ChatGPT for Local Recommendations: How to Get Recommended by AI

Consumers are increasingly asking AI chatbots rather than traditional search engines for local recommendations. Anyone operating a business, practice, or restaurant in the DACH region must understand how ChatGPT, Gemini, and Perplexity select local providers. The logic behind it differs significantly from traditional Google ranking, and the visibility gap today is enormous.

Why ChatGPT recommendations are becoming business-critical

The use of AI tools for local recommendations has exploded within a year. According to an international US study,the share of consumers using ChatGPT and other generative AI tools for local recommendations increased from 6% to 45%, making it the third most important source for business recommendations(behind Google and Facebook). Among AI tools,ChatGPT leads clearly 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 clicking on a website, and organic traffic is declining by 15 to 25%. Those who only optimize for the traditional click are therefore losing reach that increasingly takes place within the AI answer itself.

The foundation also exists in Austria.At the end of 2025, 8.69 million people used the internet, with an online penetration of 95.3%. AI adoption is particularly high among younger target groups: 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 as a customer base and brings AI-supported research as standard behavior.

Trust is differentiated but relevant: According to the international BrightLocal study,63% of active AI users trust recommendations from AI tools, while 88% verify sources, for example by checking the legitimacy of a review (51%) or the source itself (37%). AI does not replace verification, but shifts it. This makes consistent, verifiable presence all the more important.

How ChatGPT technically generates local recommendations

ChatGPT generates local recommendations from two sources. First, from training knowledge derived from publicly accessible web content that represents the world at the time of training. Second, from live search (SearchGPT), where the model retrieves current web results based in part on Bing search data. For local queries, live search is crucial because it includes timeliness, opening hours, and new reviews.

Which signals are evaluated can be derived from how it works:

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

The result today is very selective. An international US analysis found that83% of restaurants are completely invisible on ChatGPT, compared to only 14% on Google(basis: 189,905 ChatGPT results versus 16.4 million Google results). More broadly across all industries, the SOCi 2026 Local Visibility Index shows thatChatGPT currently recommends only 1.2% of all local business locations(analysis of over 350,000 locations across 2,751 brands). Competition for the AI recommendation slot is therefore far smaller than on Google, making early optimization worthwhile.

Strategy: Building the foundation for AI visibility

Maintain Google Business Profile completely

Even though ChatGPT primarily uses Bing data, a complete, consistent business profile remains the foundation of any local visibility. Choose the correct primary category plus relevant additional categories, maintain all attributes, opening hours, and photos, and keep NAP data character-exact consistent. This clean data foundation is the anchor that AI models use for assignment.

Reputation and reviews as a ranking factor

AI systems summarize reviews and use them as a trust signal. Number, timeliness, and star rating are crucial, as well as broad distribution across multiple platforms. Since many AI users verify sources, reviews should be genuine, current, and present beyond Google on relevant industry and review portals. Responding to reviews creates 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. Particularly relevant are LocalBusiness (basic data, geo, opening hours), Restaurant (cuisine, price level, menu), FAQPage (questions and answers in plain text), and Review or AggregateRating. Machine-readable facts reduce the ambiguity that 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. Presence on established third-party sources provides both training and citation material. In the DACH region, these include WKO Firmen A-Z, Herold, FirmenABC, as well as industry-specific portals and, depending on the industry, TripAdvisor or review platforms. NAP consistency across all sources is important, as contradictory information weakens the model's confidence.

Content strategy for AEO and GEO

Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO) aim to be cited by AI. Structure content in question-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 connectable for local queries. Fact-rich, clearly structured texts are easier to extract than promotional flowing text.

Best practices: Step-by-step checklist for SMEs

1. Complete profile:Fill Google Business Profile and Bing Places with correct categories, attributes, opening hours, and photos.
2. Unify NAP:Align name, address, and phone character-exact across website and all directories.
3. Implement schema:Add LocalBusiness, if applicable Restaurant, FAQPage, and Review as JSON-LD on the website.
4. Occupy directories:Create and maintain entries in WKO Firmen A-Z, Herold, FirmenABC, and industry-relevant portals.
5. Build reviews:Actively ask for reviews, respond to all, ensure timeliness and platform breadth.
6. Create AEO content:FAQ pages and location pages with clear answers, local entities, and neighborhood context.
7. Promote third-party mentions:Initiate local PR, partnerships, and editorial mentions.
8. Set up monitoring:Regularly check whether and how ChatGPT, Gemini, and Perplexity mention the business.

Traditional local SEO vs. AI recommendations

Good Google rankings do not automatically mean AI visibility. The overlap between both worlds is limited: A business that ranks at the top in the Local Pack can be completely absent in ChatGPT, and vice versa. The visibility data mentioned at the beginning clearly illustrate this. While on Google only14% of restaurants are invisible, on ChatGPT it is 83%. Those who only do traditional local SEO therefore do not necessarily cover AI recommendation.

The difference lies in source logic. 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, consistent mention. That's why schema markup, NAP consistency, and third-party presence are often more important for AI than pure ranking position.

Local SEO for Austria and the DACH region

In the DACH region, regional factors are added. Use regional keywords including district and neighborhood references as well as Austrian language variants (e.g., "Jänner" instead of "Januar"). 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 function as verifiable third-party sources. Explicitly state location context so models correctly capture the catchment area.

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:Submit typical local queries in ChatGPT, Gemini, and Perplexity ("best Italian restaurant in Vienna Neubau") and note whether and at what position the business is mentioned.
• Mention and source analysis:Check the cited sources in Perplexity and SearchGPT to understand which third-party sites feed the recommendation.
• Share of voice:Share of relevant prompts in which the business appears, compared to competitors.
• Review KPIs:Number, average, and timeliness of reviews across all platforms, as these flow directly into AI summaries.
• Consistency check:Spot checks of NAP data and schema validity, because inconsistencies prevent recommendation.

Since many AI users verify sources (88% verify reviews or source), the goal is not only to be mentioned, but to pass verification. A consistent, factually accurate presence therefore pays off twice.

Further reading

ChatGPT recommendations are an independent, rapidly growing channel that rewards different signals than traditional local SEO. The high invisibility rate in AI is both risk and opportunity: Those who now build complete profiles, consistent NAP data, clean schema markup, active review management, and presence on relevant third-party sources secure a recommendation slot that is still empty for many competitors. Sensible next steps are a NAP and citation audit, implementation of the most important schema types, and regular monitoring of one's own mentions in ChatGPT, Gemini, and Perplexity.