Authentication Analytics Playbook

Authentication Analytics Whitepaper:
Track passkey adoption & impact on revenue.

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Authentication is no longer just a yes/no checkpoint. It has become the front door to your digital business, shaping both user experience and security risk. Product managers and identity professionals face mounting demands: stricter security requirements like MFA and Zero Trust, alongside rising expectations for seamless, passwordless logins. As a result, understanding and optimizing authentication is very important.

In conversations with enterprise product & identity teams, a recurring theme emerges: setting up passkey analytics often requires weeks of dedicated engineering effort - and even then, teams struggle to distinguish informational errors from actual failures.

Authentication analytics deserves to stand alone as a function. Standard product analytics tell you what users do after they log in. Authentication analytics reveals why users succeed or fail at logging in at all - a crucial difference. This data provides rich details: device type, login behavior, credential managers, biometric use and point of failure. Traditional analytics tools often miss these signals, grouping everything into generic "session start" events. Treating authentication analytics as a standalone discipline moves your organization from reacting to outages and complaints to proactively managing and optimizing the login journey, just like you would the checkout funnel.

A catalyst for the necessity of this discipline is the operational and cultural chasm that exists between the three primary stakeholders of the authentication process: Identity, Product and Security teams. Each of these groups observes the login event through a different lens often resulting in misaligned incentives, fragmented data strategies and a disjointed user experience.

For the Security Operations Center (SOC) and fraud prevention teams, the authentication interface is primarily viewed as a threat vector. Their analytical focus is negative by design. They optimize for the absence of unauthorized access. Their dashboards, often powered by SIEM tools (e.g. Splunk) or specialized threat intelligence platforms are calibrated to detect anomalies - spikes in failed attempts, impossible travel velocity between logins and credential stuffing signatures. In this worldview, a "good" day is one with zero breaches, even if the strict policies enforcing that safety result in a 20% false-positive block rate for legitimate users. Security logs are often devoid of user-centric context, focusing on the validity of the credential rather than the quality of the experience. A user struggling with a complex MFA challenge is indistinguishable from a bot failing a script, leading to "security success" that masks "product failure".

Conversely, Product Managers and Growth teams perceive authentication as a necessary friction - a barrier that sits upstream of value capture. Their mental model equates the login screen to a checkout form. Utilizing tools like Google Analytics 4 (GA4), Mixpanel or Amplitude, they track "conversion," defined as the percentage of users who successfully navigate from the login prompt to the application dashboard or checkout page. However, these tools suffer from a lack of visibility into the "why" of abandonment. A user who drops off because they forgot their password looks identical in a Mixpanel funnel to a user who dropped off because an SMS OTP failed to deliver. Product teams, blind to the underlying infrastructure errors, may waste cycles A/B testing button colors when the root cause is a backend latency issue.

Caught in the middle are the Identity and Access Management (IAM) architects and engineers. They control the infrastructure - configuring policies in Identity Providers (IdPs) like Auth0, Okta or AWS Cognito. They have access to the rawest, most truthful data: the OIDC error codes, backend failures and API rate limits. Yet, they often lack the business context to translate error codes into a narrative about revenue loss. Without a unified analytics model, the Identity team struggles to justify investments in modern tech stacks (like passkeys) because they cannot easily correlate infrastructure performance with business KPIs like customer retention, conversion rate optimization or cart abandonment.

Authentication analytics serves as the "Rosetta Stone" that translates these disparate dialects into a common language.

• It enables Security teams to see when a new risk policy (e.g. "Block VPNs") inadvertently spikes abandonment rates for high-value customers.
• It empowers Product teams to distinguish between "User Confusion" (UX issues) and "System Failure" (vendor outages), allowing for targeted interventions.
• It provides Identity teams with the ROI data needed to champion passwordless initiatives, demonstrating that a move to passkeys is not just a security upgrade but a conversion optimizer.

The true cost of a suboptimal authentication stack is often invisible to standard financial reporting because it is distributed across multiple profit and loss (P&L) centers - marketing efficiency, support operations and fraud loss. A rigorous analytics playbook must begin by exposing these hidden dependencies.

The most direct impact is on top-line revenue, particularly in e-commerce. The "Guest Checkout Paradox" describes the tension where retailers allow guest checkout to maximize speed (velocity), sacrificing the long-term data value of a registered user (LTV). Research from the Baymard Institute shows that approximately 24% of users abandon carts specifically because a site required account creation - making mandatory registration one of the top reasons for checkout abandonment after unexpected costs (~48%). However, even for returning users, the friction of "logging in" is a conversion killer. If a user is forced to reset a forgotten password during checkout, the probability of them completing the purchase plummets. Analytics can reveal this "Bounce-on-Fail" rate. Early data from passkey adopters shows that reducing login time by ~50% directly correlates with improved session completion, proving that milliseconds in the auth flow equal dollars in the cart.

For large e-commerce brands with 100+ million active users, even a 1% improvement in login success can translate into seven-figure annual revenue impact. Authentication analytics is not a nice-to-have - it is directly tied to the bottom line.

A staggering percentage of IT helpdesk tickets (for internal employees) and customer support requests (for external users) are authentication-related. Queries such as "I forgot my password," "My account is locked" and "I didn't receive my 2FA code" top the volume charts. According to Forrester Research, the average help desk labor cost for a single password reset is approximately $70 when factoring in lost productivity - with some enterprises reporting costs as high as $150 per incident when including downstream effects. By tracking the "Auth-Support Ratio" - the number of auth-related tickets divided by total active users - organizations can quantify the operational tax of their legacy credential methods.

Finally, there is the hidden cost of risk management itself. In an attempt to secure weak credential methods (passwords), organizations deploy aggressive fraud detection engines that analyze IP reputation, device fingerprinting and behavioral biometrics. When these systems yield false positives, they block legitimate users - a phenomenon known as "insulting the customer." A user who is flagged as "Suspicious" during a legitimate transaction is not only lost revenue for that session but is highly likely to churn to a competitor. Furthermore, the reliance on passwords necessitates CAPTCHAs and knowledge-based questions, which degrade the brand experience. A sophisticated analytics framework tracks "Intervention Rates" and "False Positive Ratios," allowing teams to tune their risk engines to balance security integrity with user dignity.

To navigate the complexities of modern identity systems effectively, organizations must abandon simple counters like "total logins" in favor of compound, ratio-based metrics that reveal the health of the system.

*Each metric should appear with a mini sparkline and latest value in the dashboard UI.

The Authentication Success Rate (ASR) is the foundational metric of the discipline, yet it is frequently the most misunderstood and miscalculated. A naive approach - dividing total successful login events by total page views - introduces massive noise.

The precision of ASR depends entirely on how an "attempt" is defined.

• Page Load: Too broad. Includes users who bounced before interacting.
• Input Focus: Better, but includes users who got distracted.
• Credential Submission: The Gold Standard. An attempt should only be counted when the client sends a payload (password hash, OTP or passkey assertion) to the IdP.
• Refinement: Track "Intent Rate" separately. This is the ratio of Page Loads to Credential Submissions. A low Intent Rate signals a UI/UX problem (e.g. the login button is hard to find), while a low Success Rate signals a credential or infrastructure problem.

A Global ASR of 85% is a vanity metric that hides critical failures. To be actionable, ASR must be segmented:

• By Platform: Mobile Web ASR is notoriously lower than Desktop ASR due to keyboard usability issues (fat-finger typos) and aggressive mobile browser privacy protections.
• By Method: Compare Password ASR vs. Passkey ASR. According to the FIDO Alliance Passkey Index presented at Authenticate 2024, the average passkey sign-in success rate is approximately 93% - substantially higher than password success rates, which often languish below 80% due to memory failures and complexity requirements.
• By Cohort: New users (first return login) behave differently than power users. A drop in New User ASR might indicate that your onboarding flow failed to educate them on how to log in.

Some early adopters in retail and e-commerce have reported passkey login success rates reaching 96-99% in specific implementations - though results vary by organization, and tracking what happens before the WebAuthn assertion is returned remains a significant visibility gap.

The Authentication Failure Rate (AFR) (1 - ASR) is not just the inverse. It is a friction intensity signal.

• Context Matters: A 5% AFR on the main login page might be acceptable (typos). A 5% AFR on a step-up MFA challenge during a money transfer is a crisis.
• Security Signal: A sudden spike in Global AFR often indicates a credential stuffing attack, where a botnet is testing thousands of invalid passwords. Analytics must distinguish between "Organic Failure" (distributed, slow) and "Synthetic Failure" (concentrated, fast).

While ASR tells you that users are failing, the Error Code Distribution tells you why. Modern IdPs provide granular error codes that, when mapped correctly, serve as a diagnostic engine.

Raw codes should be grouped into three semantic buckets:

1. User Error (UE): Failures caused by the user's memory or action.

   • Examples: "Invalid Password," "MFA Code Invalid," "User Not Found."
   • Action: These indicate usability friction. High rates here scream for a transition to passwordless flows or better "Forgot Password" loops.

2. System Error (SE): Failures caused by infrastructure.

   • Examples: "Timeout," "Upstream Provider Unavailable," "Rate Limit Exceeded."
   • Action: Immediate engineering intervention. A spike here is an outage.

3. Policy Rejection (PR): Failures caused by security logic.

   • Examples: "Conditional Access Block," "Suspicious IP," "Account Locked," "Login from Tor Exit Node."
   • Action: Review security policies. Are we blocking too many legitimate users (False Positives)?

flowchart TD
    A[Raw Error Event] --> B{Classification}
    
    B --> C[User Error]
    C --> C1["Invalid Password"]
    C --> C2["MFA Code Expired"]
    C --> C3["User Canceled"]
    
    B --> D[System Error]
    D --> D1["Timeout"]
    D --> D2["Backend Unavailable"]
    D --> D3["Rate Limit"]
    
    B --> E[Policy Rejection]
    E --> E1["Conditional Access Block"]
    E --> E2["Suspicious IP"]
    E --> E3["Account Locked"]
    
    B --> F[Informational]
    F --> F1["Conditional UI Abort"]
    F --> F2["Method Switch"]
    F --> F3["Passkey Unavailable"]
    
    C1 & C2 & C3 --> G["Action: UX Improvement"]
    D1 & D2 & D3 --> H["Action: Engineering Fix"]
    E1 & E2 & E3 --> I["Action: Policy Review"]
    F1 & F2 & F3 --> J["Action: None - Expected"]

Teams should maintain a rolling window view of the top 5 error codes. Stability is key. If "Invalid Password" is consistently #1, the system is normal. If "SMS Delivery Failed" suddenly displaces it, you have a vendor incident.

One of the most critical - yet frequently overlooked - distinctions in passkey analytics is between users who have a passkey available versus those who actually use it.

flowchart TD
    A[User arrives at login] --> B{Has registered passkey?}
    B -->|No| C[Password/Social flow]
    B -->|Yes| D{Passkey available on device?}
    D -->|Unknown - Conditional UI| E[Show autofill prompt]
    D -->|Yes - Cookie hint| F[Show one-tap button]
    D -->|No| G[Offer CDA or fallback]
    
    E -->|User selects passkey| H[Platform authentication]
    E -->|User ignores/types email| C
    F -->|User clicks| H
    G -->|User scans QR| I[Cross-device authentication]
    G -->|User chooses fallback| C
    
    H -->|Success| J[Session created]
    H -->|Error| K[Error handling]
    I -->|Success| J
    I -->|Error| K
    C -->|Success| J
    
    K --> L{Recoverable?}
    L -->|Yes| M[Retry or fallback]
    L -->|No| N[Support escalation]

Passkey Availability Rate: The percentage of login sessions where a passkey could theoretically be used - i.e. the user has registered a passkey on the current device or a synced credential provider.

Passkey Usage Rate: The percentage of login sessions where a passkey was actually used to complete authentication.

The gap between these two metrics reveals user behavior friction:

• A user sees the Conditional UI prompt but ignores it and types their password
• A user has a passkey on device A but is logging in from device B
• A user cancels the biometric prompt and falls back to SMS OTP

Tracking this gap requires sophisticated client-side instrumentation because the WebAuthn API does not expose whether a credential is available before the user interacts with the authenticator - by design, for privacy reasons. Solutions include:

• Cookie-based passkey hints: Store a flag after successful passkey creation to indicate the device likely has a usable credential
• Backend credential lookup: After identifier entry, check if the user has registered passkeys and on which authenticator types (via AAGUID)
• Probabilistic estimation: Use known device fingerprint + credential history to estimate availability

As regulations like PSD2 in Europe and FFIEC guidance in US Finance mandate Multi-Factor Authentication, measuring its lifecycle becomes critical.

Track the user journey from eligibility to enrollment:

1. Eligible Users: Total active users.
2. Enrolled Users: Users with at least one MFA factor registered.
3. Factor Distribution: Breakdown by assurance level - SMS (Low), TOTP (Medium), WebAuthn/Passkey (High).

This is arguably the most important metric for MFA UX. It measures how often a user, when prompted for their primary factor, fails or chooses "Try another way."

• Scenario: A user is prompted for a Passkey. They click "Cancel" and select "Send SMS" instead.
• Diagnosis: High fallback rates from Passkey to SMS suggest issues with device compatibility (e.g. trying to use a platform authenticator on a machine without a biometric sensor) or poor user education.
• Formula: (Logins via Secondary Method / Challenges via Primary Method) * 100.

In Adaptive Authentication (Risk-Based Auth), users are only challenged when risk is high.

• Challenge Rate: What % of sessions trigger a step-up? (e.g. 5%).
• Step-Up Success Rate: Of those challenged, what % succeed? If this is low (<80%), your risk engine is effectively a "Deny" engine, blocking legitimate users under the guise of security.

TTL should be measured with "stopwatch" precision:

• Start: DomContentLoaded of the login page OR InputFocus of the username field.
• End: Receipt of the Session Token (JWT).
• Benchmarks: Passkeys have demonstrated a 50% reduction in login time compared to passwords, often clocking in under 15 seconds compared to the 30+ second average for password+MFA flows.
• Latency Breakdown: Advanced analytics split TTL into "User Think Time" (cognitive load, typing) vs. "System Processing Time" (crypto verification, API round trips).

How many tries does it take to get in?

• The "One-Shot" Ideal: 1 Attempt = 1 Success.
• The "Brute Force" Curve: A user failing 10 times in a minute is likely a bot.
• The "Struggle" Curve: A user failing 2 times and succeeding on the 3rd is a frustrated human. Tracking the distribution of attempts (1, 2, 3, 4+) identifies "struggling" cohorts who are at high risk of churn.

The "Average User" does not exist. The ecosystem is fragmented and analytics must reflect this.

When introducing a new method (e.g. Passkeys), track which method it displaces.

• Positive Shift: Passkeys replacing Passwords (Higher Security, Higher Conversion).
• Neutral Shift: Passkeys replacing Social Login (Similar friction).
• Negative Shift: Users ignoring Passkeys to stick with Passwords.

Authentication is heavily dependent on client-side capabilities.

• Platform Readiness: Track the percentage of user devices that support WebAuthn (e.g. iOS 16+, Android 9+). This "Addressable Market" metric is vital for deciding when to make passkeys the default.
• WebView Black Holes: In-app browsers (Instagram, TikTok WebViews) often break federated login flows or block biometric APIs. Segmenting failure rates by User-Agent helps identify these compatibility dead zones.

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"]
        SDK["Telemetry SDK"]
        Analytics["Product Analytics\n(GA4, Mixpanel)"]
    end
    
    subgraph Backend["Backend Layer"]
        IdP["Identity Provider\n(Auth0, Okta, Cognito)"]
        Logs["Application Logs"]
    end
    
    subgraph Observability["Observability Layer"]
        APM["APM\n(Datadog, Sentry)"]
        SIEM["SIEM\n(Splunk)"]
    end
    
    subgraph AuthAnalytics["Authentication Analytics Platform"]
        Ingest["Event Ingestion"]
        Process["Processing & Classification"]
        Dashboard["Dashboards & Alerts"]
    end
    
    SDK --> Ingest
    IdP --> Ingest
    Logs --> Ingest
    APM --> Ingest
    
    Ingest --> Process
    Process --> Dashboard

The Identity Provider (IdP) is the authoritative source for the mechanics of the transaction.

Auth0 provides a structured event schema that is essential for granular analysis.

• Log Types:
  • s (Success): Successful login.
  • gd_auth_failed / f (Failure): Generic credential failure.
  • gd_start_auth: Initiation of an MFA flow.
  • sc (Signup): New user creation.
• Enrichment: Logs include client_id, user_agent and ip. The details object often contains the specific reason, such as "Wrong email or password" or "MFA code expired".

Okta uses a query-based log system with specific event types.

• Key Events:
  • user.authentication.verify: The primary verification event.
  • user.session.start: Indicates a session was successfully minted.
  • system.push.send_factor_verify_push: Tracks the delivery of Okta Verify pushes.
• Error Codes: Look for VERIFICATION_ERROR, E0000007 (Unknown User) or E0000047 (Rate Limit). Okta also logs risk levels, which is vital for security filtering.

Cognito logging is split, requiring a dual-source strategy.

• CloudTrail: Logs management events (AdminInitiateAuth, CreateUser), but is often too high-level for user behavior analysis.
• CloudWatch User Activity Logs: Available on "Plus" plans, these INFO level logs provide details on sign-in attempts.
• Metric Traps: The native SignInSuccesses metric in CloudWatch is useful but often lacks the granularity of failure reasons. Building custom metrics from the log stream using CloudWatch Logs Insights is recommended.

While IdP logs track the backend result, product analytics tools track the frontend user intent.

Product tools capture the funnel before the IdP is contacted.

• Step 1: login_page_viewed (Product Tool)
• Step 2: login_button_clicked (Product Tool)
• Step 3: auth_attempt (IdP Log)
• Gap Analysis: If you have 1,000 button clicks but only 800 auth attempts, 20% of your users are failing due to client-side JavaScript errors, slow loading resources or UI confusion, never even reaching the server.

Google Analytics 4 (GA4) uses a User-ID feature to stitch sessions. It is critical to set this ID immediately upon the auth_success event. This allows the system to retroactively associate the anonymous behavior (browsing before login) with the known user, enabling "Cross-Device" journey mapping.

These platforms provide the infrastructure context - latency, errors and code exceptions.

Datadog & Sentry: Performance Monitoring

• Latency Tracing: Use distributed tracing (APM) to visualize the POST /login waterfall. How much time is spent in the database vs. the password hashing algorithm (bcrypt/scrypt)?
• Exception Catching: Sentry catches unhandled exceptions in the login logic (e.g. NullPointerException during user lookup) that typically manifest as generic "500 Internal Server Errors" to the user and are opaque in IdP logs.

SIEM (Splunk, Datadog Security)

These tools ingest IdP logs to correlate them with network-wide threats. They allow for the creation of complex detection rules, such as "Alert if >100 failed logins occur from a single IP subnet within 5 minutes".

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]

    %% Passkey-specific branching
    B -- Passkey Selected --> H[passkey_prompt_displayed]
    H --> I[auth_attempt]
    F -- On Passkey Creation --> J[passkey_append_success]

Identity resolution is the linchpin of accurate analytics. A hierarchical approach is required:

1. Device ID (Anonymous): Generated client-side (e.g. UUID in LocalStorage). Tracks the "machine" even before login. Persists across sessions on the same browser.
2. Session ID: Ephemeral ID for the current browsing context. Ties pre-login behavior (cart building) to post-login behavior (checkout).
3. User ID (Canonical): The immutable database ID.
   • The "Aliasing" Moment: When auth_success fires, the analytics backend must retrospectively link the anonymous Device ID and Session ID to the User ID. This enables you to report "User 123 failed" rather than "Anonymous Device X failed."

Privacy Hygiene: Never log PII (Personally Identifiable Information) like email addresses or plain-text usernames in analytics properties. Use SHA-256 hashes of these identifiers to allow for correlation without exposing user data to the analytics vendor.

The "Cross-Device Gap" is where most attribution models fracture. A user begins signup on a Desktop but finishes on Mobile to verify an email or use a biometric scanner.

• Deterministic Matching: Relies on the User-ID being present on both devices. This is highly accurate but only works after the user logs in on the second device.
• "Magic Link" Bridge: If a user clicks a magic link on mobile that was requested on desktop, pass a correlation_id in the link parameters. This allows the backend to stitch the desktop request event to the mobile fulfillment event.
• Visitor Stitching Limitations: While tools like Adobe Analytics offer probabilistic "Cross-Device Analytics," modern browser privacy controls (ITP/ETP) degrade their accuracy. Deterministic User-ID matching remains the most reliable method for authentication contexts.

Data without visualization is merely storage. To drive action, data must be curated into dashboards tailored to the specific needs of different stakeholders.

The C-Suite and VP-level stakeholders require a "Health Monitor" - a high-level abstraction that correlates authentication performance with revenue and risk.

Key Widgets:

1. Global Auth Health Score (0-100): A composite index weighting Success Rate (50%), Latency (25%) and Error Rate (25%).
   • Example: "Current Health: 98/100 (Stable)."
2. Revenue at Risk: A calculated metric estimating the potential revenue loss from failed logins.
   • Formula: (Failed Logins at Checkout) * (Average Order Value).
   • Display: "Potential loss of $12k this week due to auth failures."
3. Adoption Velocity: Trend lines showing the migration to modern auth.
   • Chart: Stacked area chart of "Logins by Method" (Password vs. Passkey vs. Social).
   • Goal: Visualizing the decline of passwords and the rise of passkeys.
4. Operational Efficiency: Metrics showing cost reduction.
   • Data: "SMS Costs Saved" (via Passkey/TOTP usage) and "Helpdesk Ticket Volume" reduction.

Product Managers need a "Microscope" to diagnose friction and optimize the funnel.

Key Widgets:

1. Granular Funnel: A multi-step visualization: View -> Input Focus -> Submission -> MFA Prompt -> Success.
   • Insight: A drop-off at "MFA Prompt" suggests users don't have their device. A drop-off at "Input Focus" suggests the form is confusing or hidden.
2. Cohort Retention: Comparing the long-term behavior of users based on their auth method.
   • Chart: "Retention of Users who use Passkeys" vs. "Retention of Users who use Passwords." (Passkey users typically show higher retention due to lower login friction).
3. A/B Test Performance: Side-by-side comparison of a new flow (e.g. "One-Tap Login") vs. the control group.
   • Metrics: Conversion Rate, Time-to-Auth.
4. Device Breakdown: "Login Success on iOS vs. Android." Essential for identifying OS-specific bugs or UI issues.

The SOC needs a "Radar" to detect threats in real-time.

Key Widgets:

1. Velocity Maps: A geospatial heatmap of login attempts.
   • Signal: A sudden concentration of logins from a country where you do no business is a clear attack indicator.
2. Credential Stuffing Detector: A ratio chart tracking "Failed Login Attempts per Unique IP."
   • Signal: A high ratio indicates a botnet using rotated IPs to attack a single account or a single IP attacking many accounts.
3. Impossible Travel: A list of users who logged in from geographically distant locations (e.g. New York and London) within an impossible timeframe (e.g. 1 hour).
4. New Device Spikes: A time-series chart of "New Device" logins vs. "Known Device" logins. A sudden surge in new devices can indicate a successful phishing campaign or a breached database being tested.

Analytics is a cyclical process: Measure -> Insight -> Action. Here are practical scenarios where this playbook drives value.

• Scenario: Analytics reveal a 15% drop-off at the "Password Reset" initiation screen.
• Insight: Users are clicking "Forgot Password" but failing to complete the email loop. Likely, the email is going to spam or the user is distracted by the context switch.
• Action: Implement a "Try another way" option directly on the reset screen (e.g. verify via SMS OTP or Magic Link). Alternatively, analyze email provider logs (SendGrid/SES) to fix deliverability issues.
• Result: A measurable decrease in support tickets tagged "I can't reset my password" and an increase in account recovery success.

• Scenario: You have enabled Passkeys, but the "Append Rate" (creation rate) is flat at 5%.
• Insight: The "Create Passkey" prompt is appearing after a high-stress checkout flow, where users are eager to leave.
• Action: Relocate the "Create Passkey" nudge to the "Login Success" screen or the "Account Settings" dashboard. Utilize "Conditional UI" (browser autofill) to make the creation process passive rather than active.
• Measurement: Track "Passkey Append Rate" pre- and post-change. A jump to 20% validates the hypothesis.

• Scenario: The Identity team deploys a new security rule: "Block traffic from VPNs."
• Signal: The "Policy Rejection" error code count spikes by 400% immediately after deployment. The "Support Ticket" volume spikes 2 hours later.
• Insight: A significant portion of legitimate, privacy-conscious customers (or remote employees) use VPNs. The policy is too aggressive.
• Action: Roll back the policy or refine it to "Step-up MFA on VPN" rather than "Block VPN."
• Automation: Set up an anomaly alert (e.g. in Datadog or PagerDuty) that triggers if Error Rates exceed 2 standard deviations from the historical mean.

In the world of e-commerce, friction is the enemy of revenue. Authentication analytics is, effectively, "Checkout Analytics."

The "Guest Checkout Paradox" defines the central tension of e-commerce auth: Guest checkout converts better (faster velocity), but Authenticated checkout drives higher LTV (repeat purchases, loyalty).

• Metric: "Guest-to-Account Conversion Rate." How many users who checkout as guests eventually create an account?
• Optimization: Use analytics to find the "Happy Moment." Do not ask for a password before checkout. Instead, ask for a Passkey creation after payment confirmation.
• Correlating Auth with Cart Abandonment: Overlay "Auth Failure" timestamps with "Cart Abandonment" events. If 80% of abandonments happen within 10 seconds of an auth challenge, your auth strategy is actively killing sales.

Strategic content placement:

When discussing broad industry benchmarks - e.g. "What is a 'good' login success rate for retail?" - it is vital to contextualize your internal data with industry standards.

• Context: "While our internal password success rate is 78%, the industry average detailed in the State of E-commerce Authentication 2025 report indicates that top-tier retailers achieve 92% by leveraging biometric passkeys and persistent sessions."
• Usage: Use the report to validate the ROI of moving away from passwords. If your analytics show high SMS costs, cite the report's findings on the cost-efficiency of WebAuthn to justify the budget for a refactor.

You do not need a massive data science team to begin. The journey to Identity Observability can be iterative.

• Goal: Establish basic visibility. Answer: "Are we up?" and "How many users are failing?"
• Action:
  • Ingest raw logs from your IdP (Auth0/Okta) into a basic visualization tool (e.g. Grafana or the IdP's native analytics console).
  • Track 3 Core Metrics: Total Logins, Success Rate (Global) and Top 5 Error Codes.
  • Resource Cost: 1 Engineer, 2 days of setup.

• Goal: Deepen understanding. Answer: "Who is failing and why?"
• Action:
  • Implement User-ID tracking in Google Analytics 4 to stitch sessions.
  • Create segments for "Mobile vs. Desktop" and "Password vs. Social."
  • Start tracking MFA Drop-off rates and Fallback usage.
  • Resource Cost: Product Analyst + Frontend Developer synchronization.

• Goal: Understand passkey adoption and usage patterns.
• Action:
  • Track append rates (passkey creation) by trigger point (post-login, account settings, checkout)
  • Measure fallback rate from passkey to password/OTP
  • Implement AAGUID tracking to understand authenticator distribution
  • Track conditional UI initiation vs. completion
  • Resource Cost: 1 Engineer dedicated for 2-3 weeks (based on real-world enterprise experience)

• Goal: Drive optimization and self-healing.
• Action:
  • Build the "Unified Data Model" in a data warehouse (Snowflake/BigQuery), merging IdP logs with Product data.
  • Deploy Passkeys and rigorously track "Append Rates" and "Fallbacks".
  • Set up automated alerting on conversion dips.
  • Run A/B tests on login UI components (e.g. "Button vs. Input Field").
  • Correlate authentication success with downstream revenue metrics.
  • Resource Cost: Dedicated Data Engineer and ongoing Product Management focus.

A common question from product and identity teams: "Why isn't there a standard tool for this like there is for product analytics or APM?"

The answer lies in the fragmentation of the authentication ecosystem and the misaligned incentives of existing vendors:

IdPs like Auth0, Okta and Cognito are built primarily as security infrastructure. Their logs and dashboards are optimized for threat detection and compliance - not for product optimization. When an IdP reports a 92% success rate, they often mean "92% of valid credentials were accepted" - but they miss the 15% of users who abandoned before even submitting credentials because the UX was confusing.

Tools like Mixpanel, Amplitude and GA4 are excellent at tracking what users do inside your product - but they treat authentication as a black box. They see login_page_viewed and `session_started but have no visibility into:

• Which authentication method was attempted
• What errors occurred at the WebAuthn or OIDC layer
• Why a user fell back from passkey to password
• The difference between "User Error" and "System Error"

Passkeys add another layer of complexity that neither IdPs nor product tools understand natively:

• Conditional UI has no success/failure event - only completion or absence
• Authenticator types (platform vs. roaming, synced vs. device-bound) affect user behavior differently
• AAGUID tracking requires specialized knowledge to map to human-readable authenticator names
• Cross-device authentication (hybrid transport) has fundamentally different success patterns

Building authentication analytics in-house is deceptively complex. Every OS update, browser version and password manager release can introduce new behaviors. Chrome's passkey implementation differs from Safari's differs from Samsung Internet's. Keeping error taxonomies current and dashboards accurate requires dedicated ongoing effort - effort most teams underestimate until they're debugging a production issue at 2 AM.

A recurring challenge cited by engineering teams: distinguishing between errors that require action and those that are simply expected behavior - such as the abort signal fired when transitioning from conditional UI to the next authentication step.

For organizations implementing passkeys with their own WebAuthn server but struggling with visibility, Corbado offers a Passkey Telemetry SDK that provides enterprise-grade authentication analytics without replacing your identity infrastructure.

The SDK integrates via a few lines of JavaScript - no backend changes required. It captures:

• All WebAuthn API calls and responses
• User interaction patterns (button clicks, input focus, form submissions)
• Error events with full context
• Timing data for performance analysis

Visualize your authentication flow as a multi-step funnel with branching paths:

Filter by browser, OS, authenticator type, geography and time range. Identify exactly where users drop off.

The SDK automatically classifies errors into actionable categories:

• User Decisions: User canceled, user chose fallback, user ignored prompt
• System Errors: Network timeout, backend validation failure, rate limit
• Platform Issues: Unsupported browser, broken authenticator, known OS bugs
• Informational: Abort during conditional UI transition (expected behavior)

This prevents false alarms and helps teams focus on real issues.

Automatic monitoring detects:

• Sudden spikes in specific error codes
• Regressions after OS or browser updates
• Geographic or device-specific failures
• Unusual patterns indicating potential attacks

When customer support receives "I can't log in" tickets, the SDK provides session replay showing:

• Exact sequence of authentication events
• Errors encountered with context
• Device, browser and authenticator information
• Timestamps for correlation with backend logs

• UUID-only tracking - no PII required
• Data stored in EU-based infrastructure
• SHA-256 hashing for any identifiers
• Configurable data retention periods

Ready-to-use dashboards for:

• Executive health scores
• Product funnel analysis
• Security anomaly monitoring
• Passkey adoption tracking

Customizable templates ensure alignment with your specific authentication flow and business metrics.

Authentication analytics is the vital bridge between the technical reality of identity infrastructure and the business reality of user experience. By measuring the nuance of every login attempt - successes, failures, latencies and fallbacks - organizations can transform authentication from a necessary evil into a competitive advantage. The transition to passkeys and modern identity standards makes this discipline more critical than ever; as the mechanisms of login become invisible (biometrics), the metrics must become more visible.

For e-commerce brands, the stakes are especially high. Authentication friction directly correlates with cart abandonment and lost revenue. The "Guest Checkout Paradox" can only be solved with data - understanding exactly when and why users fail to complete login during checkout.

Start with the core success rates. Expand into funnel analysis. Build alerting for regressions. And ultimately create a culture where every authentication error is treated as a learning opportunity to reduce friction, increase conversion and build user trust.