Google Ads Account Audit

Perform comprehensive Google Ads audits producing prioritized, actionable recommendations.

Workflow Overview

1. Data intake – Load and parse Google Ads export data
2. Structure analysis – Evaluate naming conventions and organization
3. Quality Score audit – Calculate weighted QS, find optimization opportunities
4. Keyword & Search Term analysis – Identify waste and scaling opportunities
5. Ad evaluation – Check RSA adoption, Ad Strength, relevance
6. Extensions audit – Verify coverage across campaigns
7. Display/Placement review – Analyze placement distribution (if applicable)
8. Landing page assessment – Evaluate relevance and best practices
9. Anomaly detection – Flag unusual patterns
10. Generate outputs – Create roadmap document + Excel summary

Data Sources

Option 1: Manual CSV Export

Request these reports from Google Ads (CSV/Excel):

Option 2: Google Ads API (Recommended)

For automated data extraction, see references/google-ads-api.md.

Benefits:

• Complete data without manual export
• Consistent column naming
• Can include historical comparisons
• Automatable for recurring audits

Analysis Modules

1. Account Structure & Naming

Evaluate naming conventions for clarity:

• Brand vs Non-brand – Clear separation (e.g., [Brand], [NB], _brand_, _generic_)
• Campaign types – Identifiable (Search, Display, PMax, Shopping)
• Geographic/Language – If applicable, marked in names
• Consistency – Same pattern across account

Output: Structure score (1-10) + specific naming issues

2. Quality Score Analysis

See references/quality-score.md for weighted average calculation and distribution analysis.

Key outputs:

• QS Distribution: Cost/Conv % by QS level (1-10)
• Efficiency analysis: Low QS (1-6) vs High QS (7-10) spend efficiency
• Weighted QS by campaign and ad group
• High-spend + low-QS keywords (improvement opportunities)
• QS component breakdown (Expected CTR, Ad Relevance, Landing Page)
• Estimated savings from QS improvements

Typical finding: 31% cost on QS < 7 yields only 19% conversions

3. Keywords & Search Terms

See references/keyword-analysis.md for detailed methodology.

Identify:

• Pause candidates: High spend, low/no conversions, high CPA
• Scale candidates: Low CPA + low impression share
• Zero-impression keywords: No activity in analysis period
• Keyword overlap: Same keywords across ad groups
• Search term relevance: Deviation from target keywords

3b. Match Type & Cross-Campaign Overlap

See references/match-type-overlap.md for detailed analysis.

Match Type Performance:

• Analyze cost vs conversion distribution by match type
• Calculate efficiency ratio (Conv% / Cost%)
• Typical finding: Exact match delivers 70% conv for 45% cost

Cross-Campaign Overlap:

• Detect same search terms triggering multiple campaigns
• Identify brand terms leaking to generic campaigns
• Find cannibalization between campaigns

Recommendations focus:

• Increase exact match keyword coverage
• Add negative keywords for brand protection
• Use phrase match for specific, high-intent terms

4. Ads Evaluation

See references/ads-evaluation.md for detailed methodology.

RSA Count Analysis:

• Check RSA count per ad group (target: 1-2 max)
• Flag ad groups with 3+ RSAs (data dilution)

Ad Strength Distribution:

• Analyze spend % by Ad Strength rating
• Typical issue: 52% spend on Poor RSAs, only 5% on Excellent/Good
• Priority: Optimize RSAs with high spend + low strength

Check for each campaign/ad group:

• RSA adoption (best practice = RSA only, no legacy ETA)
• Ad Strength rating (Poor/Average/Good/Excellent)
• Number of headlines (target: 15) and descriptions (target: 4)
• Pin usage (excessive pinning = Bad)

LLM Relevance Check: Compare ad copy to ad group keywords:

• Headlines contain target keywords or close variants?
• Descriptions address user intent?
• CTAs present and clear?

Recommendations:

• Optimize Poor/Average RSAs to Good/Excellent
• Add more headlines/descriptions with keywords
• Reduce RSA count to 1-2 per ad group
• Keep high-performing legacy ETAs if data supports

5. Extensions Audit

See references/extensions-placements.md for coverage matrix and best practices.

Required extensions per campaign type:

Output: Missing extensions matrix

6. Display Placements (if applicable)

See references/extensions-placements.md for placement categorization.

Analyze placement distribution:

• % spend on apps vs websites vs YouTube
• High-spend low-converting placements
• Placement exclusion recommendations

7. Landing Pages

Evaluate:

• URL diversity (one URL vs personalized per ad group)
• HTTPS usage
• Page load indicators (if available)
• Keyword relevance to landing page (from URL/path analysis)

8. Rejected Items

List all disapproved:

• Ads
• Keywords
• Extensions

With rejection reasons and fix recommendations.

9. Anomaly Detection

Flag:

• Sudden spend spikes in search terms
• New high-volume search terms
• CTR anomalies (very high or very low)
• CPA outliers

Key Metrics Reference

Output Generation

1. Executive Summary (Markdown/DOCX)

Structure:

# Google Ads Audit: [Account Name]
Date: [Date]

## Executive Summary
[2-3 sentence overview of account health]

## Priority Roadmap
### Immediate (This Week)
1. [Action] - [Impact] - [Effort]
...

### Short-term (This Month)
...

### Medium-term (This Quarter)
...

## Detailed Findings
[Section per analysis module]

## Appendix
[Methodology notes]

2. Excel Workbook

Create workbook with sheets:

• Summary – Key metrics dashboard
• Structure – Naming issues
• Quality Score – Weighted QS by campaign/ad group
• Keywords – Pause/scale recommendations
• Search Terms – Top spenders + relevance
• Ads – Ad Strength + recommendations
• Extensions – Coverage matrix
• Placements – If Display campaigns present
• Roadmap – Prioritized actions

See references/excel-template.md for column specifications.

Priority Scoring

Score each recommendation:

• Impact: High (3) / Medium (2) / Low (1)
• Effort: Low (3) / Medium (2) / High (1)
• Priority Score = Impact × Effort

Sort roadmap by priority score descending.