Optimization Score: what it measures (and what it doesn’t)
Google Ads Optimization Score is a 0–100% estimate of how well your account (or a specific campaign) is set up to perform based on the opportunities the platform can identify at that moment. A score of 100% simply means you’ve taken action on every available opportunity—either by applying the recommendation or dismissing it—so the system no longer considers anything “pending.” That’s an important nuance: 100% is not a promise of better results; it’s a signal that there are no outstanding platform-suggested changes left unreviewed.
You can see Optimization Score at the campaign, account, and manager (MCC) levels, and it’s available for active campaigns in several major campaign types (including Search, Display, App, Performance Max, Shopping, and certain video formats). The score updates in real time as your settings, performance signals, eligibility, and recommendation set change—so it’s normal to see the number move even when you haven’t touched the account.
Every recommendation comes with a score uplift (a percentage) that represents the estimated impact of applying that recommendation on your overall score. In day-to-day management, that uplift is best used as a prioritization tool, not as a KPI. Also, don’t be surprised if the math doesn’t “add up” neatly: once you apply some recommendations, others can become irrelevant or disappear, so the sum of all individual uplifts isn’t a reliable way to forecast where you’ll land.
Optimization Score vs. Quality Score (why high Quality Score can still mean a low Optimization Score)
Quality Score (1–10 at the keyword level) is primarily about expected clickthrough rate, ad relevance, and landing page experience for Search. Optimization Score is broader and system-driven: it reflects whether Google Ads sees configuration or feature adoption opportunities across bidding, budgets, ads/assets, targeting, measurement, and campaign setup.
That’s why you can have excellent keyword Quality Scores and still see a mediocre Optimization Score. They’re measuring different things at different scopes—and improving one doesn’t automatically move the other.
A proven workflow to raise Optimization Score without hurting ROI
If you want a “good” Optimization Score (and better performance), the goal isn’t to mindlessly chase 100%. The goal is to build a repeatable review process where you (1) align the account with the right objective, then (2) apply the recommendations that support that objective, and (3) confidently dismiss the ones that don’t.
Step 1: lock the objective first (so recommendations stop pulling you in multiple directions)
Optimization Score can reflect a performance objective focus (for example, conversions vs. clicks vs. impression share). In mature accounts, the fastest way to create score stability is to make sure your bidding and conversion setup clearly communicate what “winning” means. If the account is unclear—mixed goals, messy conversion actions, or misaligned bidding strategies—you’ll often see recommendations that raise the score but don’t match the business reality.
Start by ensuring your conversion actions and the ones you’ve chosen to optimize for are truly the ones tied to profit (or qualified pipeline). When your conversion optimization is clean, recommendations around bidding and expansion become much more sensible—and much safer to adopt.
Step 2: use guided recommendations to triage, then go deeper
Most advertisers waste time scrolling a long list of cards. Instead, use the platform’s guided recommendations as your triage layer: they typically surface the top categories based on impact and relevance. Once you’ve handled the big rocks, you can drop into the full list and make the finer decisions.
Step 3: treat “Apply” and “Dismiss” as equally valuable actions
Many teams get stuck because they think dismissing is “bad” or that it will hurt the account. In reality, the platform is built for you to use both actions. You can reach 100% by applying or dismissing all recommendations. The difference between amateur and expert management is that experts dismiss quickly and intentionally when a suggestion doesn’t fit the strategy.
- Apply when the recommendation aligns with your current goal, budget reality, and measurement setup.
- Dismiss when it conflicts with your account structure, brand requirements, risk tolerance, or short-term constraints (like inventory, capacity, or margin).
Step 4: understand how dismissals work (so the score doesn’t “bounce back” unexpectedly)
Dismissals have rules that matter operationally. If you dismiss at the campaign level, you’re generally dismissing that recommendation type for that campaign. If you dismiss at the account level, you’re suppressing that recommendation type across the account for a period of time. Dismissed recommendations can reappear later if the campaign remains eligible and conditions change.
Also, be aware of recommendation “bundles.” If you partially accept items inside a recommendation card, the card may remain visible until you dismiss the remainder. And in some cases, partial application can’t be undone—so it’s worth slowing down and reviewing details before applying changes at scale.
Step 5: don’t panic when recommendations appear/disappear
Recommendations come and go for normal reasons: you applied them, the campaign was paused or changed, eligibility shifted, or the platform no longer considers the benefit meaningful. This is why I advise clients to judge your process (and performance), not your ability to keep the recommendations list perfectly “clean” every day.
If you don’t see recommendations (or don’t see a score), fix the fundamentals first
Not seeing recommendations isn’t always a bug. Common real-world causes include missing billing setup, not having campaigns/ad groups/ads configured yet, having no traffic, or being too early in a campaign’s life for the system to generate meaningful suggestions. And yes—sometimes it simply means you’re already in a solid place.
High-score optimizations that usually improve performance (when done with guardrails)
Over the years, I’ve found that the healthiest way to improve Optimization Score is to focus on the recommendation categories that typically correlate with better account hygiene and better auction performance. The key is adding guardrails so you capture upside without surrendering control.
Ads & assets: aim for coverage and variety, not “more stuff”
Many accounts leave performance on the table by running too few ads, not keeping assets fresh, or underutilizing modern formats. Recommendations in this area often push you toward stronger coverage (for example, improving responsive ad setups or strengthening asset groups in Performance Max).
The guardrail is simple: apply creative recommendations when you can maintain brand quality. If your compliance team, legal review, or brand voice requires tighter control, you can still improve score by adding thoughtfully written variations and completing missing asset types—without accepting auto-generated messaging you wouldn’t approve.
Bidding & measurement: the biggest score jumps happen when conversion signals are trustworthy
Recommendations frequently encourage adopting automated bidding approaches or refining targets (such as target CPA or target ROAS). These can absolutely work—especially at scale—but only when conversion tracking is accurate, timely, and mapped to real business value.
My practical rule: if you wouldn’t make a budget decision using your current conversion reporting, don’t let an automated bidding recommendation make bidding decisions with it either. Clean up conversion actions, confirm what’s included in optimization, and then lean into bidding recommendations with far more confidence.
Keywords & targeting: expand intentionally (and protect query quality)
Google Ads often surfaces opportunities to broaden reach—through targeting expansion, keyword expansion, or new campaign types. Expansion can be profitable, but it must be controlled. When you apply reach-oriented recommendations, pair them with a plan to monitor search terms/query quality, tighten intent where needed, and ensure budgets don’t drift away from your highest-margin traffic.
If you’re a lead gen advertiser, this is where I see the most “score vs. ROI” tension: raising the score is easy if you accept aggressive expansion, but keeping lead quality high requires discipline. It’s completely reasonable to dismiss expansion recommendations that don’t match your qualification standards.
Budget recommendations: optimize for profitable volume, not just more spend
Some recommendations push budget increases or shifts to capture more traffic. These can be valid if you’re constrained by budget in profitable campaigns. But a higher score is not a reason to spend more. Apply budget-related recommendations only when you can validate that marginal dollars are likely to produce marginal profit (or acceptable CAC/LTV outcomes).
Use auto-apply only for recommendation types you would approve 90% of the time
Auto-apply can be a legitimate tool for time savings, but it’s not a set-and-forget feature. You should be able to audit what’s been applied and when, and you should know how to turn off any auto-applied recommendation type that starts creating risk.
In well-managed accounts, I typically reserve auto-apply for tightly scoped recommendation types where the downside is low and the review burden is high. For anything that can materially change targeting intent, budget allocation, or brand messaging, I keep it manual and treat recommendations as prompts for human decision-making.
Make Optimization Score a routine, not a fire drill
The accounts that maintain consistently strong Optimization Scores (without performance volatility) treat recommendations like ongoing maintenance. Build a cadence: review new recommendations regularly, prioritize by uplift and business fit, apply/dismiss quickly, and then validate impact through your core performance metrics. When you run it like a system, the score naturally climbs—and, more importantly, performance improvements tend to stick.
Let AI handle
the Google Ads grunt work
Let AI handle
the Google Ads grunt work
If you’re working to improve your Google Ads Optimization Score, it helps to treat it as a structured maintenance routine rather than a performance KPI: start by locking in the right objective and a clean conversion setup, then use recommendations to triage what matters most, and be just as intentional about dismissing suggestions that don’t fit your strategy as you are about applying the ones that do. If you want a lighter way to stay on top of that process, Blobr connects to your Google Ads account and continuously analyzes performance, then surfaces clear, prioritized actions through specialized AI agents—like a Headlines Enhancer to refresh RSA assets or a Keyword Landing Optimizer to better match keywords with landing pages—so you can keep your account aligned with best practices without blindly chasing 100%.
Optimization Score: what it measures (and what it doesn’t)
Google Ads Optimization Score is a 0–100% estimate of how well your account (or a specific campaign) is set up to perform based on the opportunities the platform can identify at that moment. A score of 100% simply means you’ve taken action on every available opportunity—either by applying the recommendation or dismissing it—so the system no longer considers anything “pending.” That’s an important nuance: 100% is not a promise of better results; it’s a signal that there are no outstanding platform-suggested changes left unreviewed.
You can see Optimization Score at the campaign, account, and manager (MCC) levels, and it’s available for active campaigns in several major campaign types (including Search, Display, App, Performance Max, Shopping, and certain video formats). The score updates in real time as your settings, performance signals, eligibility, and recommendation set change—so it’s normal to see the number move even when you haven’t touched the account.
Every recommendation comes with a score uplift (a percentage) that represents the estimated impact of applying that recommendation on your overall score. In day-to-day management, that uplift is best used as a prioritization tool, not as a KPI. Also, don’t be surprised if the math doesn’t “add up” neatly: once you apply some recommendations, others can become irrelevant or disappear, so the sum of all individual uplifts isn’t a reliable way to forecast where you’ll land.
Optimization Score vs. Quality Score (why high Quality Score can still mean a low Optimization Score)
Quality Score (1–10 at the keyword level) is primarily about expected clickthrough rate, ad relevance, and landing page experience for Search. Optimization Score is broader and system-driven: it reflects whether Google Ads sees configuration or feature adoption opportunities across bidding, budgets, ads/assets, targeting, measurement, and campaign setup.
That’s why you can have excellent keyword Quality Scores and still see a mediocre Optimization Score. They’re measuring different things at different scopes—and improving one doesn’t automatically move the other.
A proven workflow to raise Optimization Score without hurting ROI
If you want a “good” Optimization Score (and better performance), the goal isn’t to mindlessly chase 100%. The goal is to build a repeatable review process where you (1) align the account with the right objective, then (2) apply the recommendations that support that objective, and (3) confidently dismiss the ones that don’t.
Step 1: lock the objective first (so recommendations stop pulling you in multiple directions)
Optimization Score can reflect a performance objective focus (for example, conversions vs. clicks vs. impression share). In mature accounts, the fastest way to create score stability is to make sure your bidding and conversion setup clearly communicate what “winning” means. If the account is unclear—mixed goals, messy conversion actions, or misaligned bidding strategies—you’ll often see recommendations that raise the score but don’t match the business reality.
Start by ensuring your conversion actions and the ones you’ve chosen to optimize for are truly the ones tied to profit (or qualified pipeline). When your conversion optimization is clean, recommendations around bidding and expansion become much more sensible—and much safer to adopt.
Step 2: use guided recommendations to triage, then go deeper
Most advertisers waste time scrolling a long list of cards. Instead, use the platform’s guided recommendations as your triage layer: they typically surface the top categories based on impact and relevance. Once you’ve handled the big rocks, you can drop into the full list and make the finer decisions.
Step 3: treat “Apply” and “Dismiss” as equally valuable actions
Many teams get stuck because they think dismissing is “bad” or that it will hurt the account. In reality, the platform is built for you to use both actions. You can reach 100% by applying or dismissing all recommendations. The difference between amateur and expert management is that experts dismiss quickly and intentionally when a suggestion doesn’t fit the strategy.
- Apply when the recommendation aligns with your current goal, budget reality, and measurement setup.
- Dismiss when it conflicts with your account structure, brand requirements, risk tolerance, or short-term constraints (like inventory, capacity, or margin).
Step 4: understand how dismissals work (so the score doesn’t “bounce back” unexpectedly)
Dismissals have rules that matter operationally. If you dismiss at the campaign level, you’re generally dismissing that recommendation type for that campaign. If you dismiss at the account level, you’re suppressing that recommendation type across the account for a period of time. Dismissed recommendations can reappear later if the campaign remains eligible and conditions change.
Also, be aware of recommendation “bundles.” If you partially accept items inside a recommendation card, the card may remain visible until you dismiss the remainder. And in some cases, partial application can’t be undone—so it’s worth slowing down and reviewing details before applying changes at scale.
Step 5: don’t panic when recommendations appear/disappear
Recommendations come and go for normal reasons: you applied them, the campaign was paused or changed, eligibility shifted, or the platform no longer considers the benefit meaningful. This is why I advise clients to judge your process (and performance), not your ability to keep the recommendations list perfectly “clean” every day.
If you don’t see recommendations (or don’t see a score), fix the fundamentals first
Not seeing recommendations isn’t always a bug. Common real-world causes include missing billing setup, not having campaigns/ad groups/ads configured yet, having no traffic, or being too early in a campaign’s life for the system to generate meaningful suggestions. And yes—sometimes it simply means you’re already in a solid place.
High-score optimizations that usually improve performance (when done with guardrails)
Over the years, I’ve found that the healthiest way to improve Optimization Score is to focus on the recommendation categories that typically correlate with better account hygiene and better auction performance. The key is adding guardrails so you capture upside without surrendering control.
Ads & assets: aim for coverage and variety, not “more stuff”
Many accounts leave performance on the table by running too few ads, not keeping assets fresh, or underutilizing modern formats. Recommendations in this area often push you toward stronger coverage (for example, improving responsive ad setups or strengthening asset groups in Performance Max).
The guardrail is simple: apply creative recommendations when you can maintain brand quality. If your compliance team, legal review, or brand voice requires tighter control, you can still improve score by adding thoughtfully written variations and completing missing asset types—without accepting auto-generated messaging you wouldn’t approve.
Bidding & measurement: the biggest score jumps happen when conversion signals are trustworthy
Recommendations frequently encourage adopting automated bidding approaches or refining targets (such as target CPA or target ROAS). These can absolutely work—especially at scale—but only when conversion tracking is accurate, timely, and mapped to real business value.
My practical rule: if you wouldn’t make a budget decision using your current conversion reporting, don’t let an automated bidding recommendation make bidding decisions with it either. Clean up conversion actions, confirm what’s included in optimization, and then lean into bidding recommendations with far more confidence.
Keywords & targeting: expand intentionally (and protect query quality)
Google Ads often surfaces opportunities to broaden reach—through targeting expansion, keyword expansion, or new campaign types. Expansion can be profitable, but it must be controlled. When you apply reach-oriented recommendations, pair them with a plan to monitor search terms/query quality, tighten intent where needed, and ensure budgets don’t drift away from your highest-margin traffic.
If you’re a lead gen advertiser, this is where I see the most “score vs. ROI” tension: raising the score is easy if you accept aggressive expansion, but keeping lead quality high requires discipline. It’s completely reasonable to dismiss expansion recommendations that don’t match your qualification standards.
Budget recommendations: optimize for profitable volume, not just more spend
Some recommendations push budget increases or shifts to capture more traffic. These can be valid if you’re constrained by budget in profitable campaigns. But a higher score is not a reason to spend more. Apply budget-related recommendations only when you can validate that marginal dollars are likely to produce marginal profit (or acceptable CAC/LTV outcomes).
Use auto-apply only for recommendation types you would approve 90% of the time
Auto-apply can be a legitimate tool for time savings, but it’s not a set-and-forget feature. You should be able to audit what’s been applied and when, and you should know how to turn off any auto-applied recommendation type that starts creating risk.
In well-managed accounts, I typically reserve auto-apply for tightly scoped recommendation types where the downside is low and the review burden is high. For anything that can materially change targeting intent, budget allocation, or brand messaging, I keep it manual and treat recommendations as prompts for human decision-making.
Make Optimization Score a routine, not a fire drill
The accounts that maintain consistently strong Optimization Scores (without performance volatility) treat recommendations like ongoing maintenance. Build a cadence: review new recommendations regularly, prioritize by uplift and business fit, apply/dismiss quickly, and then validate impact through your core performance metrics. When you run it like a system, the score naturally climbs—and, more importantly, performance improvements tend to stick.
