Key takeaways
- ChatGPT and other AI search engines respond differently depending on the user's location, which means your brand visibility varies city by city -- and that gap is measurable.
- Cities where competitors appear in AI responses but you don't represent unmet demand you can capture before expanding physically.
- Combining AI visibility data with traditional location signals (search volume, foot traffic, demographics) gives you a more complete picture than either source alone.
- Tools like Promptwatch let you monitor AI responses across cities and regions, so you can see exactly where your brand is missing from the conversation.
- The goal isn't just to track gaps -- it's to create the city-specific content that closes them.
Expansion decisions used to rely on a familiar toolkit: census data, foot traffic studies, competitor density maps, and gut feel. That toolkit still matters, but there's a new signal that most businesses are ignoring entirely.
When someone in Austin asks ChatGPT "what's the best [your category] near me" or "who should I use for [your service] in Austin," ChatGPT gives an answer. That answer either includes your brand or it doesn't. And right now, for most businesses, it doesn't -- especially in cities where you haven't established a strong local content presence.
That's a problem. But it's also an opportunity. The cities where AI search consistently recommends your competitors but not you are telling you something: there's demand, there's a conversation happening, and you're not in it yet.
This guide walks through how to use city-level AI visibility data as a genuine input for expansion planning -- not a vanity metric, but a practical signal that can sharpen where you invest next.
Why AI search responses vary by city
ChatGPT, Perplexity, Google AI Overviews, and similar tools don't give everyone the same answer. Responses are shaped by the user's location, the phrasing of their query, and what content the model has indexed and weighted for that context.
A query like "best accounting firm in Chicago" will pull from different sources than "best accounting firm in Denver." If your website has strong content targeting Chicago but nothing for Denver, you'll likely appear in one response and not the other -- even if you serve both cities.
This creates a measurable, city-by-city visibility map. Where you appear, you have AI-driven brand awareness. Where you don't, you're invisible to a growing segment of buyers who are using AI as their first research tool.
The scale of this matters. Shoppers complete purchases 47% faster when assisted by AI, according to research cited by Surfer Academy. That means AI search isn't just a discovery channel -- it's compressing the buying cycle. If you're not in the AI response for a city, you're losing buyers before they even know you exist.
How to collect city-level AI visibility data
Manual testing (free, limited)
The simplest starting point is running location-specific prompts yourself. Open ChatGPT, Perplexity, or Google AI Mode and ask questions the way your customers would:
- "Who are the top [your category] companies in [city]?"
- "Best [your service] in [city] for [persona]?"
- "What [your product] brands are popular in [city]?"
Do this for 10-15 cities you're considering for expansion. Note which competitors appear, how often, and whether your brand shows up at all. This is slow and doesn't scale, but it gives you a real feel for the data before you invest in tooling.
Systematic tracking with AI visibility platforms
Manual testing breaks down fast once you're tracking more than a handful of cities. This is where dedicated AI visibility platforms come in.
Promptwatch supports state and city-level tracking on its Professional plan and above, letting you monitor how AI models respond to location-specific prompts across multiple cities simultaneously. You can set up prompts like "best [category] in [city]" for 20 cities at once and see your visibility score for each -- along with which competitors are being cited and which sources they're drawing from.
The city-level data Promptwatch surfaces includes:
- Which AI models mention you in each city (ChatGPT vs. Perplexity vs. Google AI Overviews often differ significantly)
- Which competitors are consistently cited in cities where you're absent
- Which pages on your site (or your competitors' sites) are being used as sources
- How visibility changes over time as you publish new content
Other tools worth knowing about for AI visibility tracking:
Otterly.AI
Profound
The table below shows how these platforms compare on city-level and multi-location tracking specifically:
| Platform | City/region tracking | Competitor comparison | Content gap analysis | AI writing agent |
|---|---|---|---|---|
| Promptwatch | Yes (Professional+) | Yes, with heatmaps | Yes (Answer Gap Analysis) | Yes |
| Rankshift | Limited | Basic | No | No |
| Otterly.AI | No | Basic | No | No |
| Profound | Yes (Enterprise) | Yes | Limited | No |
For most marketing and expansion teams, Promptwatch is the practical choice -- it's the only one that goes from "here's where you're invisible" to "here's the content that will fix it."
Reading the data: what city-level gaps actually mean
Once you have visibility data across cities, you need to interpret it correctly. Not every gap is an expansion signal. Here's how to read what you're seeing.
High competitor visibility, low yours = active demand, you're missing it
If ChatGPT consistently recommends three competitors in a city but never you, that city has an established AI search presence for your category. Buyers are asking questions. AI is answering them. You're just not in the answer.
This is the strongest expansion signal. It means the market exists and is already being served -- the question is whether you can get into the conversation before you invest in physical presence.
Low visibility for everyone = emerging or underserved market
If AI search barely mentions anyone in a city for your category, that could mean the market is early, the content ecosystem is thin, or the category isn't well-established there. This is a more speculative signal -- potentially a first-mover opportunity, but worth cross-referencing with other data before acting.
You appear but competitors dominate = content gap, not market gap
If you show up occasionally but competitors appear far more often, you have a content problem, not a market problem. This city might already be worth targeting -- you just need to build the content that earns more citations.
Cross-referencing AI data with traditional location signals
AI visibility data is powerful, but it works best when combined with signals you're probably already tracking. Here's how to layer them:
Search volume by city
Use Google Keyword Planner or a tool like Semrush to check search volume for your core keywords in each city you're evaluating. High search volume + low AI visibility = a city where demand exists but you're not capturing it in the AI channel.
Competitor density
Cities where you have strong AI visibility but few physical competitors may be easier to enter. Cities where AI search is dominated by one or two entrenched players will require more investment to break through.
Demographic and economic fit
AI visibility tells you where the conversation is happening, not whether your specific product or service fits the market. A city might have high AI search activity for your category but skew toward a customer profile that doesn't match your offering. Layer in demographic data to validate.
Existing customer distribution
If you already have customers in a city (even a small number), that's a signal the market fits. Cross-reference your CRM data with your AI visibility map. Cities where you have customers but poor AI visibility are low-hanging fruit -- you're already winning deals there, you're just not getting credit in AI search.
Turning visibility gaps into expansion-ready content
Identifying the gap is step one. Closing it is step two -- and this is where most businesses stall.
The reason you're invisible in AI search for a given city usually comes down to one thing: you don't have content that directly addresses location-specific queries. AI models cite sources. If no page on your site says "we serve Denver" or answers "what's the best [category] option in Denver," there's nothing to cite.
What city-specific content actually looks like
This isn't about stuffing city names into existing pages. It's about creating genuinely useful, location-relevant content:
- Service pages that address the specific context of each city (local regulations, common use cases, regional competitors you compare favorably to)
- Blog posts or guides that answer the questions AI models are pulling from for that city
- Case studies or testimonials from customers in that city
- FAQ content that mirrors the exact prompts you're tracking
The goal is to give AI models something worth citing when a user in Denver asks about your category.
Using AI writing tools to scale city content
Writing location-specific content for 20 cities manually is a real bottleneck. Promptwatch's built-in AI writing agent generates content grounded in actual citation data -- it knows which prompts are driving visibility in each city and writes to answer them. That's meaningfully different from generic AI content tools that don't have visibility data baked in.
For teams that want to use standalone content tools alongside their visibility tracking:
Building a city expansion scoring model
Once you have AI visibility data, search volume, and demographic fit, you can build a simple scoring model to prioritize cities. Here's a framework:
| Signal | Weight | How to measure |
|---|---|---|
| AI visibility gap (competitors cited, you're not) | 30% | Promptwatch city-level tracking |
| Search volume for core keywords | 25% | Google Keyword Planner / Semrush |
| Existing customer presence | 20% | CRM data |
| Competitor density | 15% | Manual research + AI response analysis |
| Demographic/economic fit | 10% | Census data, market research |
Score each city from 1-5 on each signal, multiply by the weight, and sum the scores. Cities that score highest are where you should focus your content investment first -- and where you should seriously consider physical expansion if the model supports it.
This isn't a perfect science, but it gives you a defensible, data-driven rationale for expansion decisions rather than relying on anecdote or executive intuition.
Tracking progress: closing the loop from content to visibility to revenue
Publishing city-specific content is only useful if you can see whether it's working. This is where the tracking loop matters.
After publishing content targeting a city, monitor your AI visibility scores for that city over the following 4-8 weeks. You should see your citation rate increase as AI models discover and index your new content. Promptwatch's page-level tracking shows exactly which pages are being cited, by which models, and how often -- so you can see the direct connection between what you published and where you're appearing.
Beyond visibility, connect AI traffic to actual revenue. Promptwatch supports this through a code snippet, Google Search Console integration, or server log analysis -- so you can see whether the cities where you're gaining AI visibility are also driving real traffic and conversions.
For multi-location businesses already running paid campaigns in ChatGPT, the organic visibility data from AI search tracking is a useful complement. As AdVenture Media Group noted in their April 2026 analysis of ChatGPT Ads, multi-location operators need to think in terms of regional architecture -- and organic AI visibility data tells you which regions are already warm before you spend on paid.
Practical starting point: a 5-step process
If you want to start using city-level AI data for expansion planning today, here's a concrete sequence:
- Pick 15-20 cities you're considering for expansion or where you want to grow.
- Run location-specific prompts manually in ChatGPT and Perplexity for each city. Note who appears and who doesn't.
- Set up systematic tracking in a tool like Promptwatch to monitor those cities on an ongoing basis and get competitor comparison data.
- Cross-reference your AI visibility gaps with search volume and CRM data to identify your highest-priority cities.
- Publish city-specific content targeting the prompts where competitors are visible but you're not. Track visibility changes over 4-8 weeks.
The cities where you close the AI visibility gap fastest are your best candidates for physical expansion -- because you'll already have brand awareness and inbound interest by the time you open the door.
A note on what this data can't tell you
City-level AI visibility is a demand signal, not a complete market analysis. It tells you where conversations are happening and where you're missing from them. It doesn't tell you about local regulatory environments, real estate costs, labor market conditions, or the operational complexity of entering a new market.
Use it as one input in a broader expansion framework, not as the only input. The businesses that will use this well are the ones that treat AI visibility data the same way they treat search volume data: as a strong signal worth acting on, but not a substitute for judgment.
What makes it genuinely valuable is that it's forward-looking. By the time traditional market research confirms that Denver is a good market for your category, your competitors have already been showing up in ChatGPT responses there for six months. AI visibility data lets you see that earlier -- and act on it before the window closes.