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# Google Ads Deep Analysis

## Process

1. Collect Google Ads account data (export, Change History, Search Terms Report)
2. **Validate**: confirm data covers ≥30 days and includes Search Terms Report before proceeding
3. Read `ads/references/google-audit.md` for full 80-check audit
4. Read `ads/references/benchmarks.md` for Google-specific benchmarks
5. Read `ads/references/scoring-system.md` for weighted scoring
6. Evaluate all applicable checks as PASS, WARNING, or FAIL
7. **Validate**: confirm all 80 checks evaluated before calculating score
8. Calculate Google Ads Health Score (0-100)
9. Generate findings report with action plan

## What to Analyze

### Conversion Tracking (25% weight)
- Google tag (gtag.js) installed and firing on all pages
- Enhanced Conversions active (hashed first-party data)
- Consent Mode v2 implemented (required for EU/EEA)
- Conversion actions mapped correctly (primary vs secondary)
- Offline conversion import configured (for lead gen)
- Server-side tagging via GTM (recommended for accuracy)
- Attribution model: data-driven preferred (last-click as fallback only)
- Conversion lag analysis (are conversions still trickling in?)

### Wasted Spend (20% weight)
- Search Terms Report reviewed (last 30 days minimum)
- Negative keyword coverage adequate (shared lists + campaign-level)
- Display placement audit (exclude low-quality sites)
- Invalid click rate within norms (<10%)
- Broad Match only used with Smart Bidding (NEVER without it)
- Brand/non-brand campaigns separated
- Geographic targeting precise (no wasted international spend)

**Negative Keyword Rules (critical: bad negatives kill campaigns):**
- NEVER suggest Broad Match negatives unless explicitly justified; they block too broadly
- Default to **Exact Match** `[keyword]` for specific irrelevant queries
- Use **Phrase Match** `"keyword"` for irrelevant intent patterns
- Source negatives from actual Search Terms Report irrelevant queries, NOT guesses
- Group into themed lists: Informational (how-to, DIY, what is), Job-seeker (jobs, careers, salary), Competitor (only if intentionally excluded), Free-intent (free, crack, torrent)
- Recommend **Shared Negative Lists** at the account level, not just campaign-level
- Review existing negatives for over-blocking (are any negatives accidentally blocking converting queries?)

### Account Structure (15% weight)
- Campaign-level organization follows business logic
- Ad groups themed tightly (15-20 keywords max per group)
- RSA ad groups have ≥3 active ads
- PMax campaigns structured correctly (asset groups, signals)
- SKAGs evaluated (migrate to themed groups if present)
- Campaign labels/naming conventions consistent

### Keywords (15% weight)
- Match type strategy appropriate (Exact → Phrase → Broad progression)
- Quality Score distribution (aim ≥7 average)
- Low QS keywords flagged (<5 = FAIL, 5-6 = WARNING)
- Keyword cannibalization check (same keywords in multiple campaigns)
- Impression share tracked for top keywords
- Keyword bid adjustments set for devices/locations/audiences

### Ads (15% weight)
- RSA: ≥8 unique headlines, ≥3 descriptions per ad group
- RSA: ad strength "Good" or "Excellent" (not "Poor" or "Average")
- Pin usage minimal and strategic (over-pinning reduces RSA flexibility)
- Ad extensions: sitelinks (≥4), callouts (≥4), structured snippets, image
- Dynamic keyword insertion used appropriately
- Ad copy includes CTA, value proposition, differentiators

### Settings (10% weight)
- ECPC (Enhanced CPC) flagged as deprecated. Migrate to full Smart Bidding (tCPA/tROAS/Maximize)
- Bid strategy appropriate for campaign maturity and goals
- Budget pacing: no campaigns limited by budget (unless intentional)
- Ad schedule aligned with business hours/conversion patterns
- Device bid adjustments set based on performance data
- Location targeting: "Presence" not "Presence or Interest"
- Network settings: Search Partners reviewed, Display opt-out for Search

## GAQL & Data Accuracy

Before analyzing data, read `ads/references/gaql-notes.md` for known GAQL field incompatibilities,
deduplication patterns, and filter scope best practices. Key rules:

- Deduplicate keywords by `(ad_group_id + keyword_text + match_type)` before any analysis
- Only analyze ENABLED campaigns and ad groups (exclude paused/removed)
- Filter to keywords with impressions > 0 for theme coherence checks (G03)
- Apply legacy BMM heuristic: BROAD + Manual CPC = legacy BMM, not intentional broad (G17)
- Only flag wasted spend on terms with >$10 spend AND 0 conversions (G16)
- Count shared negative keyword lists alongside campaign-level negatives (G14/G15)

## Google Ads MCP Integration (Optional)

For automated data collection, connect the [Google Ads MCP server](https://github.com/googleads/google-ads-mcp):

- **Tools available**: `search` (GAQL queries), `list_accessible_customers`
- **Setup**: Configure in `.mcp.json` or Claude Code MCP settings
- **Customer ID**: Extract from CLAUDE.md under Accounts > Google Ads, or ask the user
- **Fallback**: If MCP is not configured, fall back to manual data export (the default workflow)

When MCP is available, use it to pull Search Terms Reports, keyword data, conversion actions,
and campaign structure automatically instead of requiring manual exports.

## PMax Deep Dive

If Performance Max campaigns exist, additionally evaluate:
- Asset group diversity (text, images, video, feeds)
- Audience signals configured (custom segments, lists, demographics)
- URL expansion settings reviewed (opt-out of irrelevant pages)
- Brand exclusions applied (prevent cannibalizing brand search), available for all advertisers
- Campaign-level negative keywords now available for ALL advertisers
- Search themes utilized (2024 feature)
- Final URL expansion: enabled or disabled with justification
- Insights tab reviewed (search categories, audience segments)

## AI Max for Search (2026)

AI Max layers broad match + keywordless targeting on existing Search campaigns.
14% avg conversion lift. DSA likely consolidated into AI Max Q2 2026.
Requires strong negative keyword lists.

From yahav123147/paid-ads-cro-skills, MIT.

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