Local GEO strategy: Why you can't copy-paste across markets
The most common mistake marketers can make is when they extend a GEO strategy across markets: treating localisation as translation.
AI search engines do not just read words in a different language; they read a different competitive set, different source authority, and different ways local buyers phrase their questions.
A local GEO strategy has to be researched market by market, not copied from the market where it worked first.
Why translated pages don't win local AI visibility
AI answer engines are reshaping search itself, and how much they reshape it differs by market. A March 2026 SISTRIX analysis of over 100 million German keywords found AI Overviews cut the click-through rate on the number one organic position from 27% to 11%, a 59% relative drop, larger than the roughly 47% drop Pew Research measured in the US and the 32% drop GrowthSRC reported at position one (Search Engine Journal).
Translated pages underperform for a structural reason. AI systems retrieve and synthesise at the level of meaning, not the page. A German page that states the same facts as its English original is often treated as the same information, so the model keeps citing the source it already trusts for that fact, usually a local competitor or publisher, not the newly translated page.
What actually differs market to market
Who gets cited differs by language, not just content quality. A 2026 multilingual audit by Harvard Medical School-affiliated researchers (Beth Israel Deaconess Medical Centre, preprint) examined how ChatGPT, Perplexity and Google AI Overview cite sources across seven languages.
For English queries, the ten most-cited domains accounted for 43.6% of all citations, a narrow, English-language institutional core. For Spanish queries, only 11.3% of citations came from domains based in Spanish-speaking countries, rising to 46.2% once language-appropriate pages hosted on international domains were counted (arXiv).
An illustrative, hypothetical example:
A mid-size B2B SaaS company sells workflow automation software in Finland and Germany.
In English-language AI answers, three US-based platforms typically dominate the citations.
Ask the same buying question in Finnish or German, and the sources shift to two or three regional players with local case studies and local trade press coverage.
Same product category, different citation set per market.
Query phrasing differs, not just vocabulary. A Finnish buyer typing "prosessien automatisointi pk-yritykselle" is not asking a literal translation of "workflow automation for SMBs"; the phrasing reflects a different framing of the problem and often a different stage of buying intent. Keyword research based on machine-translated English lists misses this.
Which AI tool matters, too. In the Harvard-affiliated study, ChatGPT favoured encyclopaedic sources (6.1% of citations) while Google AI Overview favoured social and video content (8.2%), even for the same queries. A local GEO strategy must account for which AI tools local buyers actually use, and each tool's citation habits.
A research process for local GEO strategy
Build a local competitor list from scratch per market, using AI tools and search directly in the local language, not a translated version of the global competitor list.
Collect long-form queries in the local language, sourced from local forums, sales and support conversations, and questions AI tools are already answering there, rather than translating an English query list.
Check what AI tools actually cite in that market. Ask the same buyer questions in ChatGPT, Perplexity and Google AI Overview set to the local market and language, and log which domains get cited, since citation habits differ by platform as well as by market.
Audit local authority signals: local press mentions, local directory listings, local case studies and reviews, and local backlinks. Global brand authority does not automatically transfer into local source credibility.
Monitor for drift. AI answers change as competitors publish and as underlying models update, so a one-time local audit goes stale faster than a traditional SEO audit did, given how fast AI Overviews coverage itself expanded through 2026.
Establish entity clarity for each market. Model how the organisation relates to its local brands, products and offers, using stable naming conventions, predictable URL patterns and consistent internal linking, so each local page reinforces the parent entity rather than contradicting it while still expressing legitimate local distinctions such as regulatory status, availability, pricing or eligibility. When those relationships are unclear, AI systems default to the most confident global interpretation, even when it is wrong for the local market.
The takeaway
The scale of AI search disruption and the pattern of who gets cited both vary by market. Marketers running websites with multiple local versions cannot lift the English competitor list, keyword set and playbook and expect the same visibility elsewhere.
Each market needs its own competitor research, its own query research in the local language, and its own view of which sources AI tools trust there. The investment is in research, not translation.
