| name | google-ads-quality-score |
| description | Analyze and improve Google Ads Quality Score components. Use when diagnosing high CPCs, improving Ad Rank, analyzing Expected CTR, Ad Relevance, and Landing Page Experience, or benchmarking QS by industry. |
Google Ads Quality Score Optimizer
Deep-dive into Google Ads Quality Score (QS) mechanics, component analysis, improvement playbooks, historical tracking, and impact on CPC and Ad Rank. Includes GAQL queries, industry benchmarks, and actionable optimization workflows.
Capabilities
- QS Component Analysis - Break down Expected CTR, Ad Relevance, and Landing Page Experience
- Improvement Playbooks - Step-by-step guides for each QS component
- Historical QS Tracking - Methodology for tracking QS changes over time
- CPC and Ad Rank Impact - Quantify the financial impact of QS changes
- GAQL Queries - Ready-to-use queries for QS analysis
- Industry Benchmarks - QS benchmarks by vertical for goal-setting
Quality Score Fundamentals
What is Quality Score?
Quality Score is a 1-10 diagnostic metric that estimates the quality of your ads, keywords, and landing pages. It's calculated at the keyword level and composed of three sub-components.
Important: QS is a diagnostic tool, not a direct input to the auction. The actual auction uses real-time signals that are more granular than the 1-10 QS score.
The Three Components
| Component | Weight (est.) | What It Measures | Data Source |
|---|
| Expected CTR | ~39% | Likelihood of ad being clicked | Historical CTR, adjusted for position |
| Ad Relevance | ~22% | How well ad matches search intent | Keyword-to-ad text relevance |
| Landing Page Experience | ~39% | Post-click user experience | Page speed, content relevance, navigation |
Each component is rated: Above Average, Average, or Below Average
QS to Component Mapping (Approximate)
| QS | Expected CTR | Ad Relevance | Landing Page |
|---|
| 10 | Above Avg | Above Avg | Above Avg |
| 9 | Above Avg | Above Avg | Average |
| 8 | Above Avg | Average | Above Avg |
| 7 | Average | Above Avg | Above Avg |
| 7 | Above Avg | Above Avg | Below Avg |
| 6 | Average | Average | Above Avg |
| 6 | Above Avg | Average | Average |
| 5 | Average | Average | Average |
| 4 | Average | Below Avg | Average |
| 4 | Below Avg | Average | Average |
| 3 | Below Avg | Below Avg | Average |
| 3 | Average | Average | Below Avg |
| 2 | Below Avg | Below Avg | Below Avg |
| 1 | Below Avg | Below Avg | Below Avg |
QS Impact on CPC and Ad Rank
Ad Rank Formula
Ad Rank = Max CPC Bid × Quality Score × Expected Impact of Extensions
Simplified for QS analysis:
Ad Rank ≈ Bid × QS
Actual CPC Formula
Actual CPC = (Ad Rank of advertiser below you / Your Quality Score) + $0.01
QS Impact on CPC (Relative to QS 5 Baseline)
| Quality Score | CPC Modifier | CPC Impact |
|---|
| 1 | +400% | Pay 5× more |
| 2 | +150% | Pay 2.5× more |
| 3 | +67% | Pay 1.67× more |
| 4 | +25% | Pay 1.25× more |
| 5 | Baseline | Normal CPC |
| 6 | -17% | Save 17% |
| 7 | -29% | Save 29% |
| 8 | -38% | Save 38% |
| 9 | -44% | Save 44% |
| 10 | -50% | Save 50% |
Financial Impact Example
Current state:
Keywords: 500
Avg QS: 5.2
Monthly spend: $50,000
Avg CPC: $3.20
Clicks: 15,625
If improved to Avg QS 7.0:
CPC reduction: ~29%
New CPC: $2.27
Same spend → 22,026 clicks (+41%)
OR same clicks → $35,500 spend (-29%, saving $14,500/month)
GAQL Queries for QS Analysis
Query 1: Current QS Overview
SELECT
ad_group.name,
ad_group_criterion.keyword.text,
ad_group_criterion.keyword.match_type,
ad_group_criterion.quality_info.quality_score,
ad_group_criterion.quality_info.creative_quality_score,
ad_group_criterion.quality_info.post_click_quality_score,
ad_group_criterion.quality_info.search_predicted_ctr,
metrics.impressions,
metrics.clicks,
metrics.cost_micros,
metrics.conversions
FROM keyword_view
WHERE
ad_group_criterion.status = 'ENABLED'
AND campaign.status = 'ENABLED'
AND ad_group.status = 'ENABLED'
AND metrics.impressions > 100
AND segments.date DURING LAST_30_DAYS
ORDER BY ad_group_criterion.quality_info.quality_score ASC
Query 2: QS Distribution
SELECT
ad_group_criterion.quality_info.quality_score,
metrics.impressions,
metrics.clicks,
metrics.cost_micros,
metrics.conversions
FROM keyword_view
WHERE
ad_group_criterion.status = 'ENABLED'
AND campaign.status = 'ENABLED'
AND metrics.impressions > 0
AND segments.date DURING LAST_30_DAYS
Query 3: Below-Average Components (Priority Fixes)
SELECT
campaign.name,
ad_group.name,
ad_group_criterion.keyword.text,
ad_group_criterion.quality_info.quality_score,
ad_group_criterion.quality_info.search_predicted_ctr,
ad_group_criterion.quality_info.creative_quality_score,
ad_group_criterion.quality_info.post_click_quality_score,
metrics.cost_micros,
metrics.conversions
FROM keyword_view
WHERE
ad_group_criterion.status = 'ENABLED'
AND (
ad_group_criterion.quality_info.search_predicted_ctr = 'BELOW_AVERAGE'
OR ad_group_criterion.quality_info.creative_quality_score = 'BELOW_AVERAGE'
OR ad_group_criterion.quality_info.post_click_quality_score = 'BELOW_AVERAGE'
)
AND metrics.impressions > 50
AND segments.date DURING LAST_30_DAYS
ORDER BY metrics.cost_micros DESC
Query 4: High-Spend Low-QS Keywords (Biggest Savings)
SELECT
campaign.name,
ad_group.name,
ad_group_criterion.keyword.text,
ad_group_criterion.quality_info.quality_score,
metrics.cost_micros,
metrics.impressions,
metrics.clicks,
metrics.conversions,
metrics.average_cpc
FROM keyword_view
WHERE
ad_group_criterion.status = 'ENABLED'
AND ad_group_criterion.quality_info.quality_score < 5
AND metrics.cost_micros > 100000000
AND segments.date DURING LAST_30_DAYS
ORDER BY metrics.cost_micros DESC
LIMIT 50
Query 5: Historical QS (Using change events)
SELECT
change_event.change_date_time,
change_event.change_resource_type,
change_event.old_resource,
change_event.new_resource,
change_event.changed_fields
FROM change_event
WHERE
change_event.change_resource_type = 'AD_GROUP_CRITERION'
AND change_event.change_date_time DURING LAST_30_DAYS
ORDER BY change_event.change_date_time DESC
LIMIT 1000
Improvement Playbooks
Playbook 1: Expected CTR (Below Average → Above Average)
Root cause: Your ads get clicked less often than competitors for the same queries.
Step 1: Analyze current ad copy
- Pull all active RSA headlines and descriptions
- Compare to top competitors (use Ad Preview tool)
- Look for missing elements: numbers, urgency, CTAs, unique value props
Step 2: Headline optimization
Winning headline patterns:
[Number] + [Benefit]: "Save 40% on Enterprise Plans"
[Urgency] + [Action]: "Limited Time — Start Free Trial"
[Social Proof]: "Trusted by 50,000+ Companies"
[Direct Answer]: "Fast Business Insurance Quotes"
[Differentiation]: "No Contracts, Cancel Anytime"
Step 3: Improve ad extensions
| Extension | CTR Impact | Priority |
|---|
| Sitelinks (4+) | +10-20% CTR | Critical |
| Callouts (4+) | +5-10% CTR | High |
| Structured snippets | +3-8% CTR | High |
| Call extension | +5-15% CTR (mobile) | High (if phone business) |
| Price extensions | +5-10% CTR | Medium |
| Image extensions | +10-15% CTR | High |
| Lead form | Variable | Test for lead gen |
Step 4: Match type alignment
- Exact match keywords should have highly specific ads
- Phrase match should have ads covering the concept
- Broad match should use responsive search ads with maximum headlines
Step 5: RSA pin strategy
- Pin your best-performing headline to Position 1
- Pin brand name headline to Position 2 or 3
- Don't pin descriptions (let Google optimize)
- Monitor "Ad strength" indicator (aim for "Good" or "Excellent")
Timeline: 2-4 weeks to see Expected CTR improvement after changes.
Playbook 2: Ad Relevance (Below Average → Above Average)
Root cause: Your ad text doesn't closely match the search intent of the keyword.
Step 1: Audit keyword-to-ad alignment
For each Below Average keyword:
- Read the keyword
- Read all headlines in the ad group's RSAs
- Ask: "Does ANY headline contain this keyword or a close synonym?"
Step 2: Restructure ad groups
Tightly themed ad groups (STAG method):
Bad structure:
Ad Group: "Shoes"
Keywords: running shoes, dress shoes, hiking boots, sandals
→ Single ad can't be relevant to all of these
Good structure:
Ad Group: "Running Shoes"
Keywords: running shoes, buy running shoes, best running shoes
Ad: Headlines include "Running Shoes" prominently
Ad Group: "Dress Shoes"
Keywords: dress shoes, formal shoes, men's dress shoes
Ad: Headlines include "Dress Shoes" prominently
Step 3: Keyword insertion (use sparingly)
Headline: {KeyWord:Quality Shoes}
This dynamically inserts the user's search term into the headline.
Use only when keyword grouping is tight. Avoid for brand-sensitive contexts.
Step 4: RSA headline variety with keyword focus
- Include the primary keyword verbatim in 2-3 headlines
- Include synonyms and related terms in 3-4 more headlines
- Include unique value props in remaining headlines
- Each headline should be distinct (no near-duplicates)
Timeline: 1-2 weeks for Ad Relevance to update after restructuring.
Playbook 3: Landing Page Experience (Below Average → Above Average)
Root cause: Your landing page doesn't meet user expectations after clicking the ad.
Step 1: Page speed audit
| Metric | Target | Tool |
|---|
| Largest Contentful Paint (LCP) | < 2.5s | PageSpeed Insights |
| First Input Delay (FID) | < 100ms | PageSpeed Insights |
| Cumulative Layout Shift (CLS) | < 0.1 | PageSpeed Insights |
| Time to First Byte (TTFB) | < 800ms | WebPageTest |
| Mobile speed score | > 50 | PageSpeed Insights |
Step 2: Content relevance
- Page headline must match ad headline closely
- Keywords from the ad group should appear in the page content
- The page should deliver on the ad's promise immediately (above the fold)
- Remove interstitials, pop-ups, and distractions
Step 3: Mobile experience
- Responsive design (no horizontal scrolling)
- Touch-friendly buttons (min 48×48px)
- Readable text without zooming (16px+ body font)
- Fast-loading images (WebP, lazy loading)
Step 4: Trust signals
- SSL certificate (HTTPS required)
- Privacy policy and terms of service links
- Contact information visible
- Social proof (reviews, testimonials, logos)
- Secure payment badges (if e-commerce)
Step 5: Navigation and transparency
- Clear navigation structure
- Easy-to-find contact information
- Transparent pricing (no hidden fees)
- Clear return/refund policy
Timeline: 4-8 weeks for Landing Page Experience to update (Google re-crawls landing pages periodically).
Historical QS Tracking Methodology
Why Track QS Over Time?
Google doesn't provide historical QS reports. You must build your own tracking system.
Method: Weekly QS Snapshot
- Every Monday, run GAQL Query 1 (Current QS Overview)
- Store results in a spreadsheet or database with columns:
- Date, Campaign, Ad Group, Keyword, QS, Expected CTR, Ad Relevance, LP Experience, Impressions, Cost
- Calculate weekly aggregates:
- Impression-weighted avg QS = Σ(QS_i × Impressions_i) / Σ(Impressions_i)
- % of keywords with QS ≥ 7
- % of spend on QS ≥ 7 keywords
Key Tracking Metrics
| Metric | Target | Calculation |
|---|
| Impression-weighted QS | ≥ 7.0 | Σ(QS × Impr) / Σ(Impr) |
| % keywords QS ≥ 7 | > 60% | Count(QS ≥ 7) / Total keywords |
| % spend on QS ≥ 7 | > 75% | Spend(QS ≥ 7) / Total spend |
| % keywords QS ≤ 3 | < 5% | Count(QS ≤ 3) / Total keywords |
| QS improvement rate | Positive trend | Week-over-week change |
Industry Benchmarks
Average Quality Score by Industry
| Industry | Avg QS | Expected CTR (% Above) | Ad Relevance (% Above) | LP (% Above) |
|---|
| E-commerce | 6.5 | 45% | 55% | 40% |
| SaaS / Technology | 6.8 | 50% | 60% | 50% |
| Finance / Insurance | 5.8 | 35% | 45% | 35% |
| Healthcare | 6.2 | 40% | 50% | 40% |
| Legal | 5.5 | 30% | 40% | 30% |
| Real Estate | 6.0 | 40% | 45% | 35% |
| Education | 6.7 | 48% | 55% | 50% |
| Travel | 6.3 | 42% | 50% | 45% |
| Local Services | 6.1 | 38% | 48% | 38% |
| B2B Services | 6.4 | 42% | 52% | 42% |
QS Targets by Keyword Type
| Keyword Type | Target QS | Rationale |
|---|
| Brand keywords | 8-10 | You ARE the most relevant result |
| High-intent transactional | 7-9 | Strong ad + landing page alignment |
| Non-brand generic | 5-7 | Competitive, harder to differentiate |
| Informational long-tail | 4-6 | Often mismatched landing pages |
| Competitor keywords | 3-5 | Intentionally lower (not your brand) |
Examples
Example 1: High-Spend Low-QS Rescue
Scenario: Top 10 keywords by spend have avg QS of 4.2. Monthly spend: $25K on these keywords alone.
Analysis:
| Keyword | QS | eCTR | AdRel | LP | Spend/mo |
|---|
| "project management software" | 4 | Below | Avg | Below | $4,200 |
| "task management tool" | 3 | Below | Below | Below | $3,800 |
| "team collaboration app" | 5 | Avg | Avg | Avg | $3,500 |
| "workflow automation" | 4 | Avg | Below | Below | $3,200 |
| ... | ... | ... | ... | ... | ... |
Action plan:
- Landing pages (LP Below × 4): Rebuild landing pages for top keywords. Add keyword in H1, speed optimization, mobile fixes. Timeline: 2 weeks.
- Ad copy (eCTR Below × 3): Add 5 new headlines per ad group with keyword in headline, add all extensions. Timeline: 1 week.
- Ad groups (AdRel Below × 3): Split "task management" and "workflow" into separate ad groups with tailored ads. Timeline: 1 week.
Projected impact (6-8 weeks):
- QS improvement: 4.2 → 6.5
- CPC reduction: ~25%
- Savings: ~$6,250/month
- Or: 33% more clicks for same budget
Example 2: QS Monitoring Dashboard
Weekly report format:
Week of March 1, 2026
━━━━━━━━━━━━━━━━━━━
Overall QS: 6.4 (↑0.2 from last week)
Keywords tracked: 342
QS Distribution:
QS 8-10: 78 keywords (23%) — $12,400 spend
QS 6-7: 145 keywords (42%) — $22,800 spend
QS 4-5: 89 keywords (26%) — $8,200 spend
QS 1-3: 30 keywords (9%) — $1,600 spend
Component Health:
Expected CTR — 55% Above Avg (↑3%)
Ad Relevance — 62% Above Avg (↑1%)
Landing Page — 48% Above Avg (↑5%)
Top Improvements This Week:
"crm software" — QS 4→6 (LP fix deployed)
"email marketing" — QS 5→7 (new ad copy)
Priority Fixes Next Week:
"marketing automation" — QS 3, LP Below, $2,100/mo spend
"sales pipeline" — QS 4, eCTR Below, $1,800/mo spend
Example 3: Competitor Keyword QS Strategy
Scenario: Bidding on 50 competitor brand keywords. Avg QS: 3.1. Should we continue?
Analysis:
- Competitor keywords inherently have lower QS (Google prefers the brand owner)
- Expected QS range for competitor terms: 3-5
- CPC premium: 2-3× higher than non-brand generics
Decision framework:
| Condition | Action |
|---|
| CPA on competitor terms < 1.5× non-brand CPA | Continue, it's profitable |
| CPA on competitor terms 1.5-2.5× non-brand CPA | Selective — keep top 10 converters |
| CPA on competitor terms > 2.5× non-brand CPA | Pause and reallocate budget |
QS improvement for competitor keywords:
- Use competitor comparison landing pages
- Headlines: "Better Than [Competitor]" or "[Your Brand] vs [Competitor]"
- Include comparison tables, switch-from guides
- Realistic target QS: 4-5 (not 7+)
About this skill
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