GEO for Local Businesses: Generative Engine Optimization for Local Visibility

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 an international study, the proportion of consumers who use AI for local recommendations has risen from 6% in 2025 to 45% (US panel, BrightLocal 2026). AI is thus developing into an established channel for local recommendations alongside Google and Facebook.

The DACH region is catching up. In Germany, already 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 in Austria has a 81.87% search engine market share clearly dominant (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 international analysis of around 350,000 locations, ChatGPT recommends only 1.2% of local 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 is anchored in Google Maps. This has measurable consequences: Business profile information was in an international analysis 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, openingHoursSpecification and 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 an international study, 97% of consumers read local reviews, 71% of them via Google, and AI-recommended businesses average 4.3 stars on ChatGPT (BrightLocal 2026). Active, continuous collection of genuine reviews with responses to reviews directly contributes to AI recommendation probability.

Common mistakes

• Only focusing on Google AI Overviews: AI Overviews appear according to international analysis only for around 7% of local search queries (WordStream/Ahrefs, 2026). 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 is based on Google Maps, 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 AI crawlers: Those who block GPTBot, PerplexityBot or Google-Extended in robots.txt cannot be cited by these systems.
• 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

1. Complete Google Business Profile: Categories, services, opening hours, photos, description, Q&A.
2. Standardize NAP: Identical data on Google, Herold, WKO Firmen A-Z, Bing Places and Apple Maps.
3. Implement LocalBusiness schema: Including GeoCoordinates, openingHoursSpecification and AggregateRating.
4. Create location and FAQ pages: Answer-first, with local relevance and FAQPage schema.
5. Systematically collect reviews: Establish request process, respond to all reviews.
6. Build off-site presence: Regional press, business directories, local blogs and forums.
7. Allow AI crawlers: Check robots.txt for GPTBot, PerplexityBot and Google-Extended.
8. 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 in which currently only a fraction of local businesses are visible at all.