Authentication Analytics: Metrics & Login Funnels

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Authentication is the front door to your digital business, shaping UX and security risk. Yet setting up auth analytics requires weeks of engineering and teams still struggle to distinguish informational errors from actual failures.

Authentication analytics reveals why users succeed or fail at logging in. Standard product analytics miss these signals. This discipline tracks authentication metrics (e.g. login success rate, error distribution, time-to-login) and moves organizations from reacting to outages to proactively optimizing the login journey.

Three stakeholder teams observe login events through different lenses, creating misaligned incentives and fragmented data strategies:

Authentication analytics fills these gaps: Security teams see when policies spike abandonment, while product teams can distinguish UX issues from system failures and identity teams get the ROI data to justify new authentication investments.

The true cost of a suboptimal authentication stack is often invisible to standard financial reporting because it is distributed across multiple P&L centers - marketing efficiency, support operations and fraud loss.

Authentication friction directly impacts revenue. For detailed e-commerce authentication analysis see our State of E-commerce Authentication report.

Authentication-related tickets (e.g. "forgot password", "account locked", "didn't receive 2FA code") dominate support queues. Forrester Research estimates $70-150 per password reset incident.

Aggressive fraud detection (e.g. via IP reputation, device fingerprinting) creates false positives that "insult the customer". Blocked legitimate users who churn to competitors. In risk-based authentication, scenarios CAPTCHAs might be necessary that degrade UX.

In the following, you find an overview of the most important authentication metrics to keep track of:

A unified view of authentication requires the ingestion and correlation of data from three distinct layers of the technology stack.

flowchart TB
    subgraph Frontend["Frontend Layer"]
        Analytics["Product Analytics (GA4, Mixpanel)"]
    end

    subgraph Backend["Backend Layer"]
        IdP["Identity Provider (Auth0, Okta, Cognito)"]
        Logs["Application Logs"]
    end

    subgraph Observability["Observability Layer"]
        APM["APM (Datadog, Sentry)"]
        SIEM["SIEM (Splunk)"]
    end

    subgraph AuthAnalytics["Authentication Analytics Platform"]
        Ingest["Event Ingestion"]
        Process["Processing & Classification"]
        Dashboard["Dashboards & Alerts"]
    end

    IdP --> Ingest
    Logs --> Ingest
    APM --> Ingest

    Ingest --> Process
    Process --> Dashboard

The Identity Provider (IdP) is the authoritative source for authentication transaction data.

Most IdPs enrich logs with technical information like client_id, user_agent and ip for segmentation.

Product analytics track frontend user intent before the IdP is contacted:

1. login_page_viewed → 2. login_button_clicked → 3. auth_attempt (IdP)

Intent Gap Analysis: If 1,000 button clicks yield only 800 auth attempts, 20% fail due to client-side errors before reaching the server.

To extract meaning from these fragmented sources, organizations need a unified data model (a standardized event schema that normalizes data across providers and platforms).

flowchart LR
    A[auth_viewed] --> B[auth_method_selected]
    B --> C[auth_attempt]
    C --> D[auth_challenge_served]
    D --> E[auth_challenge_completed]
    E --> |Success| F[auth_success]
    E --> |Failure| G[auth_failure]

Identity resolution works differently depending on when you can identify the user:

When users provide an identifier (e.g. email, username) before authenticating, you can look up their credential history, registered devices and past behavior before showing any specific authentication options. This enables intelligent decisions about which method to offer.

For flows where authentication starts immediately (e.g. passkey autofill / Conditional UI), you must initiate the authentication challenge before knowing anything about the user. You only know the browser and version. Only once the flow completes can you connect the session to a specific user record.

This limitation shapes how authentication analytics work. Many metrics require working backwards from known outcomes. If you know a login completed via a specific method, you can calculate what the user likely experienced earlier even if you couldn't observe it directly. This "backwards calculation" approach accounts for ~70% of how authentication funnels are computed.

We suggest using a privacy-first identifier model when building authentication analytics. Three values / identifiers are important:

Session ID: Generated client-side on page load. Ties together all events from page load through authentication completion. Enables tracking the full journey even before you know who the user is.

Device Hint: After a user successfully authenticates on a device, you can store a hint (typically via cookie or local storage) indicating this device likely has valid credentials. On return visits, this hint enables showing optimized UI, e.g. a 1-tap passkey button instead of an email field. Note: These hints rely on client state that can be cleared, so they're probabilistic - not guaranteed.

User ID (UUID): A technical identifier that links to your user database without exposing PII. You don't need email addresses or usernames in your analytics data, a UUID is sufficient to identify users across sessions and devices. This dramatically simplifies privacy compliance and InfoSec reviews, since no personal data flows to the analytics system. The connection to actual user records stays in your backend.

To drive action, data must be depicted in dashboards tailored to the specific needs of different stakeholders.

C-Suite stakeholders need a "Health Monitor" correlating auth performance with revenue/risk.

Product Managers need to diagnose friction.

Security teams need to mointor in real-time for threats.

With the KPIs and dashboards from above, teams measure, gain insights and translate them into actions. Here are three use cases that drive value.

Why isn't there a tool for authentication analytics like there is for product analytics?

Often identity architects hear from IDP vendors that detailed telemetry data is not available in a way that is consumable. They just get logs and say 'put it in Splunk and figure it out.'

For organizations struggling with authentication visibility, Corbado's Telemetry SDK provides authentication analytics without replacing your identity infrastructure. It works with any authentication method and any IDP (with particular depth for passkey implementations).

The SDK integrates via a few lines of JavaScript. It captures all authentication events, user interactions, errors and timing data across your entire login flow.

Visualize authentication as a multi-step funnel filtered by browser, OS, method and time range. Identify exactly where users drop off and why.

The SDK automatically classifies errors into actionable categories (user decisions, system errors, platform issues, informational) preventing false alarms and helping teams focus on real issues. Automatic monitoring detects spikes after OS/browser updates and unusual patterns.

When support receives "I can't log in" tickets, look up the exact sequence of events, errors with context and device information. UUID-only tracking means no PII flows to the analytics system simplifying GDPR compliance and InfoSec reviews.

Authentication analytics bridges the gap between identity infrastructure and user experience. By measuring every login attempt organizations transform authentication from necessary evil into competitive advantage.

For e-commerce brands, the stakes are especially high - see our State of E-commerce Authentication report for detailed benchmarks. Authentication friction directly correlates with cart abandonment and lost revenue.