Login Conversion Rate

Login Conversion Rate (LCR) measures how often a user chooses a login method when we offer them at least one option. It captures the earliest point where intent turns into action, which makes it a sensitive indicator of UX clarity, perceived trust, and availability of a usable method.

We measure from the moment we present at least one login method to the moment the user starts any method. Measurement boundary: include only attempts that reach the method chooser, and exclude attempts that are already authenticated or bypass the chooser due to an existing session.

flowchart LR
    OA((Offer
    Login
    Options))
    CM((Choose
    Method))
    SM((Start
    Method))
    SA((Authenticated))

    OA --> CM --> SM --> SA

    OA -. "LOGIN CONVERSION RATE" .-> SM

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We calculate Login Conversion Rate per login attempt and we count each attempt once even if the user reloads the page. We recommend generating a stable login_attempt_id at the first offer event and using it to dedupe all later events.

• Login Attempts with Method oOffered means the attempt had an event where we showed at least one method the user could choose.
• Login Attempts with Method started means the attempt had an event that indicates the user initiated any method, such as entering an identifier, tapping continue, opening a passkey prompt or starting an OTP flow.

Count a login attempt if we log Login Method started for any method within the same attempt. Do not count passive renders, focus events or method impressions. Do not count attempts where we cannot tie the start event back to a valid login_attempt_id.

Count a login attempt if we log Login Methods offered with at least one eligible method. Do not count background refreshes that re-render the same chooser without user intent, unless they represent a new attempt id.

We use Login Conversion Rate to reduce early funnel drop off and to increase the share of users who reach an authenticated state.

We can improve the following business outcomes:

• Higher successful sign ins that reach the intended authenticated state
  • Diagnose: confusing chooser copy or weak default ordering
  • Change: simplify labels, highlight the primary method, reduce visual noise
  • Validate: LCR rises and support reports of login confusion fall for the same segments
• Lower user drop off during authentication
  • Diagnose: methods offered that are not usable on the device or are blocked by region
  • Change: improve method eligibility checks and method ordering by device and locale
  • Validate: LCR improves most in the affected device and country slices
• Lower support contacts caused by authentication issues
  • Diagnose: users churn at chooser because they do not recognize any option
  • Change: add clearer recovery entry points and context on what each option does
  • Validate: LCR increases and contact rate about login access declines
• Better operational cost through fewer retries
  • Diagnose: users start multiple methods after backing out of the chooser
  • Change: guide users to the highest likelihood method first
  • Validate: LCR rises while average method starts per attempt falls

• Intent and selection bias: Low intent traffic, automated probes, or users who only wanted to check account existence can lower LCR without a real UX problem.
• Missing telemetry or inconsistent logging: If Login Method Started fires only for some methods, or does not fire on slow networks, LCR will be understated and changes will be misleading.
• Mix shifts across segments: A launch that changes the mix of device types, countries, or new users can move LCR even if nothing in the chooser changed.
• Eligibility and gating effects: Tightened risk rules can increase offer events with fewer usable options, which can reduce LCR while improving security.

Report LCR by method set offered, primary method shown first, platform, app version, and country. Always keep a separate view for new versus returning users so mix shifts do not hide regressions.