Blog: Google Loosened Keyword Control. Here’s Where Your Team’s Time Should Go Now.

For years, a paid search team’s expertise showed up as keyword-level precision: the right match types, tightly built negative lists, bids managed down to the keyword. That skill set still matters, but less and less so each year.

Google has spent several product cycles moving control away from the literal search term an advertiser bought and toward its own read of intent. 

This piece covers what’s actually changed in how search term matching works, why fighting to reclaim that control is a low-ROI endeavor, and where to focus your optimization efforts instead.

What Exact, Phrase, and Broad Match Keywords Actually Do Now

The three keyword match types still exist in the Google Ads interface, but none of them work the way they did when the system was built.

Exact match no longer means the literal query. It accepts close variants — different word order, added or removed function words, and queries Google judges to have the same meaning as the term you bought.

Phrase match has absorbed the behavior that used to belong to modified broad match: it captures the intent and word order of your phrase while allowing additional words before, after, or in between.

Broad match has moved the furthest from its original purpose. It no longer primarily matches on the terms in your keyword list. Instead it reads signals: your landing pages, the other keywords in your account, the searcher’s context, and your bid strategy, then decides which queries are worth showing your ad against.

The common theme? Google is leaning harder on its own inference of intent than on the exact text an advertiser purchased. Each match type moved in that direction independently, and together they add up to a meaningfully different system than the one paid search precision was originally built around.

Where Match Types Disappear Completely: Performance Max and AI Max

Two Google products take the search term matching evolution to its logical end.

Performance Max removes match types altogether. In their place, advertisers provide Search Themes — a directional input describing what a campaign is about, not a control that determines which queries an ad can or can’t match.

AI Max layers automated query expansion and AI-generated creative directly onto standard Search campaigns, rather than existing as a separate campaign type. Search term matching moves beyond the keyword list entirely, ad copy gets generated and tested automatically, and in some configurations the system can route traffic to landing pages of its own choosing. 

Google has also been shifting parts of this from an opt-in feature to the default behavior for eligible campaigns. Automation is becoming the assumed state of a Search account, not an optional layer on top of one.

This is a significant reduction in the granular control paid search teams used to have. Search Themes are not match types with a different label, and automated query expansion is not the same discipline as a curated keyword list.

Why Fighting to Reclaim Keyword Control Is A Low-ROI Endeavor

This keyword evolution doesn’t mean your keyword strategy is worthless or that match type selection doesn’t matter anymore. It does. But the return on hours spent trying to force the old level of precision back into an account keeps shrinking. 

Exhaustive negative keyword lists built to counter broad match’s signal-reading behavior, granular match type experiments designed to outmaneuver an increasingly inference-driven system, endless query report audits chasing control that the platform is actively moving away from — these activities used to be where a strong paid search team distinguished itself. 

Increasingly, they’re hours spent simply working against the direction the platform is going. Time is better spent building the inputs that actually shape how well the automation performs.

Customer Research: The Input the Algorithm Can’t Generate for You

An inference-driven system like Google Ads is only as good as its understanding of who a good customer actually is, and that understanding has to come from somewhere outside the platform. Google’s models can read landing pages and search context, but they can’t tell you why your best customers actually buy, what objections come up in a sales call, or which segment of “qualified lead” churns six months later.

This is where you can build real leverage now: analyzing which queries and audiences are actually converting into revenue, not just clicks; building a feedback loop with sales or CRM data on lead quality; and talking to actual customers about why they chose you. That research becomes the intent signal that the algorithm optimizes from.

Creative and Asset Quality Fuels Ad Algorithms

Automated targeting and automated creative both work from the same underlying material: your landing pages, ad copy, sitelinks, and other assets. A well-targeted campaign built on a thin or generic asset library still caps out quickly, because there’s little for the system to differentiate or learn from.

Practical work here includes disciplined creative testing rather than a “set it and forget it” asset library, landing page relevance work that gives automated matching something specific to read signal from, and active management of pinned or excluded content so brand-critical language stays intact even as the system generates more around it. 

This is ongoing work, and it’s a better use of a paid search team’s time than another round of match type adjustments.

First-Party Data: The Signal Automated Bidding Actually Needs

Automated bidding systems optimize toward whatever conversion data they’re given, and a thin or generic signal produces thin, generic optimization — a pattern paid search teams are increasingly running into across bidding, not just matching. 

Conversion action hygiene, accurate value reporting, and clean audience and feed data all directly shape how well an inference-driven system performs, in the same way that landing pages and creative do.

The Job of a Paid Search Manager Shifted

The value of precise keyword by keyword optimization is eroding, but the skill underneath that precision — understanding intent, building the inputs that shape performance, and checking results rigorously — hasn’t gone anywhere. 

Modern paid search managers have shifted focus to research, creative and asset quality, first-party data, and measurement.  

If your team is still spending most of its time on keyword and match type management, or you’re not sure your account is set up to feed AI Max and Performance Max well, Silverback can help you figure out where your paid search hours are actually worth spending. Get in touch with our team to talk through what that shift looks like for your account.