How to Measure Authentication: Metrics, Funnels, and Conversion Tracking
Source: Dev.to
Introduction
Authentication is the front door to your product. If login is slow, confusing, or flaky, users don’t “try again later.” They leave or contact support. The problem is visibility: product analytics often stop at “login page viewed,” while identity systems report backend outcomes without the user’s context. Authentication analytics closes that gap by measuring authentication as a journey—success, failure, drop‑off, time‑to‑login, and the reasons behind each outcome.
Login data is split across teams and tools. Security teams monitor threats, product teams track funnels, and identity teams operate the infrastructure. Each view is incomplete on its own. A strict policy might look like a win while silently blocking legitimate users. Product sees abandonment but cannot tell whether the user mistyped a password, never received a multi‑factor authentication (MFA) code, or hit a client‑side error. Identity sees error codes, but those rarely translate into revenue, support load, or risk trade‑offs without additional context.
Authentication problems rarely show up as a single KPI. They leak value across conversion (failed logins and extra steps), support cost (resets, lockouts), and risk controls that create false positives and frustrate real customers.
Core Metrics
A practical metrics set usually covers:
| Category | Metric | Description |
|---|---|---|
| Reliability | Login Success Rate (LSR) | Percentage of successful logins. |
| Authentication Error Rate (AER) | Percentage of failed authentication attempts. | |
| Passkey Authentication Success Rate (PASR) | Success rate for passkey‑based logins. | |
| Friction & Speed | Authentication Drop‑Off Rate (ADoR) | Share of users who abandon the flow before completing authentication. |
| Time to Authenticate (TTA) | Average latency from start to successful authentication. | |
| Adoption | Passkey Enrollment Rate (PER) | Ratio of users who enroll a passkey after being offered. |
| Passkey Usage Rate (PUR) | Ratio of logins that actually use a passkey. | |
| Recovery & Impact | Password Reset Volume (PRV) | Number of password reset requests. |
| Authentication Support Ticket Rate (AST) | Support tickets related to login issues. | |
| Account Takeover Rate (ATOR) (where relevant) | Incidents of unauthorized account access. |
Data Sources
You typically need three sources:
- Identity Provider logs – Authoritative backend record of successes, failures, challenges, and provider‑specific error codes.
- Frontend analytics – Intent signals before the provider is contacted (e.g., login page views, “sign in” clicks). This captures client‑side failures that never reach the server.
- Observability & security tooling – Performance monitoring (latency, exceptions) plus threat signals and anomaly patterns.
Normalizing Events & Funnel Model
To make sources comparable, teams normalize events into a shared model. A practical funnel often looks like:
auth_viewed → auth_method_selected → auth_attempt → auth_challenge_served → auth_challenge_completed → auth_success | auth_failureThe key is separating viewed from attempted. A user can drop out before submitting anything, which backend logs will never see. With standardized events you can segment by device, OS, browser, credential manager, and authentication method.
Dashboards & Stakeholder Views
| Stakeholder | Primary Needs |
|---|---|
| Executives | High‑level health view and trend analysis. |
| Product teams | Granular funnels, cohort comparisons (passkey vs. password users), experiment results. |
| Security teams | Anomaly detection (e.g., credential‑stuffing spikes), risk dashboards. |
High‑Value Use Cases
- Method comparison – Compare authentication methods within a single funnel.
- Session debugging – Investigate a single user session when support escalates “I can’t log in.”
- Proactive monitoring – Detect breaking OS or browser changes before they cause churn.
Challenges
The hard part isn’t building charts; it’s stitching frontend and backend data, defining a consistent event taxonomy, and classifying errors across platforms where the same root cause can appear in many variants. Constant OS and browser updates turn authentication analytics into an ongoing discipline rather than a one‑time dashboard project.