Google Ads Recommendations: A Practical Optimization Guide
Google Ads Recommendations are automated suggestions inside the Google Ads account that propose changes to improve performance, and each one rolls up into the account's Optimization Score. They can save time, but they should be reviewed case by case because not every suggestion fits your actual business goals. A disciplined review process turns the list from noise into a steady source of tested improvements.
Key Takeaways
- Google Ads Recommendations are account suggestions, and the Optimization Score is a 0 to 100 percent estimate of performance potential.
- Common types cover bids and budgets, keywords, ads and extensions, and targeting.
- Apply recommendations that align with your goals, and dismiss the ones that conflict with them.
- Auto-apply can save time but needs guardrails so it does not override your strategy.
- Recommendations can also be read and applied programmatically through the Google Ads API.
What Are Google Ads Recommendations?
Recommendations are suggestions Google generates from your account data and auction signals. Each one proposes a specific change - raise a bid, add a keyword, attach an extension, or adjust a target - and shows an estimated impact on performance. They appear on the Recommendations page and are summarized by the Optimization Score.
The intent is helpful: surface quick wins you might miss. The catch is that the system optimizes for its own modeled outcome, which is not always the same as your real objective, especially for profitability, lead quality, or brand constraints. Treat them as a to-do list to evaluate, not an auto-pilot.
What Is the Optimization Score?
The Optimization Score is a percentage from 0 to 100 that estimates how well your account is set up to perform, based on the recommendations available. Each recommendation carries an estimated score uplift, so applying several can raise the number. It is best read as a health indicator, not a business KPI.
A high score does not guarantee profit, and a lower score does not mean the account is broken. Many strong accounts intentionally skip recommendations that would chase volume at the expense of efficiency. Use the score to spot neglected areas, then judge each item on its own merits.
Common Types of Google Ads Recommendations
| Category | Examples |
|---|---|
| Bids and budgets | Switch to automated bidding, raise budgets, set targets |
| Keywords | Add search keywords, remove conflicts, use broad match |
| Ads and extensions | Add responsive search ad assets, attach sitelinks, fix ad strength |
| Targeting | Adjust audiences, add locations, use optimized targeting |
| Measurement | Set up conversion tracking, import goals, fix tag issues |
Should You Apply Every Recommendation?
No. A healthy practice is to weigh each suggestion against your goals:
- Alignment - does this move the metric you actually care about (profit, qualified leads, ROAS)?
- Control - does it keep you in charge of spend and targeting, or hand it to automation you cannot see?
- Evidence - is the estimated impact plausible for your account, or a generic nudge?
- Trade-offs - will it raise volume while hurting efficiency or lead quality?
Which Recommendations to Trust vs Ignore
As a rule of thumb, measurement and structural fixes tend to be safe: turning on conversion tracking, fixing a broken tag, or attaching a relevant extension usually helps. Bid and budget changes deserve more scrutiny because they shift spend and control.
Recommendations to use broad match or maximize features can work, but only inside a strategy you understand and can monitor. If a suggestion would dilute targeting, inflate spend without a clear return, or conflict with a deliberate choice you made, dismiss it. Dismissing also tells the system your preference, which can reduce similar nudges.
How to Apply Recommendations
In the UI, you review the Recommendations page, open an item, and choose Apply or Dismiss, often with a preview of the change. For ongoing hygiene, some teams set a weekly review where a marketer works the list and documents the decisions.
Programmatically, the Google Ads API exposes a RecommendationService. You can list recommendations, read their type and impact, apply the ones you want with applyRecommendation, and dismiss the rest with dismissRecommendation. That lets you build an internal policy - for example, auto-apply only measurement fixes and route the rest to a human queue.
Auto-Apply: Risks and Guardrails
Google Ads offers auto-apply for categories you select, so eligible recommendations go live without a click. This saves time on repetitive accounts, but it can also make changes you did not individually review.
If you enable auto-apply, do it in categories you trust and keep the rest manual. Review the change history regularly, set account-level boundaries (like fixed daily budgets) that automation cannot break, and keep conversion tracking tight so the system optimizes toward the right outcome. The goal is speed without surprises.
Common Mistakes with Recommendations
- Chasing a higher Optimization Score as if it were the business goal.
- Auto-applying everything and losing control of targeting and spend.
- Applying bid changes without checking the impact on profitability.
- Ignoring recommendations entirely and missing genuine quick wins.
- Treating a dismissed suggestion as permanently fixed rather than re-reviewed periodically.
Getting Started: A Simple Workflow
- Open the Recommendations page and read the categories available to your account.
- Separate them into three piles: apply, dismiss, and review later.
- Apply the safe measurement and extension fixes first.
- For bid and budget items, check alignment with your targets before touching them.
- Decide which categories, if any, are safe to auto-apply, and monitor the change history.
- Re-review monthly so the account stays aligned as goals shift.
Related Reading
Read more on Google Ads AI features. Read more on Google Ads management guide. Read more on marketing dashboards.
Recommendations by Campaign Type
The useful recommendations differ by campaign type. For Search, the list often centers on keywords, match types, and ad strength. For Performance Max, it leans toward asset coverage, audience signals, and goal setup. For Display and Video, it emphasizes targeting and creative formats. Knowing your campaign type helps you prioritize: a Search account should not ignore a keyword conflict, while a Performance Max account should pay attention to asset gaps. Scan the list with your campaign mix in mind rather than treating every suggestion as equally relevant.
How Google Generates Recommendations
Recommendations come from a mix of your account history, auction-time signals, and Google's models of what tends to improve performance for similar accounts. They are not hand-written by a person looking at your business; they are system-generated and therefore generic by design. That is useful for catching obvious gaps, but it is also why a suggestion that is "best practice" on average can be wrong for your specific strategy. Knowing they are algorithmic helps you review them with the right skepticism.
A Decision Guide: Apply vs Dismiss
| Recommendation type | Default call | Why |
|---|---|---|
| Fix conversion tracking or tag | Apply | Measurement is foundational; safe and high value |
| Add extensions or assets | Apply | Usually improves ad real estate with low risk |
| Raise budgets or bids | Review | Can help or can burn spend; check efficiency first |
| Switch match types or targeting | Review | May dilute intent; confirm it fits your funnel |
| Auto-apply broad features | Dismiss or limit | Keep control unless you have a clear test plan |
Measuring the Impact of Applied Recommendations
Before and after any recommendation, capture the baseline: the metric you care about, the date, and the account state. After applying, watch it over a meaningful window rather than a day or two, because auction effects take time to settle. If a change improves your target metric, keep it and note the pattern; if it moves the wrong metric or hurts efficiency, dismiss similar ones going forward. Keeping a short log of decisions turns the Recommendations page from a stream of nudges into a managed optimization process.
The optimization score that summarizes these recommendations is a different metric worth understanding on its own. Read our Google Ads optimization score guide for when to ignore the number.
Frequently Asked Questions
What Is the Google Ads Optimization Score?
The Optimization Score is a percentage from 0 to 100 that estimates how well your account is set up to perform, based on the recommendations available. Each recommendation shows an estimated uplift, but the score is a health indicator, not a measure of profit or business outcomes.
Should I Apply All Google Ads Recommendations?
No. Apply the ones that align with your actual goals and dismiss those that conflict with them. Measurement and extension fixes are usually safe, while bid and budget changes deserve closer review because they shift spend and control.
Can I Automate Google Ads Recommendations?
Yes. In the UI you can enable auto-apply for selected categories, and through the Google Ads API you can list, apply, or dismiss recommendations programmatically so your team enforces a consistent policy.
Does a High Optimization Score Mean Better Results?
Not necessarily. A high score means your account matches Google's modeled best practices, but those practices may prioritize volume or engagement over the profitability or lead quality you care about. Judge each recommendation on its own impact.
What Is the Risk of Auto-Applying Recommendations?
Auto-apply can make changes you did not individually review, which may shift targeting or spend in ways you would not choose. Limit it to categories you trust, keep hard budget boundaries, and review the change history regularly.