ChatGPT for Local Recommendations: How to Get Recommended by AI

Alongside traditional search engines, consumers are increasingly asking AI chatbots for local recommendations. If you run a shop, a practice, or a restaurant in the DACH region, you need to understand how ChatGPT, Gemini, and Perplexity select local providers. The logic behind it differs markedly from traditional Google ranking, and the visibility gap is huge today.

Why ChatGPT recommendations are becoming business-critical

The use of AI tools for local recommendations has exploded within a year. According to the BrightLocal Local Consumer Review Survey 2026 (US), the share of consumers who use ChatGPT and other generative AI tools for local recommendations rose from 6% to 45%, making AI the third most important source of business recommendations (behind Google and Facebook). Among AI tools, ChatGPT is clearly ahead with 31%, followed by Google's AI Mode with 23%.

The sheer reach amplifies the effect. ChatGPT reached 900 million weekly active users worldwide. At the same time, AI is fundamentally changing search behavior: according to Bain & Company, around 80% of searchers rely on AI-generated results for at least 40% of their searches, about 60% of searches end without a click to a website, and organic traffic drops by 15 to 25%. If you only optimize for the traditional click, you lose reach that increasingly plays out inside the AI answer itself.

The foundation is in place in Austria too. At the end of 2025, 8.69 million people used the internet, an online penetration of 95.3%. AI adoption is particularly high among younger audiences: according to Statistik Austria, 57% of 16- to 24-year-olds and 76% of pupils and students in Austria used generative AI tools in the three months before the survey. This generation is growing into your customer base and brings AI-assisted research along as standard behavior.

Trust is nuanced but relevant: according to the same BrightLocal survey of US consumers, 63% of active AI users trust recommendations from AI tools, while 88% check the sources, for example by verifying the legitimacy of a review (51%) or the source itself (37%). AI does not replace verification, it moves it. That makes a consistent, verifiable presence all the more important.

How ChatGPT technically generates local recommendations

ChatGPT generates local recommendations from two sources. First, from its training knowledge, which comes from publicly accessible web content and reflects the world at the time of training. Second, from web search (ChatGPT search, formerly SearchGPT), in which the model retrieves current web results, drawing on Bing search data among other sources. For local queries, ChatGPT search is decisive because it takes recency, opening hours, and new reviews into account.

The signals it evaluates can be inferred from how it works:

• Structured geodata: Address, coordinates, neighborhood, and nearby landmarks help the model assign a business unambiguously to a location.
• Schema markup: Machine-readable markup (LocalBusiness, Restaurant, FAQPage, Review) makes facts unambiguous and therefore easier to cite.
• Consistent mentions: When name, address, and phone number (NAP) appear identically across many sources, the model's confidence increases.
• Review signals: The number, recency, and tone of reviews supply the material for AI-generated summaries.
• Third-party sources: Directories, industry portals, and editorial mentions serve as training and citation sources.

The result is highly selective today. An analysis by the vendor Local Falcon found that 83% of restaurants are completely invisible on ChatGPT, compared with only 14% on Google (basis: 189,905 ChatGPT search results for restaurant queries from businesses that measure their visibility with Local Falcon, February 2026). More broadly across all industries, the SOCi 2026 Local Visibility Index shows that ChatGPT recommends only 1.2% of multi-location brand locations, while 35.9% appear in the Google Local Pack (analysis of over 350,000 locations across 2,751 brands). This makes the AI recommendation slot around 30 times scarcer than a spot in the Local Pack. If you build the signals now, you have a better chance of being among the few businesses that get named.

Strategy: Building the foundation for AI visibility

Maintain a complete Google Business Profile

Even though ChatGPT draws on Bing data, among other sources, for web search, a complete, consistent business profile remains the foundation of all local visibility. Choose the correct primary category plus relevant additional categories, maintain all attributes, opening hours, and photos, and keep your NAP data identical down to the character. This clean data foundation is the anchor AI models rely on when they match a business to a place.

Reputation and reviews as a ranking factor

AI systems summarize reviews and use them as a trust signal. What matters is the number, recency, and star rating, plus a broad spread across multiple platforms. Since many AI users check the sources, reviews should be genuine, current, and present on relevant industry and review portals beyond Google. Responses to reviews create additional machine-readable context about the business.

Schema.org and machine-readable content

Structured data as JSON-LD is the most direct way to make content citable. The most relevant types are LocalBusiness (basic data, geo, opening hours), Restaurant (cuisine, price range, menu), FAQPage (questions and answers in plain text), and Review or AggregateRating. Machine-readable facts reduce the ambiguity a language model would otherwise have to interpret and increase the likelihood of a correct recommendation.

Presence on third-party sources and local directories

AI models learn from the open web. A presence on established third-party sources provides both training and citation material. In Austria, these include WKO Firmen A-Z, Herold, and FirmenABC; in Germany, Gelbe Seiten, Das Örtliche, and 11880; in Switzerland, local.ch and search.ch. Add industry-specific portals and, depending on the industry, TripAdvisor or review platforms. NAP consistency across all sources is essential, because contradictory information weakens the model's confidence.

Content strategy for AEO and GEO

Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO) aim to get your content cited by AI. Structure content in a question-and-answer format, answer questions directly in the first sentence, and name local entities specifically: district, neighborhood, nearby landmarks, catchment area. Location context ("in Vienna's 7th district, near Mariahilfer Strasse") makes content a fit for local queries. Fact-rich, clearly structured texts are easier to extract than promotional prose.

Best practices: Step-by-step checklist for SMBs

1. Complete your profile: Fill in Google Business Profile and Bing Places with correct categories, attributes, opening hours, and photos.
2. Standardize NAP: Align name, address, and phone number down to the character across your website and all directories.
3. Implement schema: Add LocalBusiness, Restaurant where applicable, FAQPage, and Review as JSON-LD on your website.
4. Claim directory listings: Create and maintain entries in the major directories of your country and in industry-relevant portals, such as Herold in Austria, Das Örtliche in Germany, or local.ch in Switzerland.
5. Build reviews: Actively ask for reviews, respond to all of them, and ensure recency and platform breadth.
6. Create AEO content: FAQ pages and location pages with clear answers, local entities, and neighborhood context.
7. Encourage third-party mentions: Initiate local PR, partnerships, and editorial mentions.
8. Set up monitoring: Regularly check whether and how ChatGPT, Gemini, and Perplexity mention your business.

Traditional local SEO vs. AI recommendations

Good Google rankings do not automatically mean AI visibility. The overlap between the two worlds is limited: a business at the top of the Local Pack can be completely missing from ChatGPT, and vice versa. The visibility data cited at the beginning illustrate this clearly. While only 14% of restaurants are invisible on Google, the figure on ChatGPT is 83%. So if you only do traditional local SEO, you do not necessarily cover AI recommendations as well.

The difference lies in how sources are used. Google evaluates a specific URL for a specific query and rewards proximity. AI models synthesize an answer from many sources and reward consistency, citability, and broad mentions that agree with each other. That is why schema markup, NAP consistency, and third-party presence often matter more for AI than the ranking position alone.

Local SEO in Austria

In Austria, regional factors come into play. Use regional keywords with district and neighborhood references as well as Austrian language variants (such as "Jänner" instead of "Januar" for January). Rely on Austrian platforms such as WKO Firmen A-Z and Herold, which serve as local authority sources. Local backlinks and mentions from regional media, associations, and industry partners strengthen both traditional and AI visibility because they act as verifiable third-party sources. State your location context explicitly so models capture your catchment area correctly.

Metrics and monitoring of AI visibility

AI visibility can be checked, even though there is no central console like Search Console. The following metrics and methods are practical:

• Direct prompt testing: Enter typical local queries in ChatGPT, Gemini, and Perplexity ("best Italian restaurant in Vienna Neubau") and record whether and in what position the business is named.
• Mention and source analysis: In Perplexity and ChatGPT search, check the cited sources to understand which third-party sites feed the recommendation.
• Share of voice: The share of relevant prompts in which the business appears, compared with competitors.
• Review KPIs: Number, average, and recency of reviews across all platforms, since these flow directly into AI summaries.
• Consistency check: Spot checks of NAP data and schema validity, because inconsistencies prevent recommendations.

Since many AI users check the sources (88% verify reviews or the source), being named is not enough. The mention also has to hold up to verification. A consistent, factually accurate presence therefore pays off twice.

Self-test: What ChatGPT says about you today

Before you work on profiles and content, you need a baseline. Asking once is not enough. Answers vary with the wording, and an account with saved memories shows you a personalized result instead of what a prospect who doesn't know you sees.

1. Open a temporary chat: No history, no saved memories, no personalized profile.
2. Run three prompt patterns: The plain category plus location, the specific problem plus location, and the direct request for a recommendation.
3. Vary each pattern three ways: With the district, with the federal state, and without a location but with the phrase "near me".
4. Log the answers: Which providers are named, in what order, and which sources ChatGPT links to.
5. Repeat after four weeks: Identical prompts, same conditions, then compare the lists.

Two things matter more than whether you show up at all. First, the cited sources: these are the pages you can actually influence, and your own website is rarely among them. Second, stability. A single mention is chance; the same mention in three out of three runs is a pattern you can build on.

If you need this regularly for ChatGPT, Gemini, Perplexity, and Google AI Overviews, our AI visibility check takes care of it.

When ChatGPT says something wrong about you

There is no correction button. A chatbot has no editorial desk where you can submit a correction. The only thing you can correct is the set of sources the answer is built from.

• Master data first: Check Google Business Profile and Bing Places. Because ChatGPT search draws on Bing data, among other sources, Bing Places carries more weight here than in traditional local SEO.
• Clean up old listings: Outdated addresses and phone numbers in directories are the most common cause of incorrect information. The citation audit shows how to find them.
• Provide one unambiguous fact page: Opening hours, services, catchment area, and price range in clear text, plus the same details as JSON-LD so machines can read them.
• Contact third-party sources: For industry directories and press articles with incorrect information, asking the operator for a correction is the fastest lever.
• Trigger reindexing: Submit changed pages through Bing Webmaster Tools and IndexNow instead of waiting for the next crawl.

How long it takes for a correction to show up in the answers cannot be predicted reliably. It depends on where the answer comes from. If it comes from web search, it catches up as soon as the source is reindexed. If the error sits in the model's training knowledge, it only disappears with a new model version.

Further reading

ChatGPT recommendations are a distinct, fast-growing channel that rewards different signals than traditional local SEO. The high invisibility rate in AI is both a risk and an opportunity: if you build complete profiles, consistent NAP data, clean schema markup, active review management, and a presence on relevant third-party sources now, you improve your chances of landing one of the few recommendation slots. Sensible next steps are a NAP and citation audit, implementing the most important schema types, and regularly monitoring your own mentions in ChatGPT, Gemini, and Perplexity.