A seller on r/Google_Ads posted this fall about their tallow balm ads. They used to show for "tallow cream." Then Google started matching them to pharmacy brands and generic face creams.
One reply put it plainly: exact match "isn't truly exact anymore."
That's semantic matching from the side where it costs money. I wanted to see the organic side, so we ran a small test.
The setup
On October 19, 2026, we searched Google from the US for 14 pairs of queries. Both queries in a pair mean the same thing. In most pairs, Google Ads volume says one phrasing is common and the other rare.
For each side we pulled the top 10 results and read the titles. The question was simple: did the rare phrasing's results use the searcher's words, or the common ones?
Eight of the pairs, with monthly searches:
| Rarer phrasing (monthly searches) | Common phrasing (monthly searches) | Top results for rarer phrasing titled only in common words |
|---|---|---|
| inexpensive airfare to london (no data) | cheap flights to london (6,600) | 10 of 10 |
| customer management software for small companies (no data) | best crm for small business (2,900) | 8 of 8 |
| reduce hypertension without medication (40) | how to lower blood pressure naturally (33,100) | 7 of 8 |
| egg cooking time (2,900) | how long to boil an egg (18,100) | 7 of 7 |
| sneakers for fallen arches (720) | running shoes for flat feet (4,400) | 6 of 7 |
| get merlot out of rug (no data) | remove red wine stain from carpet (140) | 5 of 6 |
| notebook computer not powering up (no data) | laptop won't turn on (6,600) | 5 of 7 |
| car makes noise when braking (720) | squeaky brakes (18,100) | 0 of 8 |
The common-words pull
In the 11 pairs with a clear rare side, 56 of 83 top results were titled only in the common words. That's about two in three.
Nobody who searched "inexpensive airfare to london" saw "inexpensive" in a title. They got ten pages about cheap flights.
It never went the other way. For the common phrasings, 0 of 90 results were titled only in the rare words.
Where it stopped
"Car makes noise when braking" kept its own wording, 0 of 8 swapped. Plenty of pages already use that exact phrase.
"How to fix a dripping tap" mostly got pages with "tap" in the title, several from UK sites, even from a US search.
So the pull seems to kick in when a phrasing doesn't have many pages of its own.
Same meaning, different pages
This was the part I didn't expect. Across all 14 pairs, the two top 10 lists shared 7 URLs out of 207. Never more than one per pair.
That's roughly 3%. Google matched the meaning, but the exact words still picked most of the pages.
Embeddings, tested on a laptop
Most of this runs on embeddings: text turned into vectors, where similar meanings sit close together.
We ran three free models locally: bge-small, bge-large and mxbai-large. For three queries, each one ranked five short sentences. One answered the query in other words. The other four were decoys that shared the query's words.
bge-small picked a decoy every time. For "how to fix a dripping tap" its top pick was "Tap the screen twice to fix the dripping paint effect."
The bigger two got the right sentence first in two of three queries each. The margins were small, though. On the faucet query, bge-large scored the decoy 0.730 and the real answer 0.715. mxbai-large lost the CRM query by 0.004.
Margins like that are one reason a lot of search systems mix vector scores with plain keyword matching, which people call hybrid search.
ChatGPT and AI Overviews
Same day, we sent all 29 queries to ChatGPT through a data provider's ChatGPT feed. It searched the web for 9 and answered 20 from memory.
When it searched, the rewrite was right there. "Inexpensive airfare to london" went out as a search for "cheap flights to London."
From memory, it hedged by naming both words: "notebook computer (laptop)," "dripping tap (faucet)."
Google's AI Overviews showed up on 27 of 28 pair searches. In the 13 pairs where both sides got one, the overviews shared 27 of 179 sources, about 15%. More overlap than the blue links, but still mostly set by wording.
What I'd change on a site
Put the phrasing most people type in the title and main headings. It soaks up the rare searches too.
Use the other phrasings in the body. One page can say airfare, budget tickets and cheap flights without reading like a keyword list.
Keep one page per meaning. Two pages on one site chasing the same meaning mostly split the same searches.
The exception is a phrasing with lots of its own pages, like "car makes noise when braking." With only 3% of URLs shared between pairs, that one can earn its own section.
Write passages that still make sense lifted out of the page. Google's ranking systems guide lists passage ranking, and AI answers usually quote a passage.
Where literal matching still matters
Model numbers, part numbers, error codes and quoted phrases. When we tested search operators earlier, Google mostly kept quotes, and ChatGPT dropped them while rewriting the search. An exact model number can get lost the same way.
Spell product names the same everywhere. A system matching on meaning can read two spellings as two products.
Limits
One day, one US location, 14 pairs. Search volume comes from Google Ads, which is an estimate. The embedding check was 3 queries with 5 sentences each, enough to see the failure, not enough to rank the models. We didn't rerun anything.
Doing it by hand
Our rough estimate is 11 to 18 hours a month: finding the common phrasing per topic, folding other phrasings in, merging pages that chase the same meaning and tightening answers.
RankControl, an AI SEO and AEO platform (https://rctrl.com), can take the repetitive part. RankControl's content engine writes 1,500 to 4,000+ word articles in your brand's voice, based on the pages already ranking. RankControl's free GEO Checker scores how likely AI is to quote a page, 0 to 100, with the five weakest passages.
All 14 pairs and the full method: RankControl's semantic search guide.
