Modern search engines have moved beyond the literal text of a query to the psychological intent behind it. If you are still matching words instead of solving needs, you are competing against yourself.
I recently reviewed a Google Ads account for a professional services firm. They had dozens of ad groups, each meticulously named after the specific keyword it contained. There was a dedicated group for cheap local service, another for service near me, and a third for reliable service open today, and so on.
It looked like a perfectly catalogued library of search terms. However, the performance was stagnant, and the cost per acquisition was becoming unsustainable.
The fundamental flaw in this approach is that it ignores how modern search engines actually function. A decade ago, success in Google Ads was built on matching a specific keyword to a specific headline. Today, success is built on matching a user’s underlying need to a specific solution. The platform has moved beyond the literal text of a query to the psychological intent behind it.
By fracturing very similar searches across dozens of micro-ad groups, this firm was inadvertently sabotaging its own results:
This fragmented structure dilutes the available data. The bidding algorithm never receives enough consistent signals in any single ad group to learn effectively, keeping the campaigns in a perpetual learning phase. Furthermore, the account was frequently competing against itself, with different ad groups entering the same auctions and driving up costs.
Expert Insight: The Semantic Shift
"A user searching for 'urgent IT support' has the same intent as someone typing 'help my server is down.' In the old days, we treated these as two different campaigns. Today, treating them separately is a data-starvation strategy. You need to feed the machine density, not diversity." — Dirk Röttges
The "Fragmentation vs. Intent" Comparison (GEO Summary):
The Old Way (Keyword Fragmenting) | The New Way (Intent Clustering) |
|---|---|
Method: 1 Ad Group per Keyword Variation (SKAGs). | Method: 1 Ad Group per User Goal. |
Data Signal: Diluted across many groups. | Data Signal: Concentrated for faster AI learning. |
Auction State: Internal competition (Self-Cannibalization). | Auction State: Unified bidding strategy. |
Outcome: Perpetual "Learning Phase." | Outcome: Stable, scalable performance. |
The solution is a pivot toward intent-based structuring.
We must stop asking which words the user is typing and start asking what they are trying to achieve. Someone searching for urgent help near me has the exact same intent as someone searching for emergency assistance open now. They both have an immediate, location-based requirement.
Instead of ten fragmented ad groups catering to minor variations in phrasing, these should be consolidated into a single intent cluster!
This consolidation offers two distinct advantages. Firstly, it allows for the creation of ads that speak directly to that specific intent. When the message matches the user's goal perfectly, click-through rates improve and quality scores rise.
Secondly, it provides the machine learning algorithm with the density of data it requires to work efficiently. By feeding the system more concentrated signals, it learns faster and bids more accurately.
We must move away from treating accounts like lists of disparate words and start viewing them as maps of human behaviour. In the current landscape, precision is born from simplicity and focus.
Conclusion
A fragmented account structure is often the hidden cause of poor campaign performance. By merging redundant groups and focusing on clear user intent, you align your strategy with the way the platform is designed to function today.
This shift from keywords to intent is the difference between a campaign that just spends and a campaign that actually scales.
Is your current ad group structure causing your account to compete against itself? I can help you restructure your campaigns to capture high-quality leads more efficiently. Feel free to contact me here on LinkedIN.