Read the full article here
Why track login events in GA4
Authentication is the point where anonymous traffic becomes an identifiable user. Your IdP or CIAM system can confirm that a login happened, but it usually cannot explain what led to the attempt or where users struggled along the way.
That’s where Google Analytics 4 (GA4) can help. GA4 is event-based and includes a recommended **login** event, which makes it possible to connect pre-login acquisition context (source, campaign, device) with post-login outcomes (retention, conversion metrics like AOV or CLV). For product teams, this can turn “login friction” into something you can actually quantify.
What GA4 does well for authentication analytics
Cross-device journeys with GA4 User-ID
If you set a stable user_id after authentication, GA4 can stitch sessions together and associate pre-login behavior with the logged-in user. This is useful for questions like: do users who browse longer before logging in convert better, or do some channels consistently produce high-intent users who then fail at login?
Funnel exploration and segmentation
GA4’s Exploration reports let you build login funnels and segment after the fact by device category, browser version, traffic source, and more. It’s a practical way to spot patterns such as “logins failing mostly on one browser version” or “paid traffic has lower login success.”
Audiences and retargeting
GA4 can build audiences based on event sequences, like “high-intent users who failed login,” and export them for re-engagement campaigns. This is one of GA4’s biggest advantages over server-side auth logs.
Where GA4 falls short for login tracking
GA4 is not built for operational authentication monitoring, and the limitations are structural:
- Latency: GA4 reporting can lag by 24–48 hours, which is too slow for incident response when an OTP provider breaks or a release introduces auth errors.
- Cardinality limits: Detailed error codes, user agents, and device fingerprints can exceed GA4’s limits, causing values to collapse into “(other)” and reducing usefulness for root cause analysis.
- PII restrictions: You cannot send emails, usernames, or phone numbers to GA4. That makes per-user troubleshooting impossible inside GA4.
- Tracking gaps: Consent declines, ad blockers, cross-domain auth redirects, and iframe-based flows can all distort your login numbers if not handled carefully.
A practical GA4 event schema for login funnels
To get useful login success rate metrics, most teams need more than the default login event. A simple schema that works well:
-
**login_started**: user enters the auth flow (modal opened, sign-in clicked, or passkey autofill initiates) -
**login**(success): only fire when the backend issues a valid session token -
**login_failed**: backend error or client-side validation failure
Then add a small set of parameters that stay low-cardinality:
-
method(password, passkey, google, apple, sso) -
error_category(credential_mismatch, timeout, user_cancelled, network) -
login_location(header, checkout, settings) -
is_checkout(true/false) -
user_type(new/returning) -
c_device_support(passkey_ready/legacy)
Register these as Custom Dimensions in GA4 so they appear in reports.
Implementation approach: GTM + dataLayer
A common pattern is to push semantic auth events into the dataLayer and let Google Tag Manager (GTM) map them to GA4 events and parameters. This keeps analytics logic out of your app code and makes iteration easier. (Most teams also avoid sending raw error strings directly and instead map them to categories to prevent cardinality issues.)
What to monitor in GA4
A lightweight “Login Health” view typically includes:
- Login Success Rate: successful logins ÷ logins started
- Failure distribution: login failures by method and error category
- Checkout vs non-checkout login performance: separate conversion-critical flows
- Segmented views: device, browser, channel, and returning vs new users
For deeper timing metrics like time-to-auth, you often need a BigQuery export or a separate measurement layer.
Read the full article here
Top comments (0)