SEO Traffic Diagnosis
Diagnose why organic traffic moved (down, flat, or unexpectedly up) using Ahrefs MCP combined with Search Console and analytics data. Stack-agnostic. Produces a root-cause diagnosis and an action plan.
When to use
- Organic traffic dropped sharply
- Organic traffic has been flat for months despite content investment
- After a known Google algorithm update
- After a migration, replatform, or domain change
- After a deploy that touched routing, redirects, or rendering
- When a competitor is visibly taking organic share
- When a single page dropped from a ranked position
- When stakeholders need an explanation, fast
When NOT to use
- Routine performance reporting (use
analytics-strategy) - Pre-emptive content planning (use
seo-content-gap-audit) - Backlink-only investigations (use
seo-backlink-audit) - Technical issue triage outside of traffic concerns (use
seo-site-health-audit)
Required inputs
- Description of the symptom (what changed, when, magnitude)
- Date the change started (best estimate)
- Recent SEO history: deploys, migrations, content changes, link campaigns
- Access to Ahrefs MCP, Search Console, and analytics
- Confirmation the change is real (not a tracking artifact)
The framework: 5 layers of diagnosis
A traffic change has one or more root causes. Move through the layers in order. Stop when you have enough evidence.
Layer 1: Confirm the change is real
Before diagnosing, rule out:
- Tracking gaps (analytics outages, tag manager issues)
- Bot traffic changes
- Reporting comparison errors (different date ranges, wrong segment)
- Seasonality (compare year-over-year, not just month-over-month)
- Holiday or weekday effects
Cross-check Search Console clicks against analytics organic sessions. Significant divergence often points to a tracking issue, not a real traffic change.
Layer 2: Localize the change
Where is the change happening?
Segment by:
- Country and language
- Device (mobile, desktop, tablet)
- Page or section (homepage, blog, product, category)
- Query type (branded vs non-branded)
- Landing page
A change in one segment requires different diagnosis than a change everywhere.
| Pattern | Likely cause |
|---|---|
| One country dropped | Local algorithm update, hreflang issue, geo redirect issue |
| Mobile dropped, desktop flat | Mobile usability or page speed regression |
| One section dropped | Topical algorithm update or section-specific quality issue |
| Branded queries dropped | Brand-level issue: site outage, reputation, manual action |
| Non-branded dropped | Algorithmic ranking issue |
| Single page dropped | Page-level issue: content, technical, or competitive |
| Sitewide dropped | Sitewide issue: penalty, technical, migration, or algorithm |
Layer 3: Page-level analysis
For affected pages, audit:
- Position changes per ranked keyword (Ahrefs Rank Tracker history)
- SERP composition changes (more ads, AI overviews, featured snippets, video)
- Click-through rate changes
- Index status (Search Console coverage)
- Crawl errors and accessibility
- Recent content changes
- Internal link changes
- Backlink changes (lost links, redirect chains)
A page can lose traffic without losing rank if SERP composition changed.
Layer 4: Technical analysis
Did anything break technically?
Check:
- Robots.txt changes
- Canonical tag changes
- Meta robots changes (accidental noindex)
- Redirect chains and loops
- Render issues (especially for JS-heavy frameworks)
- Site speed regressions
- Hreflang errors
- Sitemap freshness
- HTTP status codes (4xx, 5xx spikes)
- Server log evidence of crawl behavior changes
Recent deploys are the prime suspect. Compare deploy dates to traffic change dates.
Layer 5: External analysis
If layers 1-4 do not explain the change, look outward.
- Algorithm update calendar (cross-reference timing)
- Competitor moves (new content, new SERP features they captured)
- Industry trend (declining search demand for the topic)
- Manual action (Search Console security and manual actions)
- Negative SEO (sudden link velocity changes)
External-factor diagnosis benefits from competitive context: did your traffic drop while competitors held steady (suggests an algorithm-specific issue), or did the entire vertical lose ground (suggests a user-behavior shift)? Similarweb shows competitor traffic trends; Ahrefs shows competitor SERP movement; pairing both surfaces whether the issue is yours alone or the category's.
Workflow
- Confirm the symptom. Get exact dates, magnitude, segment if known.
- Validate the data. Layer 1 checks. Rule out tracking and seasonality.
- Localize. Layer 2. Segment until the pattern is clear.
- Page-level dive. Layer 3 on the most affected pages.
- Technical check. Layer 4. Recent deploys, robots, canonicals, redirects.
- External check. Layer 5. Algorithm updates, competitors, industry.
- Build the hypothesis. State the cause as a single sentence.
- Validate the hypothesis. Find the evidence that confirms or refutes it. See
references/diagnosis-checklist.md. - Action plan. Specific fixes mapped to specific evidence.
- Communicate. Write up the diagnosis. Stakeholders want clarity, not exhaustive analysis.
Failure patterns
- Jumping to algorithm update. "It must be the algorithm" is the lazy answer. Eliminate technical and page-level causes first.
- Solving the wrong problem. A drop diagnosed as "content quality" when the real cause was an accidental noindex on a deploy. Validate the hypothesis before fixing.
- No baseline for "normal." Without a baseline, every fluctuation looks alarming. Establish what normal noise looks like before reacting.
- Treating one page as the site. Site-wide and page-level diagnoses are different. Confirm scope first.
- Ignoring branded vs non-branded. A drop in branded queries means a brand-level problem. A drop in non-branded means an SEO problem. Different teams own them.
- Comparing wrong date ranges. Comparing 28 days to the previous 28 days during a holiday distorts the picture. Use year-over-year for seasonal businesses.
- Stopping at correlation. A deploy and a drop on the same day is a strong correlation, not proof. Find the mechanism.
- Single-source diagnosis. Ahrefs sees position. Search Console sees clicks and queries. Analytics sees behavior. Logs see crawl. Use them together.
- Premature reassurance. Telling stakeholders "it is just an algorithm update, will recover" without evidence sets up a worse conversation later.
Output format
A diagnosis document with:
- Summary. What changed, when, magnitude, root cause in one paragraph.
- The symptom. Charts and segment breakdowns.
- Layer-by-layer findings. What each layer ruled in or out.
- Root cause hypothesis. Single statement with evidence.
- Action plan. Ordered fixes with owners and timelines.
- Recovery forecast. Realistic expectations on timeline and ceiling.
- Monitoring plan. What to watch for confirmation of recovery.
Length: 4-10 pages. Stakeholders read this fast.
If required data is unavailable
This skill's output depends on data, measurements, or tool results it cannot generate on its own. When a required input, tool, or data source is unavailable or unverifiable, the sanctioned output is the deliverable with the gap stated: what was needed, what was actually obtained or verified, and which parts of the output are affected. Fabricating, estimating, or interpolating a required number to complete the deliverable is never sanctioned. A stated gap is a complete answer.
Reference files
references/diagnosis-checklist.md- Layer-by-layer diagnostic checklist with the specific data to pull at each layer and how to interpret each signal.
