There is no single result page for a keyword. Google resolves relevance using hundreds of factors including location, language, and device, and the AI layer deciding which sources get quoted reads the same signals without publishing a position you can check. Optimize for the intent first, apply location signals only where a real regional difference exists, and measure citations under conditions you actually record.
Geo-targeting and GEO are not the same thing
Two terms in this field share three letters and mean different things. GEO, generative engine optimization, is the practice of earning citations inside AI-generated answers. Geo-targeting is the older and narrower idea of shaping what a searcher sees based on where they are. This post is about the second one, and about how the first one changed it. If you want the acronyms pulled apart properly, we did that in our guide to AEO, GEO and SEO.
The distinction matters because the two now interact. Location has always shaped search results. What changed is that a second layer now sits on top, deciding which sources get quoted, reading the same location signals, and publishing no position you can go and check.
Why the same query returns different results
That has been true for years, and it is documented rather than inferred. Google states plainly that relevance is determined by hundreds of factors that can include the user's location, language, and device, and it maintains a help page explaining to searchers why their results differ from other people's.
Location reaches the query from several directions at once. The device can report a precise position. A signed-in account can carry a home or work address. Failing both, Google falls back on the location from the last session, cached in a cookie that expires after six hours. None of that is under your control, and all of it feeds the result your buyer actually sees.
So a rank-tracking number is an average of a distribution, reported as a fact. It is still useful. It is just a much weaker claim than the dashboard makes it look.
This has a practical edge. If your sales team says buyers in one region never find you while your reporting shows position three, both can be true at once. The fix is not to argue with the dashboard. It is to sample the query the way those buyers actually issue it, from their location and their device, and to treat the gap between the two figures as the finding.
What location signals actually do
Location signals do not make a page rank. They tell an engine who a page is for, which is a different job. Google's guidance on multi-regional and multilingual sites names the ones that count: server location, local addresses and phone numbers on the page, local language and currency, links from other local sites, and Business Profile signals.
Read that list again and notice what is missing. Not one of those signals is a keyword. Putting a city name into a title tag is not a location signal; it is a string. The signals that work are structural facts about who you serve, which is why they are harder to fake and worth more when they are true.
Why intent outranks keyword frequency
The shift from matching strings to resolving intent is the part most SEO advice still gets backwards. An engine assembling an answer is not counting how many times you said the phrase. It is deciding whether your page settles the question well enough to quote.
The consequence shows up in behavior. Pew Research Center, analyzing the browsing activity of 900 US adults, found that people who saw an AI summary clicked a traditional result in 8% of visits, against 15% for those who did not. They clicked a link inside the summary itself in 1% of visits, and ended the session entirely on 26% of pages carrying a summary, compared with 16% without one.
That is the real change. The click is no longer the reward for ranking. Being the source the answer is built from is, and that is decided by whether your content resolves an intent, not by how densely it repeats a term.
Structure decides what an engine can read
An engine cannot quote what it cannot parse. Site structure is upstream of every other optimization here, because it determines whether the relationships between your pages are legible at all.
- Give every distinct intent its own URL. A page covering four questions is quotable for none of them.
- Keep headings honest. A heading should name the question the section answers, so the section can be lifted out on its own.
- Link between related pages in body text, so the engine can see which pages belong to the same topic.
- Where a page genuinely serves a region, say so in the content itself, in the address, the currency, and the language, not in the keyword slot.
None of this is new advice. What is new is the penalty for skipping it: a page an engine cannot cleanly extract does not lose a few positions, it simply never appears in the answer.
A useful test costs nothing. Take any section of a page, read it with no surrounding context, and ask whether it answers a question on its own. If it needs the paragraph above it to make sense, it will not survive being lifted into an AI answer, and the page will lose the citation to a competitor whose section stands alone.
How to optimize for intent and location together
Treat them as two separate questions and the work gets much clearer.
- Intent first. Write the page that resolves the question completely. This is what decides whether you get cited at all.
- Location second. Apply the structural signals only where a real regional difference exists, in price, availability, regulation, or service area. Inventing one produces thin duplicate pages.
- Measure what you can, and label what you cannot. Rankings vary by user and AI answers vary by run. Sample deliberately, and record the conditions you sampled under.
The last point is the one teams skip. A visibility number with no record of where and when it was measured cannot be compared against next month's number, which makes the whole exercise decorative.
How Pressfit approaches AI search visibility
Our AI visibility work runs in four stages: discover, audit, strategize, optimize. The competitive analysis maps who gets mentioned across Google, ChatGPT, Claude, and AI Overviews, and analyzes citation authority so you can see which sources the engines actually trust in your category. Our content audit covers technical health, content quality, and keyword coverage page by page. Our content gap analysis scores opportunities by difficulty and returns briefs with target keywords and volumes, plus the categories where advertising or earned PR is the better play than publishing.
One thing worth stating plainly, because it bears directly on this topic: our measurement samples the United States at the national level. We do not currently report how citations vary between cities or regions. If a vendor tells you they track your AI visibility by locality, ask exactly which locations they sample, how often, and how they separate a real regional difference from the ordinary run-to-run variation these systems produce. Those questions are answerable, and the answer tells you what the number is worth.
Where this goes next
Personalization is not going to reverse. Results will keep splitting by intent, context, and location, and the share of questions resolved without a click will keep climbing. The teams that hold visibility through it will be the ones who stopped optimizing for a position and started building pages that are worth quoting, then measured citations honestly enough to tell progress from noise.
If you want to know which sources the engines currently trust in your category, and where your gaps are, talk to us.
FAQ
Is geo-targeting the same as GEO?
No, and the overlap in letters causes real confusion. GEO stands for generative engine optimization, the practice of earning citations inside AI-generated answers. Geo-targeting means shaping what a searcher sees based on their physical location. A page can need both, but they are different disciplines with different signals.
Why do two people searching the same term see different pages?
Because relevance is resolved per query, not per keyword. Google draws location from the device, from a signed-in account's saved addresses, or from a cached location left over from the previous session that expires after six hours. Language and device type feed in as well, so the result is assembled for that searcher rather than looked up in a fixed table.
Do location keywords in titles improve local visibility?
Rarely on their own. The signals Google documents for regional relevance are structural: server location, real addresses and phone numbers on the page, local language and currency, links from other local sites, and Business Profile data. A city name dropped into a title is a string, not evidence of who you serve.
How should visibility be measured when results vary this much?
By recording the conditions alongside the number. A citation count with no note of where it was sampled, when, and against which engine cannot be compared to next month's figure. Sample deliberately and label the sample, so you can tell a genuine movement apart from ordinary run-to-run variation.