Don't Accept Every Google Ads Recommendation Until You've Checked These 5 Things

Somewhere right now, a marketer is staring at a Google Ads dashboard with a 62% Optimization Score sitting at the top of the screen, and a button that says "Apply all" sitting right underneath it. It's tempting. The score feels like a grade. 62% feels like something that should be fixed. One click, and it climbs toward 100.

But here's a detail buried in Google's own help documentation that changes how that click should feel: your account can reach a 100% Optimization Score by applying recommendations, or by dismissing them. Either action raises the score.

Sit with that for a second. If saying yes and saying no both move the number in the same direction, what is the number actually measuring?

1. What Is Optimization Score, Actually?

Optimization Score is a percentage, from 0 to 100, that Google Ads assigns to your account or campaign. It's built from a list of recommendations - things like raising your budget, broadening keyword match types, or switching bidding strategies - each carrying a "score uplift" value based on your account's settings, performance history, and broader search trends. Apply a recommendation worth 5%, and your score goes up 5%. Dismiss it, and the same 5% is removed from the pending pool, which also raises your score.

That's not a flaw in the system, exactly - it's just a sign that the score is measuring engagement with the recommendations panel, not quality of your strategy.

2. Does a Higher Score Actually Mean Better Performance?

Here's where it gets more interesting than a simple "it's fake, ignore it." One large-scale analysis of over 17,000 Google Ads accounts found that accounts with higher Optimization Scores did, on average, deliver meaningfully lower cost-per-acquisition and stronger return on ad spend than accounts scoring below 70%. So the score isn't meaningless - there's a real pattern underneath it.

But the same research points to something easy to miss: the top-performing accounts in that study didn't get their high scores by accepting every recommendation. They got there by being selective - applying the changes that fit their goals, and deliberately dismissing the ones that didn't. The score went up either way. The performance only improved when a human decided which changes actually made sense.

That's the gap between correlation and instruction. A high score correlates with strong performance. It doesn't follow that clicking "Apply all" causes it.

3.Should I Just Click "Apply All"?

This is the assumption worth challenging directly, because it's the one the interface is quietly nudging you toward: the recommendations panel is closer to Google, and I'm not, so it probably knows best.

Consider what a recommendation actually optimizes for. Google Ads recommendations are generated from aggregate patterns and account history - not from your specific margins, your compliance obligations, or your actual business goal for the quarter. A budget-increase recommendation might raise your score while pushing your cost-per-acquisition above what your business can sustain. A "switch to broader match types" suggestion might expand your reach while diluting the intent-matching you built deliberately. Some of what the panel suggests genuinely benefits your account. Some of it benefits Google's ecosystem - more spend, more automation, broader reach - in ways that are neutral-to-positive for Google regardless of whether they help you.

Here's a concrete version of this. Imagine a Dynamic Search Ads account that's already hitting its ROAS and conversion targets. Google's panel suggests "Upgrade to Performance Max" - a recommendation that would lift the score, but offers nothing extra to an account whose numbers already look exactly the way the advertiser wants them to. Or picture a regulated lead-generation account, where a Performance Max recommendation could technically raise the score while creating exposure the advertiser can't actually afford to take on. In both cases, the score goes up. The account isn't better off. Sometimes with the nature of the business, Performance Max campaign may not be ideal.

4. Five Things to Check Before You Touch That "Apply" Button

1. Does this recommendation match my actual goal, or just the score? If you're optimizing for cost-per-acquisition and the suggestion is a budget increase, ask what it does to your CPA target specifically - not just whether it raises the percentage on screen.
2. Is my account already meeting its targets? If your current setup is already hitting your ROAS or conversion goals, a recommendation to change strategy needs a better reason than "it's available."
3. Does this fit my industry's constraints? Regulated categories - finance, lead generation, gambling - often get recommendations tuned for reach and automation that don't account for compliance limits specific to that account.
4. Am I looking inside the bulk suggestion, or just clicking through it? A recommendation like "add 26 new keywords" is really 26 individual decisions bundled under one button. Worth opening the list before accepting the group.
5. Am I testing this before I let it run automatically? Google allows recommendations to auto-apply on a subscription basis. It's worth testing a change manually first, rather than handing the decision to a setting you won't be watching in real time.

5. So Is Google's Optimization Score Useless?

Not quite - and it's worth resisting the pull to overcorrect into "ignore it entirely." Some recommendations genuinely are low-risk and broadly helpful: improving ad assets, adding more image variety, fixing broken elements the system flags. Treating every suggestion as suspicious would be its own kind of mistake.

The more honest position sits in between: use the score as a prompt to look, not as an instruction to comply. Read each recommendation the way you'd read a colleague's suggestion - worth considering, not worth adopting automatically because they said so.

We don't have a controlled experiment on this yet - comparing an account that accepts every recommendation against an identical one that applies them selectively over the same period is exactly the kind of test that would turn this argument into evidence rather than a reasoned case. Until that exists, this is a case built from available research, not a finished result.

6. A Better Question to End On

Maybe the right question isn't "should I trust Google's recommendations?" It might be simpler, and a little more uncomfortable: if my score goes up whether I say yes or no, what exactly am I optimizing for?

Michelle Labs turns digital marketing questions into evidence. Have an assumption about your ad account you'd want tested rather than accepted on faith? That's exactly the kind of question we'd want to turn into an experiment.