The authentication success rate problem is mostly a measurement problem
Authentication teams keep asking the same question: which tool reports the real authentication success rate?
The uncomfortable answer is that most enterprise stacks already have several tools claiming to answer it, and they are all measuring different points in the same login journey. That is how one team sees 94%, the identity provider reports 98%, and support still says users are stuck. Those numbers can all be internally correct.
The issue is not the math. It is the measurement point.
At its simplest, authentication success rate is successful authentications divided by authentication attempts. But a tool can only report that honestly if it counts the attempt before the outcome is known, measures the login itself instead of the page around it, keeps method as a dimension, attaches failure reasons, and survives cross-device flows.
Why most analytics tools miss the decisive moment
Modern login flows often fail in places that page and server analytics never see.
A passkey login is the clearest example. The important part happens inside the browser's WebAuthn call, where the system prompt opens, the user interacts, and a result comes back. That whole ceremony is client-side. No page load is required. Often no request is sent either.
That creates a blind spot for common categories of authentication analytics:
| Tool category | What it sees well | What it usually misses |
|---|---|---|
| Web analytics | Traffic to login, post-login conversion | Failures with no click, render, or navigation |
| Product analytics | Funnels over defined events | Anything engineers never instrumented |
| Session replay | Friction in your own form UI | Browser and OS prompts outside the DOM |
| APM / RUM | Endpoint health, latency, server errors | User-side abandonment with no request |
| Identity provider | API-level auth outcomes and reason codes | Attempts that never reached the API |
| Authentication observability | The login ceremony itself | Marketing and broader page analytics |
This is why identity provider login metrics and frontend funnel numbers rarely line up.
The browser is where login truth gets lost
The hardest failures are the ones that vanish between page tracking and server logs.
Take a cancelled Face ID prompt. Your web analytics tool may never get an event. Your APM stack sees no backend request. Your IDP never receives the authentication attempt. From those systems' point of view, nothing happened.
But something absolutely happened to the user.
That is the gap authentication observability tries to fill. Instead of watching the page or the backend, it measures inside the login flow itself. For passkeys, that means WebAuthn ceremony measurement around the navigator.credentials call. For other methods, it means counting the actual password, OTP, social, or MFA step where success or abandonment occurs.
That measurement point matters for two reasons:
- it captures attempts before the outcome is known
- it makes client-side failures visible, including timeout, cancel, and drop-off behavior
It also makes NotAllowedError attribution possible in a more credible way. The browser does not tell you whether that error meant cancel, timeout, or no available passkey. You have to reconstruct it from timing, device capability, and what the user did next.
The real evaluation question is not features, it is measurement point
A lot of CIAM analytics buying decisions go sideways because vendors get compared by dashboard features instead of where they count attempts.
If you are evaluating tools for login funnel analytics, these five questions are more useful than a long feature list:
- Where exactly is the attempt counted?
- Can you show a cancelled passkey login as a row in a real report?
- How do you distinguish cancel, timeout, and no passkey for the same browser error?
- Can support inspect one user's full authentication path?
- What happens in cross-device login tracking when the journey starts on desktop and finishes on mobile?
Those questions expose whether a product measures authentication or just activity around authentication.
There is also a bigger product question hiding above the tool comparison: does adding a new method actually get more people in? In measured deployments, the gap in total authentication success rate between users who could have used a passkey and users who could not runs from 7 to 15 percentage points. That is why passkey analytics cannot stop at passkey-only success rates. You need method and credential availability on the same dataset.
Corbado is a passkey observability and adoption platform for large B2C enterprises.
Pick by measurement point, not by feature list
Every category reports a number that is true for the point it measures. The disagreement is the clue.
Web analytics can tell you how people reached login. APM can tell you whether the endpoint is healthy. Your IDP can tell you what reached its API. But if you want the real answer to whether authentication itself worked and why it failed, the measurement has to happen inside the login flow.
Read the full breakdown.
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