The Market Leader Loses the Local Mention to Whoever Did the Location-Level SEO Work for Months

A restaurant chain came to our Solutions team with two locations in the same town. One was permanently closed; one was open. Google kept showing searchers the closed one. Seriously frustrating for them. Their plan was to push new reviews to the open location until it caught up. Our team's diagnosis took minutes and had nothing to do with reviews. Both listings, in fact, pointed to the wrong URLs, and the open location had no local page of its own. Talk about mixed messages.

This meant that Google and AI tools were reading a confused story about that brand's presence in that town, and their search performance was suffering. That was a risk when Google's Local Pack was the only thing reading location data. It's an even bigger risk now that AI models read that information too — and they generate a shortlist of local businesses, not endless search results.

We've watched this completely avoidable mistake play out across the 1.4 million business locations we manage. It happens so easily when there are tens or hundreds of other locations to manage, and the strategy is to embrace new projects, rather than fix what's already online.

I've rewritten the post we first published in 2024 (it feels like a long time ago), using this year's insights from Uberall Academy. Because local SEO matters even more now than it did then — and multi-location brands still aren't sure where "AI" belongs in the acronym "SEO", just because it's missing those two vowels. What is local SEO for multiple locations and who owns it?

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Isn’t Today All About AI Search, Not Local SEO?

Local search engine optimization is the work of making each business location findable when someone nearby searches for what it sells. That could be plumbing services, a dentist appointment, or a fresh and zesty poke bowl.

It's easy to forget that you can drag and drop that same simplified definition onto local generative engine optimization (GEO or AEO). Even if the output is different — a highly specific answer rather than a ranked list — the end goal is absolutely the same; make no mistake. It's to maximize customers or sales on-site.

Clicks are down, but crawling is up. That means AI systems are reading more of your location data than customers did. Businesses might not have the same traffic split helping them achieve their end goal, but that doesn't matter in the slightest if they're already doing a lot of the critical location-level SEO work.

For example, our AI visibility research shows Google increasingly swapping the Local Pack for AI Overviews — and a location that isn't the AI's choice is essentially invisible, even when it technically ranks third or fourth on the page.

Our Technical Product Manager Luma untangles the SEO acronyms search practitioners use interchangeably today — SEO, GEO, AEO, LLMO — on the Local Marketing Beat. She uses a librarian analogy to describe what they all mean in relation to each other.

"GEO is about making sure your content is so clear, so structured, and authoritative that AI — the librarian — summarizes you accurately and favorably to the end user. … SEO isn't going away — it's becoming the foundation. This is your book. It's now being summarized by GEO and recommended by LLMO. If the librarian can't find your book in the first place, you're not going to be recommended."

And so we come back to this: Local search optimization in the AI era is still about the work of making each business location findable when someone nearby searches for what it sells.

AI Models Don't Reliably Recommend the Big "Obvious" Brands

Market share does not predict AI mention share.

Our AEO/GEO Analyst Katya Shishchenko measured that this year across five verticals — hotels, restaurants, dentists, grocery stores, and banks. You can find out more about this experiment here.

She found, for instance:

• One national bank holds roughly 73% of the deposits in its home market — and earns about 12% of the AI mentions there.
• Hotel chains control two-thirds of New York's rooms and collect a sixth of the mentions.
• Dental chains own up to a fifth of Chicago's practices and scrape together 4.4%.

Even if you're an enterprise business with thousands of locations, you still have to do all the local work to dominate AI answers.

When most AI models "decide" whether to mention a business — a plumber, dentist, or poke bowl lunch spot — they're typically asking three separate questions: Who's the brand, what can I verify about a specific location, and what does this customer need right now?

• Brand-level signals: Recognition, scale, reputation
• Location-level signals: Data accuracy, recent activity, review sentiment
• Context: The user's intent, proximity, and what they need right now

AI models look beyond the brand — at proof points that justify recommending a local business. That's accurate and consistent location data, updated business listings, rich, specific content, and great review volume. They're looking for the business that can best deliver the user's needs before generating that shortlist.

For a franchise restaurant chain, AI models look for this:

• Brand-level signals: The national reputation, the thousands of locations, the ads, the excellent brand recall from years' worth of online and offline marketing campaigns
• Location-level signals: Claimed and complete listings across multiple directories, including industry-specific ones; regular promotions and events posted across social or listings; a steady flow of reviews
• Context: The customer's part — they might be searching at 11pm, half a mile away, wanting somewhere open late with a drive-thru

In the restaurant industry, Katya actually found that 95.7% of restaurant AI mentions are location-specific, making this entire section on how AI models work unignorable.

How do you know if your multi-location brand is guilty of only fulfilling the brand part of the equation?

Big brands often promise services nationally but don't make it clear whether these are available at the location level. They have strong brand sentiment sitting on top of thin and sporadic local reviews, and their marketing efforts go toward generating search visibility for the brand in local searches, not for locations. Website content might address nationwide FAQs but not location-specific FAQs. I think you know where I'm going with this.

Location-level evidence is what pulls the best brands apart from their local competitors, but ultimately winning on all three fronts is necessary for consistent local SEO and AI visibility performance.

How to Do Local SEO for Multiple Locations: Biggest Mistakes

The criterion for visibility success for enterprise or multi-location businesses is therefore addressing these three questions at scale: Who's the brand, what can I verify about a specific location, and what does this customer need right now?

We've seen multi-location teams fail to a) answer these questions, b) answer these questions at scale, and thereby self-sabotage their AI visibility in the same four mistakes. They come in roughly this order:

1. Incomplete or Incorrect Profiles

Every location needs one claimed, verified profile on Google and Apple. If a location contains departments, each department gets its own located-in listing with its own phone number and category — without duplicating the parent's location data.

This goes wrong all the time. We audited one brand with roughly 600 branches and found their Apple Maps listings were unclaimed. Nobody at that company controlled listings across an entire online platform.

Profile completeness is critical. In Katya's research, for example, hotels with 6 to 10 profile attributes got mentioned 22% of the time. At 31 to 50 attributes, that jumped to 94%. We're not saying "more is more" — as much as that's exactly what this sounds like. Add as many attributes as is relevant and possible for your business category.

A business description alone created a 3× mention gap for the grocery stores she analyzed, and GBP photo count turned out to be the strongest predictor of restaurant mention frequency.

Who owns this: The SEO manager sets the structure — one profile per location, located-in listings, attributes, optimized business description. A listings platform enforces this work, because nobody in their right mind audits 600 listings manually.

2. Brand Pages Instead of Local Pages

Every location needs its own page — it's the one place on the web you fully control where both Google and AI models can go to confirm a location exists, what it offers, and when it's open.

A store without its own local landing page sends searchers and AI models to a generic brand homepage that proves nothing about that address or its services. Rich, specific, vivid content enables search systems to summarize each location accurately — and recommend them confidently.

Then add LocalBusiness schema so machines parse the address, hours, and services instead of guessing at them. Humans read your front end; AI reads your back end. Both need to be right.

Who owns this: Content and SEO together. SEO owns the structure and schema, content owns making 500 pages that don't read like the same page 500 times.

3. Stale Review Activity

A steady flow of recent reviews is the "recent activity" evidence AI models check per location.

Katya's research told us that review volume consistently predicts AI mentions across the five verticals she studied; star ratings didn't. So put away your five-star fixation, and put more energy into generating and answering reviews at every location.

Who owns this: Local store managers and customer service or whoever is face-to-face with customers. HQ might set the on-brand response standard and monitor review sentiment.

4. Not Enough Monitoring

The job isn't just about uploading and submitting clean location data.

No one on your team is to blame for opening hours getting overwritten by third-party data sources, map pins getting dragged by "suggested edits," or duplicate and closed listings creeping back next to open ones.

But that doesn't mean no one should be monitoring and fixing them — if they stay incorrect, that affects your trustworthiness to search systems.

Who owns this: Your data or listings manager, if you have one — but more critically a listings platform that flags errors or changes.

Notice the common denominator from that list: Businesses benefit from hyperlocal efforts; they just run into the scale problem. Luma puts this structure perfectly, when she suggests "It requires a centralized strategy with decentralized execution" — decentralized teams carry out their best work on a centralized platform built for enterprise businesses. That way, you get brand alignment with all the local content quirks.

Local SEO Still Decides Who Gets Recommended in Search

That restaurant chain didn't need a thousand reviews to appear as open and ready to greet customers. That team could've spent months fixing that, and it would've had minimal impact on their search performance.

They needed to cover the brand, location-level, and contextual information and fix (essentially) three of the four common mistakes we described above.

Local SEO is about making each location findable when someone nearby searches for what it sells — like any of the acronyms. It’s just that now, before recommending you, an AI model checks whether your address and hours are right, whether recent reviews and updates prove the location is alive, and whether sending someone there could end in disappointment.

In AI search, brand authority and scale aren't the deciding factor behind that recommendation. If you want to see how your locations look to AI models today, GEO Studio shows you before your customers' AI-generated conversations do.