seo-reporting-measurement

Files (3)

• SKILL.md
• ai-metrics-reporting.md · references/
• algorithm-update-playbook.md · references/

SKILL.md (first lines)

## Why this matters

- **GSC data is ~75% incomplete.** Google filters approximately 75% of search impressions for "privacy" (industry analysis, Feb 2026). Single-source GSC decisions are unreliable.
- **Multi-touch attribution reveals 30-60% more organic value than last-click models** — which is why most SEO teams under-report their actual business contribution.
- **GSC branded query filter** (released November 2025) finally enables native branded vs non-branded segmentation — one of the biggest reporting improvements of the last five years.
- **Median publisher: 10% YoY decline in organic traffic H1 2025** despite maintained visibility — this is the zero-click effect showing up in the numbers. Report on impressions and rankings, not just clicks.
- **AI visibility is probabilistic.** <1% chance of same brand list from same prompt; <0.1% chance of same order (multi-run replication studies). All AI metrics must be reported with sample sizes and confidence notes.
- **Zero-click reality:** 58.5% of US searches and 77.2% of mobile searches end without a click (third-party search studies, 2025). Click-based KPIs alone understate visibility.

## Process

1. **Data foundation audit.** Is GSC connected? Is GA4 set up correctly? Are conversions tracked? Without this, nothing else matters.
2. **Define the KPI hierarchy.** Leading → core → business → AI visibility. Each layer has its own cadence and audience.
3. **Build attribution.** Default to multi-touch when possible. Document the model and its known limitations.
4. **Structure the reports.** Weekly tactical, monthly strategic, quarterly deep-dive. Each has a specific audience and purpose.
5. **Handle algorithm updates.** When Google updates or an AI model shifts, reports need to flag and contextualize — not just show numbers going up or down.
6. **Report on AI visibility with appropriate caveats.** Sample sizes, platform breakdowns, quarterly trends.
7. **Continuously refine.** Retire metrics that don't drive decisions; add metrics that do.

## Frameworks

### Critical data caveat to lead every report with

> **⚠️ GSC impression data is approximately 75% incomplete.** Google filters most search impressions for privacy. Single-source GSC decisions are unreliable. All figures below should be treated as *directional trends*, not absolute numbers. Cross-reference with third-party tools and focus on *deltas over time*, not point values.

This is not boilerplate — stakeholders consistently misinterpret SEO reports when they don't understand the data limits. Lead every report with this caveat.

### KPI hierarchy

Organized from **leading** (early signals) to **lagging** (business outcomes). Each tier has different review cadence and different audiences.

**Leading indicators (weekly review):**
- Impressions (GSC)
- CTR by position band
- Scroll depth by landing page type
- Time on page by intent segment
- Indexed pages count (GSC Coverage)
- Core Web Vitals field scores (GSC)
- Crawl stats (GSC Crawl Stats report)
- New issues detected in GSC

**Core SEO metrics (monthly review):**
- **Organic sessions segmented by intent** (not aggregate) — informational/commercial/transactional
- **Branded vs non-branded traffic** — use GSC branded query filter (November 2025+)
- **Ranking distribution** — top 3, top 10, top 20 keyword counts
- **New vs lost keywords** — velocity of keyword portfolio
- **SERP feature presence** — featured snippets, PAA, AI Overviews
- **Internal link coverage** on priority pages
- **Content freshness** — how many priority pages updated in last quarter

**Business metrics (monthly review, executive audience):**
- **Organic-attributed revenue** (multi-touch model preferred)
- **Lead generation from organic**
- **Conversion rate by landing page type**
- **Customer acquisition cost from organic**
- **Lifetime value from organic customers** (if tracked)

**AI visibility metrics (quarterly review minimum):**
- **Brand mention frequency** across platforms (60-100+ runs per prompt)
- **AI referral traffic** (source/medium reports in GA4)
- **Citation rate** — how often your domain is cited across AI queries
- **Competitor share-of-voice in AI**
- **Per-platform visibility** — never collapse into a single score

### Metrics to retire

Per industry reporting, these metrics should NOT be used as primary KPIs:

| Metric | Why retire it | What to use instead |
|--------|--------------|---------------------|
| **Organic traffic as standalone KPI** | Lacks intent context; treats all traffic as equal | Organic sessions segmented by intent |
| **Average keyword position** | Meaningless in aggregate (averaging ranks across different queries is statistically incoherent) | Ranking distribution buckets (top 3, top 10, top 20) |
| **Domain authority as a business metric** | A third-party proxy score, not a Google metric | Referring domain count and quality, actual ranking performance |
| **Bounce rate in isolation** | GA4 redefined it; often misinterpreted | Engagement rate + task completion signals |
| **Single-run AI visibility "score"** | Non-deterministic; meaningless without sample size | Visibility % across 60+ runs per prompt per platform |
| **Individual meta description CTR** | Google rewrites ~60% of meta descriptions; you're measuring Google's rewrite, not yours | Page-level CTR trends with content changes logged |

### Revenue attribution formula

```
Monthly Organic Value =
  (Organic Sessions × Conversion Rate × Average Order Value)
  +
  (Lead Gen Conversions × Lead Value × Close Rate)
```

**Important:** This is a last-click formula. Multi-touch attribution typically reveals 30-60% more organic value than last-click. If you can, use GA4's data-driven attribution model or build a custom model that credits organic for its role in multi-touch journeys.

**Document your model.** Every report should state which attribution model is used and its known limitations. Stakeholders will ask why "SEO revenue" differs from "paid social revenue" — the model is the answer.

### AI visibility measurement protocol (reporting layer)

When reporting AI visibility to stakeholders, **every metric must include sample size and the measurement protocol**. See `references/ai-metrics-reporting.md` for the detailed reporting patterns.

Core rules:

- **Sample size minimum:** 60 runs per prompt per platform. Anything less is noise.
- **Never report "rank position" in AI output.** <0.1% consistency — it's not a stable metric.
- **Report per-platform, not aggregated.** 86% of top-cited sources are platform-unique; aggregating hides the pattern.
- **Quarterly cadence** is the default; monthly if you're actively optimizing.
- **Flag the confidence** of every AI metric: Data-backed, Directional, or Experimental.

### Algorithm update assessment framework

When Google rolls out a core update or an AI platform shifts behavior:

1. **Pause before reporting.** Google core updates take 2-4 weeks to fully roll out. Initial ranking shifts are not final.
2. **Segment the impact.** Don't report "traffic is down 12%." Break it down: which pages? which intent buckets? which query types?
3. **Cross-reference with known changes.** Was there a recent migration? A new competitor? A CWV regression? Update impact gets blamed for unrelated issues constantly.
4. **Compare to industry.** If traffic dropped 12% and the industry dropped 15%, you're relatively stable. If the industry is flat and you dropped 12%, it's specific to your site.
5. **Flag what you don't know.** "Some of this may be algorithm-driven and some may be zero-click expansion from AI Overviews — it will take 30-60 days to distinguish."
6. **Recommend observation windows, not reactive changes.** The worst thing post-update is to start rewriting content before the update finishes rolling out.

See `references/algorithm-update-playbook.md` for the full diagnostic playbook.

### Report structure template

Adapt to the audience. Executives want the one-pager; SEO teams want the detail.

From narayan-metaflow/metaflow-marketing-skills, MIT.

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