07
Adoption & KPIs
North Star Passkey KPI
Main response theme: Adoption rate
Survey question
What is your North Star passkey KPI?
Why this matters
A passkey program usually needs one North Star KPI to keep rollout decisions grounded, but many teams are still defining what success should mean. This question matters because it separates programs that measure enrollment, adoption or active sign-in usage from those that are still working through the right operating metric.
Response Pattern
Activation / enrollment rate
38%
How To Read This
"Adoption rate" was quoted most often, but the term is ambiguous: many teams use it to mean activation or enrollment rate (credentials created), not actual passkey usage at sign-in. Read the distribution as a sign that KPI maturity is uneven rather than settled. Supported answers tend to cluster around enrollment first, while usage-oriented measurement becomes clearer once passkeys are already live and returning-user behavior can be observed.
Only answers that survey participants actually gave are shown. “I don’t know” and
unsupported responses are excluded. Most questions are multi-select, so percentages
describe theme prevalence and do not need to add up to 100%.
08
Adoption & KPIs
Political Success Story
Main response theme: Less friction
Survey question
How would the team describe a successful passkey launch to leadership in their own words, without committing to a hard number?
Why this matters
Passkey programs often carry two definitions of success that do not match: a measurable operating KPI and a softer narrative used in board-room language. This question separates the latter from the operating North Star KPI and from tracked ROI metrics by capturing the framing teams reach for when explaining value without committing to a number.
Response Pattern
Auth modernization brand story
38%
How To Read This
Read the distribution as the soft-power language of success rather than a measurement strategy. UX and modernization narratives dominate, while directional cost and compliance framings appear as supporting language. The gap between this and the operating North Star KPI is often where the program is most fragile: if the narrative pulls one way and the metric pulls another, the next quarterly review reveals the tension.
Only answers that survey participants actually gave are shown. “I don’t know” and
unsupported responses are excluded. Most questions are multi-select, so percentages
describe theme prevalence and do not need to add up to 100%.
09
Adoption & KPIs
Adoption Interventions
Main response theme: Post-login nudges
Survey question
Which interventions moved adoption the most?
Why this matters
Passkey adoption rarely moves because of a single switch; it usually depends on where the prompt appears, how much friction the flow removes and how clearly the experience is explained. This question matters because it distinguishes product-led adoption from broader rollout tactics such as communication, enablement or stronger migration pressure.
Response Pattern
Marketing / support collateral
70%
How To Read This
Read the distribution as a stack of levers, not a single winner. User-facing product guidance and education appear as recurring patterns, while more forceful migration approaches and automation-style tactics depend heavily on how mature the underlying rollout already is.
Only answers that survey participants actually gave are shown. “I don’t know” and
unsupported responses are excluded. Most questions are multi-select, so percentages
describe theme prevalence and do not need to add up to 100%.
10
Observability & telemetry
Journey Observability
Main response theme: IdP / backend logs
Survey question
How do you detect issues in the passkey authentication journey today?
Why this matters
Passkey issue detection is usually built from the signals an organization already has: backend logs, front-end telemetry, vendor dashboards and support feedback. This matters because observability is what turns a passkey rollout from a black box into a system teams can actually operate and improve.
Response Pattern
Custom frontend telemetry
44%
How To Read This
The distribution should be read as a maturity curve rather than a yes-or-no result. Teams with better instrumentation can see more of the journey, but the real dividing line is whether they can connect symptoms across channels and explain what is happening end to end.
Only answers that survey participants actually gave are shown. “I don’t know” and
unsupported responses are excluded. Most questions are multi-select, so percentages
describe theme prevalence and do not need to add up to 100%.
11
Observability & telemetry
Drop-Off Attribution
Main response theme: Cannot attribute reliably
Survey question
Can you attribute drop-offs to specific causes?
Why this matters
Attribution asks a harder question than detection: not just whether something broke, but where in the journey it broke and why. That distinction matters because passkey drop-offs can come from funnel friction, platform behavior or authentication errors and those require different fixes.
Response Pattern
Cannot attribute reliably
87%
Analytics tool attribution
54%
OS/browser attribution
17%
WebAuthn error attribution
8%
How To Read This
Read the spread as a gradient from coarse visibility to causal clarity. Some teams can identify the step where users fall out, fewer can tie that to a platform condition and only the most mature setups can confidently attribute a specific WebAuthn-related cause.
Only answers that survey participants actually gave are shown. “I don’t know” and
unsupported responses are excluded. Most questions are multi-select, so percentages
describe theme prevalence and do not need to add up to 100%.
12
Observability & telemetry
WebAuthn Error Tracking
Main response theme: Not tracked
Survey question
How do you track passkey and WebAuthn errors, and have you caught any platform regressions that way?
Why this matters
Tracking WebAuthn errors by operating system, browser and authenticator class is important because it can expose platform-specific breakage before it becomes a broader adoption problem. The question matters most where passkey behavior changes across devices, browsers or credential providers and teams need early warning rather than generic failure reporting.
Response Pattern
Platform regression found
20%
How To Read This
The pattern should be read as an observability ladder. Broad error awareness is more common than structured platform slicing, while authenticator-level tracking and regression detection represent a more advanced operating model.
Only answers that survey participants actually gave are shown. “I don’t know” and
unsupported responses are excluded. Most questions are multi-select, so percentages
describe theme prevalence and do not need to add up to 100%.
13
Observability & telemetry
Post-Rollout Surprises
Main response theme: Credential provider mix surprise
Survey question
After the company launched or piloted passkeys, what surprised them?
Why this matters
Post-launch surprises reveal the gap between pre-rollout models and actual operating conditions. This question captures what diverged from expectation after shipping, from enrollment patterns to provider behavior to user-support volume to cross-device handoff friction. It differs from error tracking, which measures what teams detect and from interventions, which measure what teams tried.
Response Pattern
Credential provider mix surprise
67%
Cross-device handoff friction
46%
Enrollment lower than expected
42%
Support ticket pattern unexpected
29%
Enrollment higher than expected
8%
How To Read This
Read the distribution as a forward-looking signal: enrollment gaps and support-volume surprises suggest planning assumptions need recalibration for the next cohort, and provider-mix surprises surface where the ecosystem diverges from vendor documentation. Few teams report positive surprises. The data is restricted to companies that have launched or piloted; pre-launch programs are excluded by design.
Only answers that survey participants actually gave are shown. “I don’t know” and
unsupported responses are excluded. Most questions are multi-select, so percentages
describe theme prevalence and do not need to add up to 100%.