Key takeaways
- A national or brand-level AI visibility score can look healthy while individual stores or offices are invisible in the metros where revenue actually gets decided. Measurement has to happen at the location level, not just the brand level.
- ChatGPT and Google AI Overviews source local answers very differently. ChatGPT leans on directories like Expertise.com and Angi (84% of its cited local answers include a directory or review site); Google leans on its own properties and the business's own website (92.5% of AI Overviews answers cite the business site directly). You need separate playbooks for each.
- Domain authority is not the deciding factor in who gets cited. Promptwatch's citation data shows mid-authority domains (DR 46-75) take almost half of all ChatGPT citations, while the biggest sites have been losing share since August 2026. A well-built local page can out-cite a national competitor's homepage.
- Prompt design makes or breaks your dashboard. "Best plumber in Denver" and "best plumber near me" are different prompts that trigger different AI behavior, and both need tracking, city by city, not just once at the brand level.
- Tool choice depends on whether you need a measurement dashboard or a team that also fixes the reviews, listings, and local pages behind the score. Full-stack platforms, geo-grid trackers, and done-for-you services each solve a different part of the problem.
Your Seattle store is fine. Your Denver store doesn't exist to AI. Now what?
Here's the thing nobody tells you when you roll out a shiny "AI visibility dashboard" for a 40-location brand: one score at the top of the report is almost always lying to you. Not maliciously. It's just math. Average 40 locations together and the three that are crushing it in ChatGPT answers will quietly cover for the twelve that never get mentioned at all.
I've seen this pattern over and over in the research for this piece. A home-services company doesn't win a single national answer to "who's the best plumber." It wins or loses a specific question ("emergency plumber Tulsa") in a specific city, against specific local competitors, based on specific local signals: how many recent reviews it has, whether its location page actually mentions Tulsa anywhere other than the title tag, whether the review that AI keeps quoting says something quotable.
V9 Digital put it well: brand authority does not transfer evenly across markets, because search engines (and now AI engines) treat each location as its own entity with its own citation strength, its own review pattern, its own proximity signals. Copy-pasting a location page template and swapping the city name doesn't fix that. It never did for Google Maps, and it definitely doesn't for ChatGPT or AI Overviews.
Why brand-level AI visibility hides local revenue risk
Birdeye's framing of this problem is the sharpest one I found in the research: AI visibility "drift" happens quietly. Review velocity slows at one location. Sentiment weakens. The location page goes stale. None of that shows up in a brand-wide mention-rate chart, because the other locations are still performing. But it absolutely shows up when a customer in that specific city asks ChatGPT or Google a question and gets sent to a competitor.
There's a genuinely unsettling example in Birdeye's research: a brand-new burger restaurant with barely any reviews outranked long-established chains for "best burger in [city]" because a single detailed review explicitly used that exact phrase, with photos attached. AI models love a specific, well-written, recent claim more than they love decades of brand equity. That should change how you think about what "authority" even means at the local level.
The practical consequence: you cannot manage location-level risk with a brand-level number. You need a score, or at minimum a tracked set of prompts, per city, compared against the local competitors that actually show up in those answers.
ChatGPT and Google AI Overviews source local answers completely differently
This is the part of the research that should reshape your whole local GEO strategy, and most guides on this topic skip right past it.
According to a first-party study cited in the research (Cheers' Market Baseline, built from over 9,400 ChatGPT answers to home-services questions across 100 US and Canadian metros), ChatGPT's local answers are directory-heavy. Expertise.com showed up in 32% of answers, Angi/HomeAdvisor in 29%, HomeStars in 16% (Canada), BBB in 14.5%. Eighty-four percent of ChatGPT's cited local answers included at least one directory or "best of" list. Yelp, surprisingly, appeared as an actual citation in only 0.6% of answers, even though it's often the rating ChatGPT displays on its map card.
Google AI Overviews and AI Mode work almost the opposite way. In the same study, Google's own properties appeared in 95.5% of answers and the business's own website in 92.5%. Yelp showed up in nearly 20% of Google's answers. Directories mattered far less overall, only 38% of cited Google answers referenced a directory, compared to 84% for ChatGPT.
That means your GEO work for ChatGPT visibility is mostly about getting listed, verified, and well-reviewed on the directories that ChatGPT actually cites in your category, not about polishing your own homepage. Your GEO work for Google AI Overviews is closer to classic local SEO: a strong, specific location page and a healthy Google Business Profile and Yelp presence matter a lot more there.
Promptwatch's own citation-type research backs up how fragmented ChatGPT's sourcing has become. Nearly half of ChatGPT responses with citations (47.6%) cite ten or more different domains, and for "organic," non-branded prompts, which is exactly the shape of most local "best [service] in [city]" queries, that climbs to 52.5%. You are not competing for one slot. You're competing to be one of eight or more sources an AI model stitches together per city.
Domain authority isn't the moat you think it is
If your instinct is "we're a big national brand, our domain authority will carry us into these answers," the data says otherwise. Promptwatch's August 2026 breakdown of ChatGPT citation share by domain rank shows DR 46-60 and DR 61-75 sites together pulling in almost half of all citations, while the very top bracket, DR 91-100, dropped from about 7% of citations in week one of the measurement period down to roughly 3% and stayed there. Smaller sites in the DR 0-30 range still earned about 14% of citations.
For a multi-location brand, that's good news and bad news. Bad news: your enterprise domain authority will not carry a weak Tulsa location page into AI answers on its own. Good news: a specific, well-structured, recently updated location page genuinely competes, even against bigger national names, if it answers the actual question better.
What to actually track, city by city
Based on the research, a working local GEO tracking setup needs four layers, run per location, not just per brand.
1. The right prompt shapes, per city
Aleyda Solis makes a point that's easy to ignore: a prompt library that's unrepresentative, say, only branded questions, or only generic "best of" lists without a place name, will make your dashboard look useful while pointing you at the wrong priorities. Buffy's research draws a useful line between explicit city-named prompts ("best dentist in Austin") and implicit ones where the model infers location from context ("best dentist near me"). Both need testing, and "near me" prompts should be run from an actual location context to be realistic, not just assumed.
For a franchise or multi-location brand, your prompt set per city should look more like:
- "best [service] in [city]"
- "top [category] near me" (tested from that city's context)
- "which [brand type] is reliable in [city]"
- comparison prompts naming you against the specific competitors active in that market
Cheers' own production data is a good cautionary example here: across 13 home-services companies, "best or top" list prompts without a place name produced a 28.4% pooled appearance rate, but Cheers deliberately doesn't publish a number for "near me" or named-place prompts because too few companies even track that prompt shape consistently. Ask any vendor exactly which prompt shapes sit behind the percentage they show you. A visibility score built on the wrong prompt mix isn't lying outright, but it isn't telling you anything useful either.
2. A score and a rank per location, not a single brand average
Social Places' approach is a clean illustration of what location-level reporting should look like in practice: a visibility score and local rank for each outlet, benchmarked against the competitors that actually show up in that city's answers, so you can see which stores need attention instead of guessing.
| Location | Visibility score | Local rank |
|---|---|---|
| Waterfront | 81 | #1 |
| Fourways | 75 | #2 |
| Clearwater | 73 | #2 |
| Century City | 72 | #3 |
| Constantia | 71 | #2 |
| Gateway | 70 | #4 |
That kind of table is the whole point of local GEO tracking. A single brand-wide number would have hidden the gap between Waterfront and Gateway entirely.
3. Which sources are getting cited, per city
Knowing you're visible is step one. Knowing whether ChatGPT is citing a directory, a review site, or your own location page is what tells you what to fix, and where. If a city's answers keep citing Angi and never your site, the fix is a listing problem. If answers cite your homepage instead of a location page, the fix is a content and internal-linking problem.
4. Crawler access and technical basics, per market
OpenAI's own documentation confirms ChatGPT's live search layer estimates a user's location from IP address and shares it with third-party local data providers, then rewrites "good restaurants near me" into something like "top restaurants San Francisco." If your site blocks OAI-SearchBot in robots.txt, you're opted out of live ChatGPT search answers entirely, regardless of how good your content is. That's a one-line fix that too many sites get wrong by accident.
Tool options, by what they actually do for multi-location brands
Not every AI visibility tool handles location-level tracking. Several are brand-level only and will happily sell you a dashboard that can't answer "which of our 40 stores needs help this month."
| Tool type | Examples | Location-level tracking | Fixes the problem, or just measures it |
|---|---|---|---|
| Full-stack local AEO/GEO platform | Uberall GEO Studio, Yext | Yes, built in on higher tiers | Measures plus publishes AI-ready content |
| Geo-grid rank tracker | Local Falcon | Yes, grid-based (9 to 441 points) | Measurement only, by design |
| Done-for-you local GEO service | Cheers, Birdeye | Yes, core to the model | Manages reviews, listings, and pages directly |
| Brand-level AI visibility platform | Profound, Semrush AI Visibility Toolkit | Limited or plan-dependent | Mostly measurement, some content tooling |
| End-to-end GEO platform with agentic content | Promptwatch | City/state tracking on Professional plan and above | Measures, generates content, and publishes to CMS |
Promptwatch sits closer to the full-stack category than most pure trackers. It tracks prompts with state and city-level granularity, logs when AI crawlers actually visit your location pages (which most of the trackers in that table can't show you at all), and its Content Agents can generate and publish location-specific GEO content straight to Webflow, Framer, or WordPress rather than leaving you with a spreadsheet of gaps and no one to close them. For a brand managing dozens of locations, the crawler log data alone answers a question none of the pure monitoring tools can: is ChatGPTBot or PerplexityBot even reading your Tulsa location page, or is it hitting a 404 and giving up.
For teams that want a geo-grid style view specifically for map-pack and local AI Overview positioning, Local Falcon is still the purpose-built tool, with its Share of Local Voice metric showing what percentage of grid points around a location actually surface your brand in the top results. It's measurement only though. Someone still has to act on what it finds.
For brands that would rather hand the whole loop, reviews, listings, local pages, citations, to a managed team, Cheers and Birdeye both frame themselves as done-for-you operators rather than software-only vendors, which matters if you don't have the internal headcount to chase 40 locations' worth of fixes every month.
Pricing reality check
Location-level AI visibility tracking isn't cheap once you're past a handful of outlets, and pricing models vary a lot by vendor.
| Vendor | Entry price | Location-level tracking | Model |
|---|---|---|---|
| Uberall GEO Studio | $109/mo (Starter, no location tracking) | Yes, from $449/mo Scale tier | Credit and prompt based |
| Local Falcon | ~$25/mo | Yes, core feature | Credit based, unlimited locations/keywords |
| Cheers | $750/mo (up to 3 locations) | Yes, core model | Done-for-you, scoped |
| Promptwatch | $95/mo Essential | State/city tracking from Professional ($245/mo) | Prompt and response based |
| Profound | ~$99-399/mo reported | Limited, plan-dependent region caps | Prompt based |
| Yext | $199-999/yr self-serve; enterprise $10k+/yr | Yes, benchmarks per location | Platform fee plus usage |
Before you sign anything, Uberall's own buyer's guide makes a point worth repeating: ask what prompt shapes are behind the vendor's headline visibility percentage, and ask for cost per AI answer per month, since that's the only number that's genuinely comparable across quotes with wildly different credit systems.
A reasonable rollout plan
For a brand with, say, 20 to 200 locations, trying to do this all at once is how these projects die. A sequence that actually works:
- Pick your 10 highest-revenue or highest-risk markets first. Build the real prompt set for each, explicit city-named and implicit "near me" versions, plus competitor-comparison prompts.
- Separate your ChatGPT strategy from your Google AI Overviews strategy. Directory presence and reviews for ChatGPT; strong location pages and Google Business Profile health for AI Overviews.
- Check crawler logs before you assume a content problem. If OAI-SearchBot or GoogleOther is never reaching a location page, no amount of rewriting will help until that's fixed.
- Roll the same process out to the next tier of markets once you've validated it works on the first ten. Don't try to instrument all 200 locations simultaneously, you'll drown in data you can't act on.
- Re-check your prompt set every quarter. The mechanics shift fast, Promptwatch's own data shows ChatGPT started using the site: search operator in roughly 17% of its query fan-outs almost overnight in August 2026, and Reddit's share of ChatGPT citations collapsed from around 4% to under 1% in the same window. A prompt library built on last year's assumptions about how these models search will quietly go stale.
If you want to browse more of the category before committing, the GEO software directory at bestgeosoftware.com lists the broader field of platforms, and the AI rank tracking directory at ai-rank-tools.com is a reasonable second stop if what you actually need is grid-based rank tracking rather than a full execution layer.
The honest bottom line
A brand-wide AI visibility score is a vanity metric for multi-location businesses. It tells you something happened somewhere, not where you're actually losing customers. The brands getting this right in 2026 treat every location as its own small business with its own citation profile, its own review velocity, and its own prompt behavior, then build the measurement and the fixes around that unit, not around the logo at the top of the page.