Quattr leads AEO, SEO, and content rankings on G2 Spring 2026. View our G2 badges →
Request demo
Request demo

Stats

How to find out why traffic dropped

anomaly-investigator a saved expert workflow your assistant follows, in Claude, ChatGPT or Cursor

The "why did it move?" diagnostician: a fixed descent from is-it-real statistics through data artifacts, decomposition, localization, market events and technical suspects, ending in ranked causes with evidence.

Try it

traffic dropped, what happened

Private beta Part of the quattr-ai-search plugin, rolling out org by org. Request access ↗

Say any of these

  • why did clicks drop
  • traffic fell
  • what happened last week
  • impressions spiked
  • why are we down
  • diagnose this drop
  • did the update hit us
  • citations dropped, why

You never name the skill or a tool, the assistant routes on what you asked. These are the phrasings it recognises for this one.

And when it stays out of the way

Not for "are we up or down" (search-pulse), for scheduled reporting, or for pure "is this change real" verdicts with no cause-hunt wanted.

Use it instead of search-pulse when you want causes, not a headline; use weekly-search-report instead for the routine Monday readout.

What it runs

  • significance_check
  • compare_periods
  • gsc_breakdown
  • competitive_trends
  • site_health_cwv_check
  • indexation_gap_diagnosis
  • crawl_budget_waste
  • ai_crawler_analysis
  • web_analytics_overview

9 governed analyses, composed and scoped for you. Named here for transparency, not because you have to ask for them.

The numbers behind it

What it refuses to do

  • Single-cause storytelling is the named failure mode: when two causes overlap (e.g. an algorithm update + a reporting artifact), present both with their separate evidence.
  • Certainty language is banned on can't-tell verdicts; "confirmed" requires either significance + a mechanism, or an intervention test.
  • Ranked causes are hypotheses with evidence, the card says what would CONFIRM each; this skill never asserts causation from observational reads alone.

What the answer looks like

Skill: Drop triage with a significance verdict. Simulated · illustrative

Why did clicks drop?

Analysis plan, every step, resolved for you

  • Routed to the governed workflowanomaly-investigator
  • Dates bound explicitlyJul 12 to Jul 18, 2026
  • 4 analyses queued in parallelsignificance_check · get_data_freshness · gsc_breakdown · competitive_trends
  • Honesty gates armedsignificance α=0.05 · scope echo · freshness

The drop is real, and it isn't site-wide: 82% of it sits in How-to guides on mobile.

Organic clicks
33.9K ▼ −18.6%
Concentration
82% of the loss How-to guides · mobile
Data artifacts
none freshness verified, not a load gap

Jul 12 to Jul 18, 2026 vs Jul 5 to Jul 11, 2026organic

⚠ Observationalas of Aug 1, 2026 (GSC lags 1 to 2 days)IllustrativeOpen in Quattr ↗

Welch test on daily clicks vs the prior week, a real decline, not noise.

Verdict
Real — significant
p-value
0.003
95% CI
−24% to −12%

Ranked causes follow, a documented core update overlaps the window (correlation, not confirmation).

Jul 12 to Jul 18, 2026 vs Jul 5 to Jul 11, 2026organic

⚠ Observationalas of Aug 1, 2026 (GSC lags 1 to 2 days)IllustrativeOpen in Quattr ↗

Top-ranked cause: core-update volatility on How-to guides, rivals gained the same SERPs that week.

A simulated conversation on fictional data. Scope, freshness and the ⚠ Observational marker are attached by the same director that renders every demo here, not written by hand.

See this Skill inside a real analysis

All runs →

A recorded run where this Skill does the work, the prompts as typed, and the decision that came back.

Want this Skill running on your own accounts? Every Quattr customer works with an AI Search Strategist who sets it up with you.

Consult an expert