traffic-diagnosis

name	traffic-diagnosis
description	Systematically diagnose why traffic changed — spike, drop, or shift in mix. Follow these steps in order. Each step narrows the hypothesis.

Traffic Change Diagnosis

Systematically diagnose why traffic changed — spike, drop, or shift in mix. Follow these steps in order. Each step narrows the hypothesis.

Step 1 — Establish the baseline

Compare the period in question to the equivalent prior period:

dimensions: ["date"]
metrics: ["sessions", "totalUsers", "newUsers", "engagedSessions", "keyEvents"]
date_range: affected period + same-length prior period

Measure: absolute change, % change, and whether all metrics moved together. If sessions dropped but engagement rate improved, volume dropped but quality held — different cause than if everything dropped proportionally.

Step 2 — Isolate which channel changed

dimensions: ["sessionDefaultChannelGroup"]
metrics: ["sessions", "totalUsers", "engagedSessions", "engagementRate"]
date_range: affected period vs prior period

Which channel account for most of the change? Narrow to that channel before going deeper.

Step 3 — Check time pattern (sudden vs gradual)

dimensions: ["date", "sessionDefaultChannelGroup"]
metrics: ["sessions"]
date_range: last 28 days

• Sudden single-day spike/drop → campaign launch/end, deploy, media mention, bot flood
• Gradual decline over weeks → SEO decay, seasonal drift, quality score drop
• Step change that persists → tracking change, filter change, channel definition update

Step 4 — Fingerprint the cause

Run queries matching the suspected cause:

Bot flood — see bot-traffic-detection skill:

• engagementRate near 0, averageSessionDuration ≈ 0
• Unusual hostname or country concentration

Campaign start/end:

dimensions: ["sessionCampaignName", "sessionSource", "sessionMedium"]
metrics: ["sessions", "keyEvents", "engagementRate"]
dimension_filter: sessionDefaultChannelGroup IN ["Paid Search", "Paid Social", "Display"]

SEO change (organic):

dimensions: ["sessionDefaultChannelGroup", "landingPagePlusQueryString"]
metrics: ["sessions", "engagementRate", "keyEvents"]
dimension_filter: sessionDefaultChannelGroup = "Organic Search"
date_range: 90 days (to see gradual trend)

Technical/tracking change — all channels drop equally at the same moment. Check with dimensions: ["date"] — a vertical drop on a single date across all channels points to a tag firing issue or consent mode change.

Step 5 — Conclude

State:

1. What changed (metric + magnitude)
2. Which channel drove it
3. When it started (sudden or gradual)
4. Most likely cause (from fingerprinting)
5. One action: investigate further / fix tracking / pause campaign / accept as seasonal

What not to do

Do not conclude "traffic dropped" from a single metric in isolation. Do not compare to the prior week without accounting for day-of-week patterns (Monday always differs from Sunday — compare Monday-to-Monday or full weeks).