Ads Google

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-shared/references/google-audit.md for full 74-check audit
4. Read ads-shared/references/benchmarks.md for Google-specific benchmarks
5. Read ads-shared/references/scoring-system.md for weighted scoring
6. Evaluate all applicable checks as PASS, WARNING, or FAIL
7. Validate: confirm all 74 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 ( 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:

• 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)
• 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)

If AI Max for Search is available/active:

• Broad Match + AI Max integration evaluated
• Auto-generated headline performance monitored
• Search term categories reviewed for relevance
• Budget impact assessed (AI Max can shift spend)

Key Thresholds

| Metric | Pass | Warning | Fail | |--------|------|---------|------| | Quality Score (avg) | ≥7 | 5-6 | $8.00 | | Wasted Spend | 20% | | Ad Strength | Good+ | Average | Poor | | Invalid Clicks | 10% |

Output

Google Ads Health Score

Google Ads Health Score: XX/100 (Grade: X)

Conversion Tracking: XX/100  ████████░░  (25%)
Wasted Spend:        XX/100  ██████████  (20%)
Account Structure:   XX/100  ███████░░░  (15%)
Keywords:            XX/100  █████░░░░░  (15%)
Ads:                 XX/100  ████████░░  (15%)
Settings:            XX/100  ██████████  (10%)

Deliverables

• GOOGLE-ADS-REPORT.md: Full 74-check findings with pass/warning/fail
• Wasted spend estimate (monthly $ value)
• Quick Wins sorted by impact
• PMax-specific recommendations (if applicable)
• Keyword health matrix with QS, CTR, CVR per keyword group

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: citedy
• Source: citedy/adclaw
• License: Apache-2.0
• Homepage: https://pypi.org/project/adclaw/

Install and usage instructions live in the source repository linked above.