Which Tool reports the real Authentication Success Rate?

1. Which tool reports the real authentication success rate?#

Your analytics dashboard and identity provider may both report login success rate while counting different things. One may use visits to the login page. Another may count only requests that reached the server. Neither definition automatically answers how many people started a login journey and completed it.

The right comparison begins with your measurement requirement. This guide explains six tool categories, the questions they typically support and the evidence to request before buying another product.

2. Define the login journey before comparing tools#

Choose a start that reflects the metric you want: a page impression, an engaged journey or a method attempt. Record success at the intended authenticated state. Keep retries and unfinished journeys consistent with the observation window.

For example, a failed passkey attempt followed by password success is a failed method attempt inside a successful journey. An automatic prompt is also different from an explicitly initiated request.

The diagram uses passkeys as an example. It separates the application-visible request from the internal browser interface and the backend verification step. Password and OTP flows have different steps; the same definition discipline applies.

A tool can report events supplied by your application without being able to observe every internal platform action. Ask how those events are collected and what remains unknown.

3. Compare six analytics and observability categories#

These are practical groupings, not exclusive product boundaries. Many vendors span more than one category. APM means application performance monitoring; RUM means real user monitoring.

3.1 Web analytics#

Adobe Analytics and Google Analytics 4 connect login activity to acquisition and the wider website journey. They can report explicitly collected authentication events, including browser-side outcomes. The work is defining those signals, mapping them and maintaining the implementation.

See the practical Adobe error-tracking setup or GA4 login-tracking guide for the relevant collection path.

3.2 Product analytics#

Amplitude and Mixpanel support analysis of recorded product events, funnels and cohorts. A well-instrumented login can be analysed alongside activation and retention. Evaluate how method changes, retries and incomplete journeys enter that event model.

3.3 Digital experience analytics and session replay#

Quantum Metric, Glassbox, Contentsquare and FullStory help investigate interactions with the product interface. DOM-based replay can show the application's form and rendered errors. It does not itself record an operating-system biometric sheet or browser-owned passkey dropdown. Separate custom events may provide additional evidence.

3.4 Application performance monitoring and real user monitoring#

Datadog, New Relic and Sentry can provide application errors, performance measurements and browser telemetry. Their coverage depends on the installed SDKs and configuration. A handled authentication error can be reported explicitly; it need not be an unhandled exception.

3.5 Identity providers and CIAM#

Customer identity and access management (CIAM) supports authentication for a product's customers. Providers such as Auth0, Okta and Microsoft Entra ID expose information about authentication requests and decisions. Evaluate the specific product's frontend telemetry and reporting capabilities as well as its server logs.

3.6 Authentication observability#

Authentication observability organises available signals around login journeys: method attempts, retries, fallback and outcomes. Corbado Observe belongs here. The evaluation should focus on supported collection, the journey model and the diagnostic workflow your team needs.

4. Compare the implementation work#

Avoid a binary matrix that labels whole categories “can” or “cannot”. Use the same test journey in each shortlisted product.

Browser-side WebAuthn outcomes can be sent to a general analytics platform. A dedicated product's value is the collection and analysis work it supplies, rather than exclusive access to an error code.

5. Five questions to ask an analytics vendor#

1. Where do you count an attempt, and how do you handle automatic prompts?
2. Can you show a failed method followed by a successful fallback in one journey?
3. Which failure causes are directly observed and which are inferred?
4. What can support learn about one affected journey without asking the user to reproduce it?
5. In a cross-device passkey flow, which events are recorded on the initiating device and which steps remain hidden?

Include a web example and a native-app example if both matter to your product. Do not assume their APIs expose identical signals.

6. Measure the effect on the whole login journey#

Start by reconciling definitions and available frontend/backend evidence. A discrepancy can come from mismatched populations, collection failure or different success boundaries.

Then compare total journey completion and duration alongside method-level results. A high passkey success rate does not by itself show how much the whole product improved. Cohorts with and without passkeys may differ in other ways; use an appropriate experiment or account for selection effects before claiming causation.

The authentication analytics playbook provides broader metric definitions and reporting guidance.

7. Where Corbado Observe fits#

Some customers retain Adobe Analytics and add Observe for detailed authentication analysis. Observe runs alongside the existing identity stack and sends pseudonymous telemetry. The purpose is to make login journeys easier to investigate across methods, environments and fallback paths.

See Login Methods in Corbado Observe →

Bring a concrete investigation to a demo: repeated OTP requests, failed passkey attempts on one browser or a support case involving fallback. Compare the evidence and workflow with your current tools. Keep platform limits explicit, including ambiguous errors and browser-owned interfaces.

Explore Corbado Observe.

About Corbado

Corbado is the Passkey Intelligence Platform for large-scale CIAM teams running consumer authentication. We help you see what IDP logs and generic analytics tools can't: where passkeys, passwords, OTP, social login and fallback journeys succeed, stall or fail, which devices and browsers create friction, and when an OS update silently breaks login. Two products: Corbado Observe layers process mining and observability across authentication journeys. Corbado Connect adds managed passkeys with analytics built in alongside your IDP. VicRoads runs passkeys for 5M+ users with Corbado (+80% passkey activation). Talk to a Passkey Expert →

Frequently Asked Questions#

Which tools can report authentication success rate?#

Several categories can report it when they receive suitable events. Check how each implementation defines attempts, verified success, retries and unfinished journeys before comparing percentages.

Why do analytics and identity-provider numbers differ?#

They may measure different stages or populations. Check collection coverage, correlation, time windows and definitions before interpreting the difference as a login problem.

Can session replay show a biometric prompt?#

A DOM-based replay does not itself record browser or operating-system interfaces. Separate application events can provide context about an authentication request's outcome.

Can a tool identify the exact cause of every passkey error?#

No. Some platform errors are deliberately ambiguous. Context can support investigation, but inferred causes should remain distinguishable from directly observed results.

Does Observe replace my existing analytics platform?#

Observe can complement an existing analytics and identity stack with a dedicated authentication journey model and diagnostic workflow. Evaluate the additional coverage against your team's actual questions.