Time to Authenticate

Time to Authenticate measures the total time a person experiences during login from initiating the authentication flow to reaching the intended authenticated state. We use it to quantify login speed, identify friction and protect conversion and productivity when authentication changes.

We start timing when the user initiates the login flow, and we stop timing when the user reaches authenticated content. Measurement boundary sentence: we include user input time, redirects and all server round trips until the authenticated state is reached.

flowchart LR
    OA(("Offer<br/>Auth"))
    OM(("Offer<br/>Method"))
    SM(("Start<br/>Method"))
    SuM(("Success<br/>Method"))
    SuA(("Success<br/>Auth"))

    OA --> OM --> SM --> SuM --> SuA

    OA -. "ENGAGEMENT RATE" .-> SM
    OM -. "METHOD CONVERSION" .-> SM
    SM -. "METHOD SUCCESS RATE" .-> SuM
    SM -. "AUTH SUCCESS RATE" .-> SuA

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We compute Time to Authenticate once per authentication attempt. If a user retries within the same session, each retry is a separate attempt, because it represents new friction.

• Auth started Timestamp is when the user clicks sign in, opens the login screen or triggers an authentication prompt.
• Auth completed Timestamp is when the app confirms an authenticated session and the user can access protected content.

A practical way to report is to show median time and a tail metric such as p95, since a small set of very slow attempts drives frustration.

We use Time to Authenticate to target specific sources of friction and verify that changes reduce real user time, not just server latency.

We can improve the following business outcomes:

• Higher successful sign ins that reach the intended authenticated state Diagnose which methods or platforms have the slowest median. Change the default method order and reduce step count. Validate by a sustained median drop without an increase in retries.
• Lower user drop off during authentication Diagnose long tails that correlate with extra steps like one time codes. Change the flow to reduce manual entry and reduce redirects. Validate by a p95 drop and fewer abandoned attempts.
• Lower support contacts caused by authentication issues Diagnose spikes where time grows alongside verification errors or delivery delays. Change messaging, timeouts, and recovery UX. Validate by fewer long attempts and fewer repeated starts.
• Better operational cost through fewer retries and fewer challenges Diagnose flows where time is dominated by waiting or repeated prompts. Change rate limits, challenge policy, and caching. Validate by fewer attempts per successful session and lower p95.

• Intent and selection bias: users who choose a slower method can make overall time look worse even if the main method improved.
• Missing telemetry: inconsistent start or completion events will undercount long attempts, especially when users close the app or lose connectivity.
• Mix shifts across segments: changes in platform mix, network quality, or new user share can move the metric without any product change.
• Timer boundary drift: starting the timer before the login UI is interactive or stopping it before the authenticated page renders can hide real friction.

Break down by auth method, platform, device type, and network quality, then track median and p95 weekly with alerts on sudden increases.