GEO for Local Businesses: Generative Engine Optimization for Local Visibility
GEO for local businesses is the targeted optimization of business presence for citation by AI search systems like Google AI Overviews, ChatGPT and Perplexity, complementary to classic local SEO optimization for the Google Local Pack and Maps.
Key Takeaways
- 45% of consumers use AI for local recommendations, compared to 6% the previous year (US survey, BrightLocal 2026). After Google and Facebook, AI is now the third most used tool for this.
- AI currently recommends only a fraction of locations of US multi-location brands (ChatGPT 1.2%, Perplexity 7.4%, Gemini 11%; SOCi 2026). That is a major opportunity for early adopters.
- Gemini draws on Google Maps and Business Profile data for local queries and reproduced profile data with 100% accuracy in SOCi's 2026 analysis; a complete Google Business Profile is the foundation.
- NAP consistency across Google, Herold, WKO Firmen A-Z, Bing Places and Apple Maps is the central technical GEO lever.
- LocalBusiness schema is present on only around 4% of mobile pages; structured data is an underutilized lever for AI readability.
- External mentions account for 27% of local ChatGPT sources; off-site presence and reviews (avg. 4.3 stars for ChatGPT recommendations in the US) are critical signals.
- GEO complements Local SEO, does not replace it; first measurable effects typically appear after 8 to 12 weeks.
Why GEO is now relevant for local businesses
Local search is fundamentally changing. Consumers no longer just ask Google for the "best hairdresser nearby", but ChatGPT, Gemini or Perplexity. According to a US survey, the proportion of consumers who use AI for local recommendations has risen from 6% in 2025 to 45% (BrightLocal, Local Consumer Review Survey 2026). AI is thus developing into an established channel for local recommendations alongside Google and Facebook.
AI adoption is also growing in Germany, where 67% of people aged 16 and over use generative AI such as ChatGPT, Microsoft Copilot or Gemini (Bitkom, 2025), compared to 40% the previous year. For Austria, there are two stable framework conditions that make GEO particularly effective: There are 8.69 million internet users with 95.3% internet penetration (DataReportal, January 2025), and 89% of the population aged 15 and over own a smartphone (Statista, 2023), the most used device for internet usage.
At the same time, Google is clearly dominant in Austria with an 81.87% search engine market share (StatCounter, 2026), ahead of Bing (9.01%) and DuckDuckGo (2.75%). This means: Traditional Local SEO remains the foundation, but the AI layer increasingly determines who gets recommended.
The crucial point for early adopters: AI is still very selective with local recommendations. According to an analysis of around 350,000 locations of US multi-location brands, ChatGPT recommends only 1.2% of these locations, Perplexity 7.4% and Gemini 11%, while brands appear in the traditional Google Local 3-Pack at 35.9% (SOCi Local Visibility Index 2026). AI visibility is therefore currently significantly harder to achieve than good traditional ranking. Those who optimize early have disproportionate opportunities in a thinly populated recommendation space.
What GEO is and how it differs from Local SEO and AEO/LLMO
GEO (Generative Engine Optimization): Optimization to be cited as a source or recommended as a business in the generative answers of AI systems. The goal is mention, not just ranking.
Traditional Local SEO: Optimization for the Google Local Pack, Google Maps and organic local results. The goal is the highest possible position in the traditional results list.
AEO/LLMO: Answer Engine Optimization and Large Language Model Optimization are often used synonymously with GEO. They emphasize optimization for direct answers and for processing by language models. In practice, the disciplines overlap significantly.
The central difference: Local SEO brings position, GEO brings citation. A page can be mentioned in an AI answer even if it is not classically ranked first, because AI systems select sources based on content quality, structure and consistency, not just rank.
How AI selects local businesses: the strategy
AI systems draw on different data sources for local answers. An analysis of ChatGPT sources shows: 58% of local sources are company websites, 27% external mentions and 15% directories (BrightLocal, study "Uncovering ChatGPT Search Sources", 2024). This results in the GEO strategy for local businesses along four levers.
1. Google Business Profile as foundation
Gemini draws on Google Maps data for local queries. This has measurable consequences: In one analysis, business profile information was 100% accurate for Gemini, but only around 68% for ChatGPT and Perplexity (SOCi 2026). A complete, maintained Google Business Profile with correct opening hours, categories, services, photos and description is therefore the basic requirement. Without a clean profile, Gemini simply recommends incorrect or no data at all.
2. NAP consistency across directories
NAP stands for name, address and phone number. AI systems rely on an ecosystem of consistent data. Inconsistent information across different sources weakens trust and thus the likelihood of recommendation. Particularly relevant for Austria are:
- Google Business Profile: Primary source, especially for Gemini and AI Overviews.
- Herold.at: Largest Austrian business directory.
- WKO Firmen A-Z: Official directory of the Austrian Economic Chamber, high authority.
- Bing Places and Apple Maps: Relevant for Copilot and Apple devices respectively.
Identical spelling of name, address and phone number across all directories is the central technical GEO lever.
3. LocalBusiness schema and structured data
Structured data in JSON-LD format makes local content machine-readable for AI. Despite high effectiveness, the field is thinly populated: JSON-LD is overall present on 41% of pages, but LocalBusiness schema only on around 3.97% of mobile pages (HTTP Archive Web Almanac, 2024). This is precisely the opportunity. Important are:
- LocalBusiness schema: With name, address (
PostalAddress),GeoCoordinates,openingHoursSpecificationand phone number. - FAQPage schema: For local questions such as directions, parking or services.
- AggregateRating: Machine-readable review signals.
4. Off-site mentions and local entity authority
Since 27% of ChatGPT sources are external mentions, presence outside one's own website counts. Unstructured citations such as regional blog mentions, industry magazines, local press and forum posts increase the likelihood of being cited by AI. They build local entity authority, i.e. the AI's understanding that a business is a relevant player in a region and industry.
Best practices: Answer-first content and E-E-A-T
Local landing pages and location pages should be written so that AI systems can extract passages directly.
- Direct answer first: Start each section with a clear, standalone answer of 40 to 60 words, not with introductory phrases.
- Support with statistics: Concrete figures with source references increase the likelihood of being cited in AI answers. Research on Generative Engine Optimization shows visibility increases of up to 40% (Aggarwal et al., 2023).
- FAQ sections: Answer local questions in natural language and mark up with FAQPage schema.
- Demonstrate E-E-A-T: Prove experience, expertise, authority and trust through named authors, references and concrete local details instead of generic statements.
- Maintain location pages: One separate, content-wise unique page per location with directions, opening hours and services, not thin duplicates.
Review management
Reviews are a dual signal, for traditional ranking and for AI. According to a US survey, 97% of consumers read reviews of local businesses, and 71% use Google to do so (BrightLocal, Local Consumer Review Survey 2026). In SOCi's analysis of US multi-location brands, locations recommended by ChatGPT averaged 4.3 stars (SOCi Local Visibility Index 2026, via BrightLocal). Active, continuous collection of genuine reviews with responses to reviews directly contributes to AI recommendation probability.
Common mistakes
- Only focusing on Google AI Overviews: According to an Ahrefs analysis of desktop search results (September 2025), AI Overviews appear for only 7.9% of local search queries. The greater lever lies with ChatGPT, Gemini and Perplexity as independent assistants.
- Inconsistent NAP data: Different spellings across directories undermine the trust of AI systems.
- Neglected Google Business Profile: Since Gemini draws on Google Maps data for local queries, profile errors directly impact AI answers.
- No LocalBusiness schema: Without structured data, AI must guess local key data.
- Only optimizing own website: Those who ignore external mentions leave 27% of relevant AI sources unused.
- Blocking the wrong AI crawlers: Blocking OAI-SearchBot, PerplexityBot or Googlebot in robots.txt means the site is no longer shown as a source in ChatGPT search answers, Perplexity or Google Search including AI Overviews, respectively. GPTBot and Google-Extended, by contrast, mainly control use for AI training, and Google-Extended also controls grounding in the Gemini apps. Google-Extended has no effect on visibility in AI Overviews.
- Unrealistic expectations: GEO is not a switch. Entity authority and consistent data take time until AI systems adopt them.
Measuring GEO success
Traditional rankings are not sufficient for GEO. Meaningful metrics are:
- Citations: How often and where is the business mentioned in AI answers? Manually verifiable by asking the same 15 to 20 local search queries monthly in ChatGPT, Gemini and Perplexity.
- Share of voice: Share of own mentions compared to local competitors in AI answers.
- Recommendation frequency: Is the business recommended for generic queries ("best provider for X in city Y")?
- Profile accuracy: Do the data output by AI regarding opening hours, address and services match?
Realistic timeframe: First measurable effects typically appear after 8 to 12 weeks, as AI systems update their database with a delay and require consistent signals across multiple sources.
Practical checklist for Austrian SMEs
- Complete Google Business Profile: Categories, services, opening hours, photos, description, Q&A.
- Standardize NAP: Identical data on Google, Herold, WKO Firmen A-Z, Bing Places and Apple Maps.
- Implement LocalBusiness schema: Including
GeoCoordinates,openingHoursSpecificationandAggregateRating. - Create location and FAQ pages: Answer-first, with local relevance and FAQPage schema.
- Systematically collect reviews: Establish request process, respond to all reviews.
- Build off-site presence: Regional press, business directories, local blogs and forums.
- Allow AI crawlers: Permit OAI-SearchBot, PerplexityBot and Googlebot in robots.txt. Decide deliberately on GPTBot (AI training) and Google-Extended (AI training and grounding in the Gemini apps).
- Measure monthly: Local test questions in ChatGPT, Gemini and Perplexity, log citations and share of voice.
Further reading
GEO for local businesses is a complement, not a replacement for traditional Local SEO. The foundation of a maintained Google Business Profile, consistent NAP data and structured data pays into both disciplines. Those who lay this foundation cleanly, build external mentions and continuously collect reviews position themselves in a still thinly populated AI recommendation space.
Data & Statistics
The share of US consumers using AI for local recommendations has risen from 6% (2025) to 45% (2026). After Google and Facebook, AI is now the third most used tool for this.
BrightLocal - Local Consumer Review Survey 2026 (AI Trust) (2026)AI visibility is 3 to 30 times harder to achieve than classic local ranking; ChatGPT recommends only 1.2% of locations of US multi-location brands, Perplexity 7.4%, Gemini 11% compared to 35.9% visibility in the Google Local 3-Pack.
SOCi 2026 Local Visibility Index, via Search Engine Land (2026)Profile accuracy in AI responses: around 68% for ChatGPT and Perplexity compared to 100% for Gemini, which draws on Google Maps data for local queries.
SOCi 2026 Local Visibility Index, via Search Engine Land (2026)AI Overviews appear for only 7.9% of local search queries (desktop search results, September 2025).
Ahrefs - What Triggers AI Overviews? 86 Factors and 146 Million SERPs Analyzed (2025)67% of people in Germany aged 16 and over use generative AI (previous year: 40%).
Bitkom / Bitkom Research (2025)8.69 million internet users in Austria, 95.3% internet penetration (January 2025).
DataReportal - Digital 2025: Austria (2025)Google has 81.87% search engine market share in Austria (Bing 9.01%, DuckDuckGo 2.75%).
StatCounter Global Stats - Search Engine Market Share Austria (2026)97% of US consumers read reviews of local businesses, 71% use Google to do so; locations of US multi-location brands recommended by ChatGPT average 4.3 stars (SOCi).
BrightLocal - Local SEO Statistics 2026 (2026)LocalBusiness schema is present on approximately 3.97% of mobile pages; JSON-LD overall on 41%.
HTTP Archive - Web Almanac 2024, chapter Structured Data (2024)ChatGPT sources for local answers: 58% business websites, 27% external mentions, 15% directories.
BrightLocal - Local SEO Statistics (source: Uncovering ChatGPT Search Sources, 2024) (2024)89% of the population in Austria aged 15 and over own a smartphone, the most used device for internet access.
Statista - Smartphone usage in Austria (2023)Generative Engine Optimization increases the visibility of content in AI responses by up to 40%
Aggarwal et al., GEO: Generative Engine Optimization (arXiv) (2023)Opens the chat with a prepared prompt.
Frequently asked questions
What is GEO for local businesses?
How does GEO differ from traditional Local SEO?
Is a good Google Business Profile sufficient for AI visibility?
Which directories are important for GEO in Austria?
How long does it take for GEO measures to take effect?
How do you measure GEO success for local businesses?
Why are external mentions important for AI visibility?
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