Passkey Analytics: Track Adoption, Funnels & Login KPIs

Passkey analytics provides product, identity and security professionals with comprehensive insights into passkey authentication flows, user behavior and adoption patterns. Corbado's management console offers powerful passkey analytics tools designed to help you optimize authentication experiences and increase passkey adoption rates.

From funnel analysis to device insights, our passkey analytics delivers actionable data that improves authentication performance. For broader authentication measurement strategies beyond passkeys, see our authentication analytics playbook.

The Authentication Funnel Analysis is one of the most important aspects of passkey analytics, providing a visual representation of all authentication processes and events. This feature functions like process mining for authentication, showing you exactly how users navigate through your system in a very graphical and understandable way.

Access the Authentication Funnel Analysis in the Corbado management console under Analytics > Funnel. The flowchart displays user paths through different authentication screens, helping you identify bottlenecks and unexpected behavior patterns.

The video above demonstrates the Funnel Analysis feature, showing how to visualize authentication flows in a funnel. You can see exactly how users move through signup, login and passkey append screens identifying where drop-offs occur and which paths lead to successful authentication.

The passkey analytics dashboard displays critical KPIs including:

• Gradual Rollout progress: Track how many users are eligible for passkeys in your current rollout size
• Passkey Intelligence filtering: Monitor intelligent filtering effectiveness of your smart passkey prediction and decision engine
• Append rates: Measure passkey creation success rates
• Usage success: Track successful passkey login attempts
• Fallback rates: Monitor authentication fallback frequency

In the following, you find three very common uses for the Authentication Funnel Analysis.

When launching passkeys, verify that users can successfully create and use passkeys. Start on the left side of the funnel and follow the "happy path": Was the user included in gradual rollout? Did passkey intelligence allow the passkey prompt? Did the user successfully create a passkey? This validation ensures your system is functioning before scaling to more users.

Compare authentication success rates between users with passkeys and those using fallback methods (e.g. passwords, OTPs). In typical deployments, users with at least one passkey show significantly higher login success rates. This data helps convince stakeholders: "If we increase passkey adoption, the overall authentication success rate improves."

Clicking on any node in the funnel reveals detailed metrics: completion rates over the filtered period, 12-month averages and month-over-month changes. Red triangle indicators flag nodes contributing to most authentication failures (often fallback methods for users without passkeys).

Analyze cross-device authentication (CDA) flows where users scan QR codes and use Bluetooth proximity checks. If CDA completion rates are low (e.g. 42%) or error rates high (e.g. 25%), this signals a need to improve copy, user education or investigate technical issues. High skip rates indicate users aren't understanding or trusting the cross-device flow.

Compare passkey analytics across different platforms (web vs. mobile apps) to identify platform-specific issues. For example, if iOS web shows 95% completion but iOS app drops to 70%, this signals an implementation issue requiring investigation.

Beyond clicking nodes, you can click the edges (connections between nodes) to see how metrics evolve over time. For example, clicking the edge showing "users with at least one passkey" reveals:

• Current percentage of users with passkeys (e.g. 58.9%)
• 12-month trend showing whether adoption is growing or declining
• Monthly and 30-day comparisons to spot sudden drops

A decline in April might indicate an OS update that broke passkey creation or a change in your implementation that inadvertently reduced adoption. Spotting these patterns proactively prevents issues from affecting more users.

Beyond standard signup and login flows, passkey analytics tracks:

• Auto-healing appends: Passkey creation after authentication errors
• Hybrid authentication appends: Passkey creation following QR code and Bluetooth proximity verification
• Account settings appends: Manual passkey creation within user profiles

Device Analytics provides insights into user login patterns and passkey activation rates across different user segments. This passkey analytics section helps you understand who actively uses your platform and how they engage with passkeys.

The Device Analytics video walks through login frequency segmentation, showing how to identify power users (20+ logins) versus occasional users (1-3 logins) and correlate this with passkey activation states. This helps answer questions like "Are power users more likely to adopt passkeys?"

The analytics dashboard segments login activity into three categories:

• Web-only login activities: Users who exclusively access via web browsers
• App-only login activities: Users who only use native applications
• Combined activities: Cross-platform user behavior

Each segment breaks down users by login frequency over 12 months:

• 1 login
• 2 logins
• 3 logins
• 4-10 logins
• 11-20 logins
• 20+ logins (power users)

Month-over-month percentage changes show growth or decline trends, helping you identify whether power users or occasional users drive passkey adoption.

For each login frequency segment, passkey analytics divides users into four activation states:

• Neither created nor used: Single-login users with no passkey interaction
• Append-only: Users who created a passkey but never used it for login
• Minority usage: Users with passkeys who use them less than 50% of the time
• Majority usage: Users who authenticate with passkeys more than 50% of logins

This granular passkey analytics data reveals which user segments embrace passkeys and where you need to improve activation efforts.

Device analytics provides detailed breakdowns by:

• Operating systems: iOS, Android, Windows, macOS
• OS versions: Specific version distribution
• Browser usage: Which browsers complete successful logins
• Passkey readiness: Percentage of devices capable of passkey authentication

For native apps, device analytics reveals how users have configured device authentication:

• Biometrics (e.g. Face ID): Users with biometric authentication enabled
• PIN / Passcode: Users relying on numeric codes or PIN patterns
• None: Small minority with no device lock (typically less than 1% on modern devices)

This passkey analytics information helps convince stakeholders that users actively employ biometric authentication on their devices, addressing concerns that "users won't use biometrics" with concrete data from your own user base.

Activation Analytics focuses on increasing passkey activation rates, one of three core KPIs for passkey analytics (alongside passkey usage rates and passkey error rates). This section helps you monitor and optimize how effectively users create passkeys when prompted.

This video demonstrates how to track append rates across multiple presentation attempts. You'll see how activation rates vary by platform (iOS, Android, Windows, macOS) and how to identify OS-specific issues affecting passkey creation.

The append rate measures the percentage of users who create a passkey when shown the creation screen. Passkey analytics displays append rates as both relative percentages and absolute numbers, split by platform (web, native apps).

Track append rates across multiple presentation attempts:

• First append screen: Initial passkey creation prompt
• Second append screen: Users who skip the first passkey creation opportunity
• Third, fourth, and fifth+ screens: Subsequent creation passkey attempts

The "nth screen" effect: Users don't always create passkeys the first time they see the prompt. Some need a second, third or fourth exposure before deciding. Analytics show that even on the fourth append screen, double-digit conversion rates persist. This insight justifies showing passkey prompts multiple times rather than giving up after a single decline. Persistence pays off without aggressive prompting.

The user activation rate shows the percentage of your entire user base who have created at least one passkey. Higher activation rates directly correlate with increased passkey login opportunities.

Passkey analytics breaks down activation rates by operating system and version enabling you to:

• Compare append rates across iOS, Android, Windows, and macOS
• Identify OS-specific issues affecting passkey creation
• Detect problematic OS versions requiring attention
• Proactively address implementation flaws before they affect more users

This granular passkey analytics approach ensures you can spot and resolve activation barriers quickly.

Login Analytics tracks passkey usage rates and performance metrics, providing insights into how effectively users authenticate with passkeys versus fallback methods.

The Login Analytics video shows real-world passkey login rate data comparing authentication speed between passkey and non-passkey methods. Typically, passkeys complete 4-5x faster than password-based flows.

The core metric in login analytics is the passkey login rate (passkey usage rate). It's the percentage of authentication attempts completed using passkeys. This passkey analytics metric appears across all active platforms with both trend charts and absolute numbers.

Passkey analytics tracks how users start their authentication:

• Conditional UI (Autofill): Users selecting passkeys from browser autofill suggestions
• One-Tap Button: Username-less authentication where returning users click a single button and the passkey flow starts automatically (available in web only)
• Passkey Overlay (Native Apps): On iOS/Android apps, the platform's native credential selector appears instead of a one-tap button
• Text Field Login: Traditional flow where users enter a username and click login, then complete passkey authentication

Comparing initiation methods reveals UX optimization opportunities. If most users default to text field login despite having passkeys, improving one-tap button placement could streamline authentication.

Login analytics compares passkey authentication speed against non-passkey methods, typically showing passkeys are 4-5x faster. Passkey analytics displays performance metrics including:

• 25th percentile login times
• Median (50th percentile) times
• 90th percentile times
• Average speed improvement

Compare passkey login rates and initiation methods across different operating systems to identify platforms where optimization is needed. If certain OS versions show significantly lower passkey usage, your passkey analytics data guides targeted improvements.

Passkey Insights provides deep visibility into the characteristics and nature of passkeys created in your app. This passkey analytics section helps you understand how users store and manage their passkeys.

This video explores authenticator (credential manager) distribution (e.g. iCloud Keychain, Google Password Manager, Windows Hello), sync status tracking and transport methods. You'll see how to identify whether passkeys are device-bound or synced across devices which is critical for understanding recovery scenarios.

Passkey analytics shows where users store their passkeys through detailed authenticator breakdowns:

• iCloud Keychain: Dominant for iOS and macOS users
• Google Password Manager: Primary for Android users
• Windows Hello: Common for Windows devices
• Third-party password managers: 1Password, Bitwarden, Dashlane and others

Pie charts display authenticators with over 5% market share, while detailed tables show the complete distribution including exotic password managers.

Combine authenticator data with additional dimensions:

• Authenticator + Operating System: See which OS versions use specific authenticators
• Authenticator + Browser: Understand browser preferences for each authenticator
• Sync Status + Operating System: Identify which platforms sync passkeys

This passkey analytics depth helps you understand user preferences and plan for password manager compatibility.

The hybrid passkey rate shows the percentage of passkeys capable of cross-device authentication via QR codes and Bluetooth. High hybrid rates ensure users can authenticate on devices without platform authenticators.

The synced passkey rate measures passkeys stored in cloud accounts or password managers with backupState and backupEligible flags set to true. Synced passkeys enable seamless authentication on new devices.

As of January 2026, Windows Hello doesn't sync passkeys by default, creating device-bound credentials. However, some Windows users employ third-party password managers that provide sync functionality.

Passkey analytics tracks transport capabilities, which determine how passkeys can be used:

• Hybrid transport: Passkeys that support cross-device authentication. A user with a passkey stored on their iPhone can log in on a laptop by scanning a QR code and completing a Bluetooth proximity check.
• Internal transport: Passkeys that only work on the device where they were created
• Hybrid + Internal: Passkeys supporting both local and cross-device authentication, offering maximum flexibility.

Understanding transport distribution helps predict user experience when they switch devices. If most passkeys lack hybrid transport, users may not be able to use passkeys on new devices.

Beyond static snapshots, passkey analytics offers time series views (daily, weekly, monthly) showing how passkey characteristics evolve:

• Track authenticator adoption trends
• Monitor sync rate changes after OS updates
• Identify sudden shifts in transport method distribution
• Correlate changes with your implementation updates

Example pattern detection: Comparing August to September data might reveal a stark drop in "hybrid + internal" transport and a surge in "non-tech" passkeys. Combining this with OS version data could pinpoint an iOS update that changed passkey behavior. This proactive detection prevents you from discovering issues only after users complain.

Planning changes: Before excluding third-party password managers or changing passkey policies, time series data shows exactly how many users would be affected and which segments rely on specific authenticators.

This temporal passkey analytics perspective helps you understand the impact of OS updates, feature releases, and policy changes on your passkey ecosystem.

While tools like GA4 can track basic login events, dedicated passkey analytics offers critical advantages:

For teams using GA4 for marketing attribution, the ideal setup combines GA4's user journey context with Corbado's authentication-specific observability. See tracking logins in GA4 for implementation guidance.

For organizations seeking passkey observability, Corbado provides the analytics capabilities described in this article. It works with any passkey implementation and any IdP without replacing your identity infrastructure.

The SDK integrates via a few lines of JavaScript and captures all passkey events: creation prompts, authentication attempts, errors and timing data. Visualize authentication as a multi-step funnel filtered by OS, browser and time range identifying exactly where users drop off.

Automatic classification separates user decisions (e.g. cancelled, skipped) from system errors (e.g. timeout, platform issues) preventing false alarms. Anomaly detection alerts you to spikes after OS updates before users complain.

Track passkey distribution across iCloud Keychain, Google Password Manager and Windows Hello. Monitor sync rates, transport methods and device authentication types providing the stakeholder-ready data to drive adoption decisions.

Comprehensive passkey analytics empowers you to optimize authentication experiences and maximize passkey adoption. The Corbado management console provides all the passkey analytics tools you need, from funnel analysis and device insights to activation tracking and login metrics.

By leveraging these passkey analytics capabilities, you can proactively identify issues, compare platform performance and make data-driven decisions that improve user authentication. Whether you're validating initial implementations or scaling to millions of users, our passkey analytics suite gives you the visibility required for success.

Passkey analytics tracks authentication metrics specific to passkey implementations: activation rates (how many users create passkeys), usage rates (how often passkeys are used for login), error rates and device/authenticator distribution. Unlike generic analytics, it provides visibility into passkey-specific behaviors like cross-device authentication and sync status.

According to FIDO Alliance data from May 2025: 74% of consumers are aware of passkeys, 69% have enabled at least one passkey, and 53% believe passkeys are more secure than passwords. Google reported 352% growth in passkey authentications year-over-year after making passkeys the default login option.

Track three core KPIs:

1. Activation Rate (users who create passkeys when prompted)
2. Usage Rate (percentage of logins completed with passkeys)
3. Error Rate (failed passkey authentications)

Segment by platform to identify OS-specific issues.

GA4 can track basic login events but lacks passkey-specific capabilities: no authenticator distribution, no sync status visibility and no cross-device authentication tracking. GA4 also has 24-48 hour latency and cardinality limits. Use GA4 for marketing attribution and dedicated authentication analytics tools for passkey-specific KPIs (e.g. Corbado).

The Passkey Index launched in October 2025 aggregates passkey utilization data from major service providers including Amazon, Google, Microsoft, PayPal and TikTok. It provides industry benchmarks for passkey adoption and business impact metrics.