How to Optimize Google Ads Quality Score Step by Step

Quality Score is Google’s 1-10 diagnostic rating of how relevant your keywords, ads, and landing pages are to a user’s search. Higher Quality Scores reduce your cost per click and improve ad position. This guide breaks down each component and provides specific strategies to improve them.

Key Takeaways

• Quality Score is built from three components: Expected CTR, Ad Relevance, and Landing Page Experience
• Focus improvements on components rated “Below Average” first — they drag down the overall score most
• Tight ad group theming is the single most impactful structural change for Ad Relevance
• Landing page speed improvements benefit both Quality Score and conversion rates
• Track Quality Score distribution over time, not individual keyword scores

Introduction

Quality Score directly impacts how much you pay per click and how often your ads show. A keyword with a Quality Score of 10 can pay up to 50% less per click than the same keyword with a Quality Score of 5, while appearing in a higher position. At scale, this difference compounds into significant cost savings or wasted spend.

Google calculates Quality Score using three components, each rated as “Above Average,” “Average,” or “Below Average”:

The optimization strategy is straightforward: identify which components are below average and fix those first. Improving a component from “Below Average” to “Average” has a larger impact than moving from “Average” to “Above Average.”

Step 1: Audit Your Current Quality Score Distribution

Before making changes, establish a baseline. Individual keyword Quality Scores fluctuate, but the distribution across your account reveals systemic issues.

Pull the report:

1. In Google Ads, navigate to Campaigns > Keywords > Search keywords
2. Click “Columns” and add: Quality Score, Expected CTR, Ad Relevance, Landing Page Experience
3. Also add Historical Quality Score columns to see trends
4. Export the data for analysis

Assess the distribution:

Identify component patterns:

Look at the component breakdown across your below-average keywords. If most show “Below Average” for the same component, you have a systemic issue rather than individual keyword problems:

• Mostly Expected CTR below average — Your ad copy needs work across the board
• Mostly Ad Relevance below average — Your ad group structure is too broad
• Mostly Landing Page below average — Your site experience needs improvement

Doing this analysis across a large account with hundreds or thousands of keywords is tedious but critical. Lyra’s Campaign Health Analyzer automatically scores Quality Score distribution across your entire account and flags the specific component dragging scores down, so you know exactly where to focus.

Step 2: Improve Expected CTR with Better Ad Copy

Expected CTR predicts how likely users are to click your ad when it shows for a given keyword. Google compares your historical CTR against other advertisers bidding on the same keyword, normalized for ad position.

Strategies to improve Expected CTR:

1. Include the keyword in headlines — Users scan for relevance. Ads that echo the search query in the first headline get higher click rates.

2. Write benefit-driven copy — Features tell, benefits sell. “Free Shipping on All Orders” outperforms “We Offer Shipping Services.”

3. Use numbers and specifics — “Save 40% This Week” beats “Save Money Today.” Specificity signals credibility.

4. Add all relevant ad extensions — Sitelinks, callouts, structured snippets, and call extensions increase your ad’s visual footprint and provide more reasons to click.

5. Test multiple RSA variations — Provide at least 12 unique headlines and 4 descriptions per RSA. Avoid redundant messaging — each headline should offer a distinct value proposition or angle.

6. Use countdown customizers — For time-sensitive offers, countdown customizers (“Sale Ends in 3 Days”) create urgency and boost CTR.

What does not help Expected CTR:

• Bidding higher (Google normalizes for position)
• Clickbait headlines (high CTR but poor post-click metrics hurt Landing Page Experience)
• Excessive keyword insertion (can create awkward, unnatural ad text)

Step 3: Fix Ad Relevance Through Tighter Ad Group Structure

Ad Relevance measures how well your ad copy matches the intent behind the keyword. The most common cause of low Ad Relevance is ad groups that are too broad — containing keywords with different intents served by the same generic ad.

The solution is tighter ad group theming.

Example of a poorly structured ad group:

Ad group “Running Shoes” containing: running shoes, trail running shoes, running shoes for women, best running shoes for flat feet, cheap running shoes, running shoe store near me

These keywords have different intents. A single ad cannot be highly relevant to all of them.

Restructured into themed ad groups:

Each ad group gets tailored ad copy that directly addresses the keyword’s intent. This structural change alone often moves Ad Relevance from “Below Average” to “Above Average.”

Guidelines for ad group sizing:

• 10-20 keywords per ad group is the sweet spot
• All keywords in an ad group should share the same intent
• If you cannot write one ad that is relevant to all keywords in the group, the group is too broad
• Use the Responsive Search Ad “Ad Strength” indicator as a signal — “Poor” often indicates a relevance mismatch

Step 4: Optimize Landing Page Experience

Landing Page Experience evaluates how useful, relevant, and navigable your landing page is for users who click your ad. Google assesses this through a combination of algorithmic analysis and user behavior signals.

Factors that influence Landing Page Experience:

1. Content relevance — The page should directly address the keyword’s intent. If someone searches “running shoes for flat feet” and lands on a generic shoes homepage, that is a relevance mismatch. Create dedicated landing pages for your highest-spend keywords.

2. Page load speed — Google has confirmed that page speed is a factor. Target a Largest Contentful Paint (LCP) under 2.5 seconds and First Input Delay under 100ms.

   Quick speed wins:

   • Compress images (use WebP format)
   • Enable browser caching
   • Minimize render-blocking JavaScript
   • Use a CDN for static assets

3. Mobile optimization — Over 60% of Google searches happen on mobile. Your landing page must be fully responsive with readable text, tap-friendly buttons, and no horizontal scrolling.

4. Transparency and trust — Include clear contact information, privacy policy, and business details. Google penalizes pages that feel untrustworthy or lack transparency about the business behind them.

5. Navigation ease — Users should be able to find what they need without excessive scrolling or clicking. The above-the-fold content should immediately confirm they are in the right place.

Landing page audit checklist:

Step 5: Monitor, Test, and Iterate

Quality Score optimization is not a one-time project. Competitive dynamics change, landing pages get updated, and new ad copy variants emerge. Build a monitoring cadence.

Weekly checks:

• Review any keywords that dropped below Quality Score 5
• Check component breakdowns for newly flagged issues
• Review RSA asset performance reports — replace “Low” performing headlines and descriptions

Monthly analysis:

• Compare Quality Score distribution against the previous month
• Calculate the cost impact: keywords with QS 4-5 are paying roughly 25-50% more per click than they would at QS 7-8
• Identify new keyword additions that launched with low Quality Scores and address them before the scores solidify

Quarterly deep dive:

• Audit landing page speed (sites slow down over time as content and scripts accumulate)
• Review ad group structure — keyword additions and match type changes can cause groups to drift from their original theme
• Test new landing page variants for your highest-spend, lowest-QS keywords

Tracking these metrics manually across a large account requires significant spreadsheet work. Lyra’s Campaign Health Analyzer generates Quality Score diagnostics automatically, breaking down component-level performance across your account and trending changes over time. It flags keywords where Quality Score degradation is costing you the most in excess CPC.

Practical Example

An e-commerce account selling outdoor gear has 500 keywords. The initial audit reveals:

Ad Relevance is the clear priority. Investigation shows that 8 ad groups contain 40+ keywords each with mixed intents.

Actions taken:

1. Split the 8 broad ad groups into 24 themed groups with 10-15 keywords each
2. Wrote custom RSAs for each new group, with headlines matching the keyword theme
3. Left Landing Page Experience alone (already strong)
4. Made minor CTR improvements by adding callout extensions across all campaigns

Results after 6 weeks:

The account saved $2,650/month while getting more impressions, simply by fixing ad group structure.

Common Mistakes

• Obsessing over individual keyword scores — Quality Score fluctuates daily for individual keywords. Focus on distribution trends across your account, not whether one keyword moved from 7 to 6.
• Ignoring component breakdowns — A Quality Score of 5 could mean all three components are average, or one is above average while another is below. The fix is completely different in each case. Always check components.
• Over-segmenting ad groups — The old SKAG (Single Keyword Ad Group) approach creates management overhead without proportional benefit. Theme-based groups with 10-20 related keywords perform well and are maintainable.
• Sending all traffic to the homepage — Your homepage is not a landing page. Dedicated landing pages that match ad copy and keyword intent are one of the fastest ways to improve Landing Page Experience.
• Neglecting mobile experience — If your landing page is not mobile-optimized, you are fighting with one hand tied behind your back. Mobile experience is a major Landing Page Experience factor.

Lyra’s Campaign Health Analyzer includes Quality Score diagnostics in its daily health checks, alerting you when component scores degrade and estimating the CPC impact of current Quality Score distribution. This replaces manual spreadsheet analysis with automated, ongoing monitoring.