Local SEO in LLMs

The Most Important Points Upfront:

• Local SEO is no longer a pure search topic. With AI integration in Google Maps, local visibility is increasingly determined during navigation — not just on the search results page.
• LLMs evaluate businesses not by keywords, but by entity clarity. Consistent, complete, and semantically rich data across all touchpoints is the new prerequisite for local visibility.
• The requirements for Local SEO differ fundamentally by company size: small businesses win through depth, mid-sized companies often fail on consistency, and large enterprises struggle with insufficient location-specific content depth.

How LLMs Are Structurally Changing Local SEO

The local customer journey was long linear: search, Local Pack, website, conversion. LLMs and AI Overviews disrupt this chain at multiple points simultaneously.

More than half of all local search queries now end without a single click. Users get directions, phone numbers, reviews, and recommendations directly from Google Maps and AI snapshots. This means: a significant portion of your potential customers make a decision about your business without ever having seen your website. Your digital presence outside the website is therefore no longer a supporting channel — it is the primary touchpoint.

For SEO professionals, this is methodically challenging because classic tracking logic fails here. A user who calls your phone number directly through an AI answer doesn't show up in any attribution. The demand is real, but measurement doesn't capture it.

From Keyword to Entity

LLMs don't think in keywords — they think in entities and their relationships. The question an LLM asks when evaluating your local presence is not: 'Does this page contain the keyword dentist Vienna?' The question is: 'Is this business a trustworthy, clearly defined entity that I can recommend with confidence?'

LLMs verify this trustworthiness not from a single source, but by cross-referencing across all available data points: website, Google Business Profile, review platforms, industry directories, and social media profiles. Discrepancies in this data are interpreted as a trust problem, not an error. The AI system will, when in doubt, choose the business whose data is coherent — even if it's weaker in other dimensions.

Schema Markup is therefore not an optional technical measure, but the direct language that LLMs understand: whoever marks up business data in a structured way gives AI systems a reliable foundation for recommendations.

Google Maps as an AI Surface

Google Maps is evolving from a static navigation solution into a conversational assistant: with 'Ask Maps,' Google integrated a conversational feature directly into Maps in March 2026 that answers complex, multi-step questions. Users can ask, for example, 'Is there a tennis court with lighting on my route that I can still use tonight?' and receive a personalized answer with a map, opening hours, and a direct navigation option.

The AI answers questions based on real-time data from the Google Business Profile, including inventory, opening hours, and review content. Whoever is not represented with complete, up-to-date data simply won't be recommended to that user at that moment.

The shift: local visibility is no longer just a matter of rankings — it's a matter of data availability in real time.

This applies not only to the Google Business Profile. Since ChatGPT partially accesses Bing data for local queries, a complete and up-to-date Bing Places profile is no longer an optional step today. The same applies to Apple Business Connect: whoever wants to be visible in Siri answers and Apple Maps needs a well-maintained presence there. The logic is the same as with the Google Business Profile: the more complete and consistent the data, the higher the probability of being recommended in an AI-generated answer.

Use Cases: What This Means by Company Size

Small Businesses: The Advantage of Depth

For small, locally anchored businesses, LLMs present an unexpected opportunity. AI systems prefer depth over breadth. A business that provides complete and semantically rich information for a very specific local context has structurally better chances of being recommended in an AI answer than a larger generalist with thin data.

Specifically: a physiotherapy practice in Vienna-Margareten that describes on its website which specific conditions it treats, which health insurers are accepted, how appointment booking works, and whose Google Business Profile contains reviews in which patients explicitly mention 'fast appointment availability' and 'specialization in athletes' — that practice will, with high probability, be named by an LLM for the query 'physio Vienna short notice sports medicine.' A competitor with 50 generic five-star reviews and an incomplete profile description will not.

The mistake small businesses make here: they underestimate how much semantic work still needs to be done on the Google Business Profile and the website. The profile is filled out, but not optimized. The website exists, but doesn't speak a language that an AI can understand and cite.

Mid-Sized Businesses: The Consistency Problem Scales

For businesses with multiple locations — for example, a law firm with offices in Vienna, Graz, and Linz — the central problem is not missing quality, but missing consistency.

The Google Business Profile is, at 32 percent (according to a Whitespark study), the single most important ranking factor in the Local Pack and Maps. Inconsistent or incomplete profile data across multiple locations is interpreted by AI systems as entity uncertainty. If the Graz branch has different service attributes in the GBP than the main office in Vienna, if opening hours differ between the website and the profile, or if the company name is formatted differently in various directories, the entire brand loses trust in the AI system.

The operational challenge: who in a company with 15 locations is responsible for keeping all profiles up to date daily? In most mid-sized structures, there's no clear answer to this. And that's exactly where one of the greatest untapped leverage points lies.

Large Enterprises: From Rankings to Entity Authority

For large corporations with strong brand recognition, the question shifts. Ranking is not a problem. Entity Authority is.

LLMs evaluate local visibility by consistency, completeness, timeliness, and the quality of review sentiment. For large brands, an additional factor comes into play: they are frequently mentioned in AI answers as a category reference, but not always as a concrete recommendation for a specific location. The challenge is not getting the brand into an answer — it's linking the right branch to the right query.

This requires what is often missing in an enterprise context: location-specific content depth. A bank with 80 branches doesn't need 80 identical location pages that differ only in the address. It needs 80 pages that each describe the specific context of the location — the surrounding neighborhoods, the typical customer profiles, the location-specific services. That's effort. But it's the effort that determines whether an LLM recommends the Vienna-Floridsdorf branch for 'home financing easy first meeting Vienna north' or not.

Practical Tips: What to Prioritize Now

• Entity Clarity before content volume. Before producing more content, ensure the AI can clearly identify your brand. Clear company descriptions on website and GBP, consistent service naming across all touchpoints, complete contact details without discrepancies. This is the foundation everything else is built on.
• Review strategy, not just review quantity. LLMs extract specific service attributes and trust signals from reviews. A business that proactively guides customers to describe concrete situations and services in their reviews builds a semantic profile that AI systems use directly for recommendations. 'Great service, would come again' is not as valuable and citable for an LLM as 'Last-minute weekend appointment, quick diagnosis, clear price information.'
• Introduce conversational content formats. LLMs are trained on questions, not statements. Pages structured in question form, with direct, precise answers under each heading, are preferred by AI systems as sources. FAQ pages with Schema Markup are not just user experience tools — they are direct inputs for the answer rendering of LLMs.
• Prioritize location-specific depth. Generic pages with 'city plus service' are increasingly losing ground in AI-driven search queries. LLMs prefer content that demonstrates granular, location-specific expertise. That doesn't mean building keyword pages — it means providing real contextual information: what makes this location special? Which specific customers does it serve? What local circumstances are relevant?

How Improove Helps with Local SEO and GEO

Local SEO for Improove is not an isolated service offering — it's part of an integrated system: first we understand how your business is currently perceived in classic search engines and AI systems before we take action. What does a GBP audit show? Where are there data discrepancies? In which AI answers do you appear, and with what tone? Where is semantic depth missing?

On this basis, we develop a roadmap that treats SEO and GEO not as parallel disciplines, but as a system built on top of each other. Local visibility today is not created through more activity, but through better data architecture, clearer entity definition, and consistent maintenance of the touchpoints that AI systems actually evaluate.

About the Author

Diana Skof is Head of Marketing at Improove. Her focus is on developing holistic, cross-channel marketing strategies where content and SEO work seamlessly together. Her view of modern marketing is that strategy and creativity must go hand in hand to create lasting relevance in the digital space.