Most advertisers treat Google Ads like it's unpredictable. Performance goes up, performance goes down, and they blame bid strategies, match types, or whatever Google changed last week.
I get why it feels that way. But it's the wrong diagnosis.
Google Ads isn't volatile. It's structural.
After auditing thousands of accounts across every industry and budget level, I can tell you this: campaigns that work do so for consistent reasons. Campaigns that fail tend to fail the same predictable ways.
The difference comes down to three systems: the user system, the cost system, and the machine learning system.
When one is weak, everything else compensates poorly. When all three are aligned, the account becomes predictable, diagnosable, and scalable.
Most advertisers don't think about it this way. They see Google Ads as a creative exercise or a bidding game. What they're actually dealing with is an auction built on machine learning that responds to user behavior.
If you don't understand how these three systems interact, you'll spend a lot of time and money learning the hard way.
Let me show you what I mean.
The User System: Intent Is Everything
Here's the thing about search advertising that separates it from every other channel: people tell you exactly what they want.
When someone types a query into Google, they're articulating a need. Your job is to decode that need and respond to it appropriately. Most advertisers don't do this. They hear "keyword research" and they think it means "find words related to my product."
Wrong.
A search query is a window into someone's current state of awareness. And awareness has levels.
Someone searching "what is marketing automation" is problem-aware but solution-unaware. They know they have an issue but they don't know what category of solution exists yet. If you show them an ad that says "Start Your Free Trial," you'll get ignored. They're not ready for that conversation.
Someone searching "HubSpot vs ActiveCampaign" is solution-aware and comparison-shopping. They know the category. They know the main players. They're evaluating options. Now you can talk about features, pricing, and differentiation. Now "Start Your Free Trial" makes sense.
This is what "understanding the user" actually means. It's not demographics. It's not psychographics. It's matching your message to their current level of understanding about the problem they're trying to solve.
The audience size also determines your targeting approach, your budget requirements, and your timeline to profitability. Most people skip this step and wonder why their campaign economics don't work.
The funnel position determines your creative approach. Top of funnel is education. Middle of funnel is consideration. Bottom of funnel is conversion. The messaging is completely different at each stage, and if you mix them up, you confuse people.
Get this right and everything else gets easier. Get this wrong and nothing else matters.
The Cost System: Understanding The Auction Mechanics
Google Ads is an auction. But it's not a simple auction where highest bid wins.
It's a second-price auction with a quality multiplier. That sentence contains the entire secret to cost management, so let me unpack it.
In a second-price auction, you pay just enough to beat the next highest bidder, not your maximum bid. This is good. It means you're not overpaying just because you were willing to.
The quality multiplier is your Quality Score. It's Google's way of rewarding ads that users actually want to click on. Higher Quality Score means you can win auctions with lower bids. Lower Quality Score means you pay more for the same position.
This creates three cost levers:
First lever: Your bid strategy. Manual CPC gives you control but requires constant attention. Target CPA automates based on a cost goal but needs conversion data to work. Maximize Conversions goes after volume but can overspend if you're not careful. Each strategy optimizes for something different. Pick the wrong one and you're asking the system to solve for the wrong variable.
Second lever: The competitive environment. If you're in legal services or insurance, CPCs are going to be high. There's nothing you can do about that. It's a function of lifetime value and competitive intensity. You can't "hack" your way into cheap clicks in an expensive category. You can only decide if the unit economics work at the prevailing market rate.
Third lever: Your conversion rate. This is the one most people ignore, and it's the most powerful.
Let's do the math. You're paying $5 per click. Your conversion rate is 2%. Your cost per acquisition is $250.
Now double your conversion rate to 4%. Your cost per acquisition drops to $125. Same traffic source. Same CPC. Half the cost per conversion.
Most advertisers try to lower their CPC from $5 to $4. They'd be better off improving their landing page and doubling their conversion rate. The ROI on conversion rate optimization is almost always higher than the ROI on bid optimization.
The cost system is not about finding cheap clicks. It's about understanding what you can afford to pay based on your conversion rate and lifetime value, and then structuring your bids and targeting to operate profitably at that cost level.
If your math doesn't work at current market rates, you have three options: improve your conversion rate, increase your prices, or find a different channel.
The Machine Learning System: Data Quality Determines Everything
Google's algorithms are good. Really good. But they're not magic.
Machine learning systems need three things to work: data, time, and a clear objective. Most campaign failures in the automated bidding era come from not providing one or more of these.
The data requirement is specific. Google's own documentation says you need 30 conversions in 30 days for Target CPA to work properly. That's not a suggestion. That's the minimum statistical threshold for the model to find patterns.
If you're getting 5 conversions a month, automated bidding will underperform. The algorithm doesn't have enough signal to separate good auctions from bad auctions. You're basically asking it to predict outcomes based on noise.
This means budget matters. If your conversion rate is 2% and your CPC is $5, you need $7,500 in monthly spend just to hit the 30-conversion threshold. Below that, you're probably better off with manual bidding or focusing on conversion rate optimization first.
The tracking requirement is absolute. The algorithm optimizes for what you tell it to optimize for. If your tracking is broken, you're teaching it to do the wrong thing.
I've seen accounts where Google Ads reported 100 conversions but the CRM showed 60 sales. The algorithm was optimizing for 40 conversions that didn't actually exist. The campaign looked good in Google Ads and terrible in the P&L.
This happens more than you think. Form submissions that didn't go through. Duplicate conversions from page refreshes. Tracking pixels that fire on thank you page loads instead of actual form completions.
If your conversion tracking isn't accurate, machine learning makes your campaign worse, not better. It optimizes harder toward the wrong goal.
The conversion definition determines what you get. This is the tricky part.
You can track soft conversions (form fills, demo requests, content downloads) or hard conversions (actual sales, signed contracts). Soft conversions give you volume and faster learning. Hard conversions give you accuracy but slower learning.
Most B2B companies optimize for demo requests because that's what gives them enough conversion volume for the algorithm to work. But if your demo-to-close rate is 10%, you're teaching the algorithm to find people who will book demos, not people who will buy.
The solution isn't to stop tracking demos. It's to understand that the algorithm is doing exactly what you told it to do, and to account for the demo-to-close conversion rate in your planning.
The best approach: start with a conversion that gives you enough volume for learning (usually 30+ per month), then add conversion value or offline conversion imports to teach the algorithm the difference between good conversions and great conversions.
Machine learning is not about "set it and forget it." It's about setting up the right data inputs so the algorithm can actually do its job.
How This All Connects
These three systems don't operate independently. They interact.
Your user targeting determines how much data you'll get. If you target too narrow, you won't hit the conversion volume threshold for machine learning to work well. If you target too broad, your conversion rate drops and your costs go up.
Your conversion rate determines what you can afford to pay in the auction. If your conversion rate is high, you can bid more aggressively. If it's low, you need cheaper clicks or better targeting.
Your machine learning setup determines how efficiently you can find the right users at the right cost. Good tracking and enough data means the algorithm can find profitable auctions you wouldn't have found manually.
Here's what this looks like in practice:
You're launching a campaign for a $1,500 online course. Your target CAC is $300. Your expected conversion rate is 3%. That means you can afford $9 per click.
You research the competitive landscape and see CPCs running $6-8 for your keywords. Good, you have margin.
You map out your keywords by intent. Educational keywords go in one campaign with educational ad copy. Comparison keywords go in another campaign with comparison-focused messaging. Competitor keywords go in a third campaign with direct differentiation.
You make sure your conversion tracking is working correctly. You test it three times. You verify it in your CRM.
You launch with manual CPC to gather baseline data. You let it run until you have 30 conversions. Then you switch to Target CPA with a $300 target.
The algorithm now has clean data, a clear goal, and enough volume to learn. It starts finding the auctions where someone searching for your keywords is most likely to convert. Your cost per acquisition stabilizes. Your campaign becomes predictable.
This is how you build a campaign that actually works. Not by obsessing over ad copy, but by understanding how the user system, cost system, and machine learning system interact.
Get the strategy right and the tactics take care of themselves. Get the strategy wrong and no amount of tactical optimization will save you.
How to use this in real accounts...
When something is not working, do not ask: βWhat should we tweak?β
Ask:
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Do we understand who this user is?
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Does the math work at current conversion rates?
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Does the system have enough signal to learn?
Those three questions solve most problems faster than any checklist.
If your Google Ads feel chaotic, it is rarely because you are missing a trick. It is usually because one of these pillars is out of alignment.
Fix the foundation, and the system starts working the way it should.
If you want to explore these pillars in more detail, we break them down inside our free membership. And if you want to go deeper and understand how Google Ads actually works at every level, our certifications cover the full system from strategy to scale.
Until next week,
- Isaac Rudansky
Founder of the Modern Marketing Institute
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