than passwords: cancellations at the biometric prompt, cross-device friction, missing platform support, or inconsistent WebView behavior. Measuring passkeys separately prevents “fallback masking,” where overall login looks healthy while passkeys are quietly unreliable.
Read the full article about the Passkey Authentication Success Rate here
Friction & Speed KPIs: where users silently give up
Authentication Drop-Off Rate
Authentication Drop-Off Rate captures the share of attempts that start but never reach completion, even if no explicit error is recorded. This is often where conversion and retention leak: users close the tab, get distracted, hit confusing UX, or get stuck on delivery delays.
This KPI is most actionable when paired with step-level telemetry (method chooser, identifier entry, code entry, passkey prompt, recovery screens). It’s also sensitive to timer windows, so you want a consistent definition of when an attempt “times out.”
Read the full article about the Authentication Drop-Off Rate here
Time to Authenticate
Time to Authenticate measures elapsed time from starting authentication to reaching authenticated content. It includes user input, redirects, network time, and server verification. The median matters, but tails (like p95) are where frustration lives.
This metric is how you quantify “login feels slow.” It often correlates with drop-off, especially when the flow adds waiting (OTP delivery, magic link email, retries, or multiple prompts). Improvements usually come from reducing steps, improving defaults, tightening recovery UX, and addressing mobile performance issues.
Read the full article about Time to Authenticate here
Passkey adoption KPIs: enrollment versus real usage
Login Engagement Rate
Login Engagement Rate asks: when a login entry point is offered, how often do users actually start a login attempt? If it’s low, you might be showing login at the wrong time, confusing visitors, or over-counting “offers” due to re-renders.
This KPI is a good diagnostic for early funnel issues before credentials even enter the picture.
Read the full article about the Login Engagement Rate here
Login Conversion Rate
Login Conversion Rate measures how often users who are shown at least one method actually start any method. It’s highly sensitive to method chooser UX: unclear labels, too many options, poor ordering, and showing unusable methods on a given device or locale.
If you want a quick “is our chooser confusing?” signal, this is it.
Read the full article about the Login Conversion Rate here
Passkey Enrollment Rate
Passkey Enrollment Rate measures how often users who are offered a passkey creation opportunity actually complete enrollment. It’s the gateway metric for passkey adoption.
Enrollment depends heavily on timing and context. In many products, users do not go looking for “security settings,” so measuring enrollment based on a real offer event (not just eligibility) is critical. If enrollment is low, the issue is usually prompt timing, copy clarity, or platform-specific enrollment friction.
Read the full article about the Passkey Enrollment Rate here
Passkey Usage Rate
Passkey Usage Rate measures, among completed logins, how many are completed with a passkey. This is the “ROI metric” for passkeys because it reflects whether passkeys became the default, low-friction path in real behavior.
A common pattern: enrollment looks great, usage stays low. That usually means passkeys are not presented as the primary path, cross-device flows create friction, or one bad experience teaches users to choose fallback forever. Usage needs to be tracked separately from enrollment.
Read the full article about the Passkey Usage Rate here
Fallback & Recovery KPIs: fallback methods and account recovery flows
Password Reset Volume
Password Reset Volume measures completed password resets per active user over time (often normalized per user per year). It quantifies the ongoing tax of passwords: user frustration, deliverability costs, and support workload.
It also helps you validate whether changes actually reduce password dependence, rather than just shifting problems elsewhere.
Read the full article about the Password Reset Volume here
Business Impact KPIs: what is the business impact of passkeys?
Authentication Support Ticket Rate
Authentication Support Ticket Rate tracks what share of support tickets are driven by login and account access issues. Tickets lag behind UX problems, but they are a real cost signal and often correlate with spikes in errors, lockouts, delivery failures, or confusing recovery paths.
If your success rates look stable but tickets rise, you may have hidden friction or a messaging problem that metrics alone are not capturing.
Read the full article about the Authentication Support Ticket Rate here
Account Takeover Rate
Account Takeover Rate measures confirmed compromised accounts relative to active accounts in a period. This is a “real harm” metric, not an attempts metric.
It’s also easy to distort if definitions drift. You need consistent confirmation criteria and awareness that takeovers are often confirmed later than the login event. Still, it’s the cleanest way to tie authentication quality to fraud loss, support burden, and user trust.
Read the full article about the Account Takeover Rate here
Total Authentication Success Rate
Total Authentication Success Rate aggregates all methods into one number: out of started authentication attempts, how many end in an authenticated state? It answers the executive-level question: “Can users get in?”
But it’s only useful if you pair it with the method-level and funnel-stage KPIs above. Otherwise, method mix shifts and fallback masking can hide real issues.
Read the full article about the Account Takeover Rate here
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