--- title: "How to Compare AI Visibility Across Cities: 5 Steps" url: "https://jayyseo.com/how-to-compare-ai-visibility-across-cities/" date: "2026-10-21T11:56:54+00:00" updated: "2026-10-22T06:26:25+00:00" author: "Jay" description: "If your business serves more than one city, your AI visibility can be very different in each place. Someone in New York might see your brand when they ask ChatGPT for a recommendation. Someone in Los Angeles might see three…" category: "AEO" --- # How to Compare AI Visibility Across Cities: 5 Steps If your business serves more than one city, your AI visibility can be very different in each place. Someone in New York might see your brand when they ask ChatGPT for a recommendation. Someone in Los Angeles might see three competitors before your name ever comes up. That is why it helps to compare AI visibility city by city. You get a clearer picture of where you are doing well, where competitors are getting attention, and which locations need more work. Here is a simple five-step process you can follow, even if you are just getting started with AI search tracking. ## Why It’s Important to Compare AI Visibility Across Cities ### Find Local Gaps That Overall Reports Can Hide Your business may appear regularly in AI recommendations for one city but rarely in another. An overall visibility score can hide those differences. Comparing cities gives you a clearer view of where your brand appears and which locations need attention. RadarKit’s Local Radar tracks appearance rate and recommendation position to help uncover these gaps. ### Discover Which Competitors Win in Each Market The businesses competing with you in AI answers may differ from city to city. Local comparisons reveal who appears instead of your brand and whether those competitors consistently secure prominent positions. Local Radar captures recommended businesses alongside their positions, ratings, review counts, and appearances, giving you specific competitors to investigate. ### Separate Occasional Mentions From Consistent Recommendations Being mentioned once does not mean your business has strong local visibility. Comparing appearance frequency with recommendation position helps distinguish occasional inclusion from a consistent presence near the top of answers. RadarKit tracks both metrics, making the comparison more useful than a mention count alone. ### Build a More Focused Local Content Strategy City-level findings help you decide which local pages and service topics deserve closer attention. If your business appears for general searches but misses specific service prompts in one city, you have a focused area to investigate. Use these gaps to guide content research rather than making identical changes across every location. ### Measure Progress Without Relying on a Single Answer AI recommendations vary by time, location, and other factors, so one answer is only a snapshot. Repeating the same prompts across cities helps you assess whether visibility changes persist over time. Keep platforms, prompt wording, and reporting periods consistent to make the comparison meaningful. Alert me when my business rank drops in a key city - Automation - How does Local Radar handle location tracking - Give me a prompt template for city level tracking - How do competitors affect local AI visibility - What metrics matter most for local AI rankings ## How to Compare AI Visibility Across Cities: 5 Steps Comparing AI visibility across cities means checking whether AI recommends your business in each market, where it places you, and which competitors appear instead. A useful comparison keeps the prompts, platforms, and reporting period consistent so you can identify meaningful differences. RadarKit’s Local Radar supports this process by running prompts from residential IPs in selected cities and tracking business appearances, positions, ratings, and local competitors. Its current pricing reserves state- and city-level tracking for Enterprise, so confirm access to your target cities before starting. Here are five steps to build a useful city-by-city comparison. ### 1. Build a Consistent Set of Local Prompts Start with questions that reflect how customers look for your services. Cover several types of buying intent rather than relying on one broad “best business” query. - For a dental business, your prompt groups might include: - General discovery: “Best dental clinics in [city].” - Specific treatment: “Where can I get dental implants in [city]?” - Urgent needs: “Emergency dentist in [city].” - Decision criteria: “Which dentists in [city] are good for nervous patients?” Use the same prompt structure for every city. Change the location, but keep the service, customer need, and qualifying language consistent. For example, comparing “best dental clinic in Delhi” with “affordable emergency dentist in Mumbai” would mix location differences with different customer priorities. Those results would not provide a clean comparison. Separate city-named prompts from location-dependent prompts such as “best dentist near me.” The first tells the AI which city to consider. The second helps you examine recommendations made from the selected tracking location. RadarKit records answers to your chosen prompts through real AI chat interfaces, allowing you to examine your brand and competitors against a consistent question set. ### 2. Run the Prompts From Each Target City Next, use Local Radar’s city-level residential-IP tracking to collect answers from the markets you want to compare. This lets you examine recommendations from a selected city rather than only adding its name to a prompt. Keep the following settings aligned across locations: - The same prompt groups. - The same AI platforms. - The same reporting dates. - The same collection frequency. - The same business or branch being evaluated. RadarKit lists ChatGPT, Google AI Mode, Google AI Overviews, Microsoft Copilot, Perplexity, and Gemini among its monitored experiences. Review each platform separately before combining results into a broader summary. This prevents one platform’s stronger performance from hiding weaker visibility elsewhere. A useful reporting structure is city first, then platform, then prompt category. Also, check the actual collection schedule. pricing lists daily or weekly refresh options for Enterprise, which includes city-level tracking. Compare cities using the same schedule rather than treating every dataset as equally fresh. ### 3. Compare Appearance Rates, Positions, and Competitors Once you have collected responses, focus on three questions: how often you appear, how prominently you appear, and who appears instead. ### Appearance Rate Appearance rate measures the share of collected answers that include your business. For a straightforward manual calculation, divide the answers mentioning your business by the total answers reviewed, then multiply by 100. Use the same inclusion rules in every city. Record the underlying response counts alongside percentages. A result based on a handful of answers should not carry the same weight as one supported by a larger, repeated sample. Local Radar displays business appearance counts and percentages, making this a useful starting point for your comparison. ### Position When Present Next, examine where your business appears in the recommendations. Local Radar tracks rank, and RadarKit’s broader dashboard includes average-position reporting. Read position alongside appearance rate. A business that ranks first in one answer but is absent from most others has a different visibility pattern from one that consistently appears in second or third place. Avoid treating an absence as an ordinary low ranking. Keep “not mentioned” separate so the comparison remains understandable. ### Competing Businesses Review the businesses recommended alongside or instead of yours. RadarKit supports local competitor tracking and comparisons of competitor mentions and share of voice across monitored prompts. ### Look for repeated patterns: A different competitor dominates each city. - One competitor appears across several markets. - Your business appears for general searches but misses treatment-specific prompts. - Your strongest city performs well on one platform but poorly on another. - These patterns make the comparison more useful than a single overall score. ## 4. Inspect the Answers and Sources Behind Each Gap After identifying weaker cities, read the underlying answers. A visibility difference tells you where to investigate, but not automatically what caused it. RadarKit combines local recommendation tracking with citation analysis. Its Local Radar examples show recommended businesses, ratings, review counts, and associated sources. ### For each underperforming city, examine: - Which websites support the recommendations. - Whether competitors’ location pages appear. - Which directories or review platforms recur. - How the AI describes each recommended business. - Whether the answer accurately represents your services and location. For example, if competitors repeatedly appear alongside city-specific directories, investigate those listings. If their service pages receive citations while yours do not, compare how clearly the pages address the prompt’s treatment, location, and customer need. Treat these observations as evidence, not proof of causation. A competitor’s higher rating or directory presence may be worth investigating, but the answer alone does not establish why the AI selected it RadarKit also captures query fanout, which can provide additional search context for content research. Use it where available to understand which related questions or searches accompany the original prompt ## 5. Make City-Specific Improvements and Measure Again Turn the findings into a focused action list for each city. Avoid making the same changes everywhere when the gaps differ. ### Possible actions include: - Clarifying which services a location actually provides. - Improving a city page that does not answer the tracked questions. - Correcting inaccurate business information. - Adding useful treatment, availability, or accessibility details. - Investigating relevant third-party sites that repeatedly appear in citations. - Creating content for service questions where your business is consistently absent. RadarKit’s Content, Plan, Outreach, and Reddit agents work from visibility gaps and cited-source data. Its Content Agent uses search-result and AI citation information to help address entities, content gaps, and citation opportunities. Use those workflows to support the work, while reviewing factual accuracy and local relevance before publishing or sending anything. After making changes, rerun the same prompt groups from the same cities. Compare equivalent reporting periods and track: - Whether appearances become more frequent. - Whether positions improve when the business appears. - Whether additional service prompts start mentioning the business. - Whether descriptions become more accurate. - Whether your pages or relevant third-party mentions appear among the sources. - Keep a dated change log so you can connect observed movements with the work completed, without assuming every movement resulted from your edits. - RadarKit includes historical data, branded PDF reporting, API access, and MCP connectivity, which can support recurring analysis and reporting. - The goal is a repeatable comparison: the same customer questions, measured under consistent conditions, followed by improvements tailored to each city’s actual visibility gaps. ### Best Tool to Compare AI Visibility Across Cities: Radarkit ![Radarkit Best Tool to Compare AI Visibility Across Cities](https://jayyseo.com/wp-content/uploads/2026/10/Radarkit-Best-Tool-to-Compare-AI-Visibility-Across-Cities.jpg) _Radarkit Best Tool to Compare AI Visibility Across Cities_ If you want a tool built for this kind of work, Radarkit is the best option to start with. It works like a real customer by running prompts from city-level residential IPs and recording the AI answers people actually see. Radarkit tracks major AI platforms, including: - ChatGPT - Google AI Overviews - Google AI Mode - Gemini - Perplexity - Microsoft Copilot Its Local Radar feature is especially helpful for local businesses. It shows which businesses AI recommends in a selected city, where your brand ranks, and which competitors appear instead. It also tracks: - Brand visibility and average position - Sentiment in AI answers - Competitor mentions and share of voice - Citations and sources used by AI - Local business rankings and ratings - Product visibility in AI shopping answers - Ads shown inside AI responses What makes Radarkit useful is that it does not stop at tracking. Its AI agents help you turn visibility gaps into next steps, such as content ideas, outreach opportunities, and ways to improve citations. ### Radarkit Pricing Radarkit has four plans. Every paid plan includes a 7-day free trial, and you can cancel anytime. | Plan | Price | Best for | Projects | Prompts tracked | Locations | | --- | --- | --- | --- | --- | --- | | Lite | $29 per month | Individuals and small businesses | 2 | 15 | 50+ | | Growth | $79 per month | Growing brands and marketers | 5 | 50 | 50+ | | Pro | $139 per month | Small teams and agencies | 10 | 100 | 50+ | | Enterprise | Custom annual pricing | Large brands and agencies | Unlimited | Custom | 70+ | All plans track ChatGPT, Google AI Mode, Google AI Overviews, Microsoft Copilot, Perplexity, and Gemini. They also include competitor monitoring, local business tracking, Google Search Console integration, API access, client portals, and branded PDF reports. ### Which Plan Makes Sense? - Lite at $29 per month is good if you want to test AI visibility for one or two brands. - Growth at $79 per month works well if you manage several locations or need more prompts. - Pro at $139 per month is a better fit for small agencies or teams handling multiple clients. - Enterprise is best for larger brands that need unlimited projects, custom prompts, state and city-level tracking, SSO, unlimited seats, and dedicated support. For comparing AI visibility across cities, Growth or Pro is usually the sweet spot. Both include more prompts, more projects, and daily prompt refreshes. ## FAQs ### What is AI visibility? AI visibility is how often your brand appears in answers created by AI search tools. It includes brand mentions, citations, position in the answer, sentiment, and whether the information is accurate. ### Why does AI visibility change by city? AI visibility changes by city because AI tools use local data, reviews, business listings, nearby competitors, and location-specific sources when creating answers. ### How often should I compare AI visibility across cities? Once a month is enough for most businesses. If you are launching a new location, running a campaign, or dealing with a major change, weekly tracking can give faster feedback. ### How many prompts should I track per city? Start with 5 to 10 prompts per city. Use a mix of discovery, comparison, local-intent, trust, and brand-related questions. ### Is Radarkit good for local AI visibility? Yes. Radarkit’s Local Radar tracks AI recommendations by city using residential IPs. It shows your local rank, ratings, competitors, and visibility rate. ### How much does Radarkit cost? Radarkit starts at $29 per month for the Lite plan. Growth costs $79 per month, Pro costs $139 per month, and Enterprise uses custom annual pricing. ### Final Thoughts Comparing AI visibility across cities does not need to be complicated. Pick your priority locations, use the same prompts everywhere, test across several AI platforms, measure mentions and citations, then improve the local signals that influence AI answers. If you want a dedicated platform for this, Radarkit is the best place to start. It combines real-browser AI tracking, city-level local visibility, competitor analysis, citation insights, and AI agents in one place. With plans starting at $29 per month, it is a practical option for businesses that want to understand and improve how they show up in AI search. --- Source: https://jayyseo.com/how-to-compare-ai-visibility-across-cities/