PPC performance conversations often focus on best practices.
- Account structures should be clean.
- Match types controlled.
- Budgets scaled gradually.
- Campaigns should avoid overlap.
- Everything should be logical, efficient, and easy to explain.
That foundation matters. It creates consistency and avoids obvious inefficiencies.
But it’s not where the biggest gains come from.
Looking back over the past 10 years, many of the most meaningful performance improvements didn’t come from refining those frameworks. They came from testing ideas that didn’t quite fit them — things that felt slightly uncomfortable but aligned with how platforms actually behave.
In practice, Google Ads and Meta don’t optimize toward best practice. They optimize toward signals. Once you think in those terms, your approach to performance changes.
Control still matters more than you think (SKAGs aren’t actually ‘dead’)
Single Keyword Ad Groups were widely written off as automation improved.
The narrative was simple: machine learning removed the need for granular control. Structure mattered less.
In practice, that wasn’t entirely true.
In several accounts, reintroducing SKAGs on a small subset of high-intent, high-revenue keywords led to immediate performance gains. Query matching tightened, ad relevance improved, and conversion rates increased.
This wasn’t about reverting to old structures across the board. It was about recognizing where precision still adds value.
The takeaway is more nuanced than “SKAGs work” or “SKAGs are dead.”
Control still matters, but only where intent justifies it.
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Broad match works better when you aggressively constrain it
Broad match has always carried distrust.
Too much expansion. Too little control. Too much reliance on Google’s interpretation of intent.
In practice, one of the most effective setups combines broad match with aggressive negative keyword management.
Instead of restricting input, you let Google explore — then shape the output.
Search term mining becomes the control layer. You continuously remove irrelevant queries and reinforce valuable ones.
This creates a system where reach expands without fully sacrificing relevance.
The shift is how you apply control.
You don’t control broad match upfront. You control what it learns from.
Target Impression Share changes behavior when visibility matters more than efficiency
Target Impression Share is usually positioned as a defensive metric.
Used for brand campaigns. Used to protect coverage. Rarely used for growth.
Applying it to non-branded, high-value terms feels counterintuitive. You prioritize presence…
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