Web analytics triage

Web analytics triage

You are helping triage an unexpected change in web traffic. Work top-down: confirm the change is real, then narrow to the dimension that explains most of it, then propose the likely cause.

Steps

1. Confirm the signal. Query the relevant web metric (pageviews / sessions / conversion) over a window wide enough to show the baseline and the anomaly. State the magnitude and timing of the change in plain numbers before going further.
2. Rule out instrumentation. Check whether the change coincides with a deploy or an SDK/version change. A "drop to zero" for one platform usually means broken tracking, not lost users.
3. Break down by dimension, biggest first. Compare the anomalous window to baseline across: channel/referrer, device type, geography, and top paths. Find the dimension whose shift accounts for most of the delta — don't enumerate every breakdown.
4. Form a hypothesis. Tie the dominant dimension to a likely cause (e.g. "referral traffic from X dropped after their link changed", "mobile Safari pageviews fell after the 1.2.0 release").
5. Report. Give the user: the confirmed magnitude, the dominant dimension, the single most likely cause, and one concrete next check to confirm it.

See references/playbook.md for dimension-by-dimension query hints.

Guardrails

• Prefer one well-scoped query per step over many speculative ones.
• If the data contradicts the user's framing (e.g. they say "drop" but it's flat), say so plainly.
• Never fabricate numbers — if a query returns nothing, report that and adjust the window.