Generative Engine Optimisation for International Brands

The five layers of multi-market GEO

Single-market GEO is largely a content and structure problem. Multi-market GEO is that plus four further problems that do not appear at all until you cross a language border. We work through them in this order because each one depends on the last.

Layer 1: Perception, or how the model describes you in each language

Start by asking the models directly, in each language, and writing down what comes back. Ask what your brand does, who it is for, how it compares with two named competitors, and what it costs. Do it in English, then in each of your target languages, using a native speaker’s phrasing rather than a translation of your English prompt.

What most brands find is a widening gap as they move away from English. The English answer is detailed and broadly accurate. The German answer is thinner. The Japanese answer confuses you with a similarly named company or omits half your product range. Occasionally a market returns nothing at all.

The cause is structural rather than mysterious. These models learned from a corpus that is heavily weighted towards English-language material, so a brand with substantial English coverage and a thin local-language footprint has given the model far more to work with in one language than in the others. Authority does not cross the language boundary on its own, any more than links from your home market establish authority abroad.

• What to record: for each market, note whether the model names you unprompted, whether the description is accurate, whether it is current, and which sources it cites. The citation list is the most useful part, because it tells you which pages the model is actually reading in that language.

Do this before anything else. It is the only way to know whether you have a content problem, an entity problem, or a source problem in a given market, and the answer is usually different in each one. Our AI SEO audit covers the same ground formally if you would rather not run it yourself.

Layer 2: Entry points, or which questions matter in each market

VML’s GEO work centres on what it calls Category Entry Points: the specific buying moments when someone asks an assistant what is best for a given situation. The advice is to identify one or two high-intent questions and make sure your brand is the answer to them every time. It is a sound framework and we use it. It also breaks in an instructive way the moment you apply it across markets.

The entry points are not the same everywhere, and the translated version of your British entry point is frequently the wrong question entirely. The variables that move it:

• Market maturity. In an established market people ask comparison questions: which of these two should I choose. In an emerging one they ask definitional questions: what is this and do I need it. Same product, different funnel stage, different content requirement.
• Regulation. In some markets the first question is about compliance, certification, or consumer rights, and a brand that cannot answer it is dismissed before features are ever discussed.
• Local alternatives. Your competitive set changes by country. An assistant comparing you in Spain is comparing you with Spanish incumbents your UK marketing has never had to mention.
• Purchase norms. Payment methods, delivery expectations, and whether people buy online at all in your category vary enough to reshape the question being asked.

The working method is the same one that applies to keyword research: establish the intent, then find how that intent is actually expressed locally with a native speaker, rather than translating your list. If that distinction is unfamiliar, our guide to translation versus localisation sets out why the two produce different results.

Layer 3: Entity consistency across language versions

Models build a representation of your brand as an entity, assembled from everything they have read about it. When your language versions disagree, that representation gets weaker rather than richer, because the model has no reliable way to decide which version is correct.

The disagreements are usually mundane and almost always invisible from inside the organisation, because no one person sees every language version at once:

• The company name rendered differently across markets, sometimes with a local legal suffix, sometimes translated, sometimes transliterated.
• Different founding dates, employee counts, or headquarters on different language versions of the about page.
• Product names translated in some markets and left in English in others, with no statement anywhere that they are the same product.
• Organisation schema present on the English site and missing everywhere else, or present but carrying different values.
• Local directory, association, and marketplace listings that were created years ago by regional teams and never reconciled.

The fix is unglamorous and effective. Decide the canonical facts about your organisation, publish them identically in structured data on every language version, use sameAs to connect your language versions and your authoritative external profiles, and state explicitly when a locally named product is the same product as its English counterpart. Where a name genuinely differs by market, say so in text rather than leaving the model to infer it.

This layer is the highest ratio of effect to effort in the whole framework, because it is a data problem with a definite answer rather than a content problem without one.

Layer 4: The sources a model trusts are local

NEWTON Media’s AI visibility work makes a point worth borrowing: generative systems frequently draw on sources companies do not expect, including forum threads and community discussions rather than corporate sites or major news outlets. An outdated negative thread can shape an AI answer long after it has stopped appearing in search results.

Extend that across markets and the implication is sharper. The trusted sources are different in every country, and the ones you know are the ones from your home market.

The work here is closer to digital PR than to technical SEO, and it does not scale the way translation does: each market needs its own relationships, in its own language, with people who already have standing there. Our piece on digital PR covers the mechanics, and the same principles that govern content strategies for international brands apply to which sources you pursue.

One caution. Do not try to participate in every community platform in every market. Understand where conversations about your category happen, check whether they are affecting your AI visibility, and act only where they are.

Layer 5: Measurement, per market rather than in aggregate

Aggregate AI visibility reporting hides exactly the problem this guide is about: a brand can look healthy overall while being invisible in three of its five markets, because the English numbers carry the average. Segment everything below by market and by language, and accept that this measurement is directional rather than precise. Models are not deterministic, the tooling is young, and anyone offering exact AI visibility figures is overselling.

• Citation rate. How often you are cited in AI answers for your priority questions in that language.
• Answer share. What proportion of relevant local queries mention you at all.
• Description accuracy. Whether what the model says about you in that language is correct and current. This is the metric that matters most internationally and the one nobody tracks.
• Entry point coverage. How many of that market’s buying questions you appear in.
• AI referral traffic. Segmented by source domain and landing page language. Note that some assistants strip referrer data, so this will always understate the real figure.

Expect influence you cannot see. A model can compare you favourably, and the buyer can act on it, without any of it appearing in your analytics. That is the same measurement problem described in the great decoupling, arriving from a new direction, and it becomes more acute as agentic commerce lets assistants complete transactions without the customer ever reaching your site.

Building your localised GEO plan

Which layers to prioritise depends on the regions you operate in, your vertical, and whether you can currently see any AI referral data. Work through the three below, then use the audit sequence.

By region

By vertical

• Luxury B2C: perception accuracy is the priority. Models flatten positioning towards generic category language, and a diluted description does more damage here than a missing citation.
• Enterprise B2B: entry points and sources. Buying committees use assistants during shortlisting, so the objective is appearing on the list at all, in the local language, cited by sources procurement recognises.
• E-commerce: entity and measurement. Product data consistency across language versions determines whether assistants can compare you accurately, and agentic purchasing makes this the most urgent of the three.

By measurement maturity

• Not tracking AI referrals yet: start with Layer 1. Manual prompting in each language costs nothing and gives you a baseline within a day.
• Tracking in aggregate: segment by market and language before doing anything else. The aggregate view is actively concealing your worst markets.
• Tracking per market already: move to Layers 3 and 4, where the remaining gains are.

The audit sequence

1. Prompt every model in every target language and record the description, the accuracy, and the cited sources.
2. Score each market on whether you are named, described accurately, and cited. Three columns, one row per market.
3. Reconcile your entity data across every language version, then publish it identically in structured data.
4. Identify two entry points per market with a native speaker, rather than translating your existing list.
5. Map the trusted sources in each market from the citation lists you recorded in step one.
6. Build or improve one asset per entry point in the local language, structured for extraction.
7. Pursue local sources in one market first, measure for three to six months, then extend.
8. Re-run step one quarterly and track the direction of travel per market.

A content structure that travels

For each priority page, in each language: answer the entry point question directly in the first two sentences, use headings phrased the way local users ask, keep paragraphs short enough to extract cleanly, add a comparison table where a choice is involved, include an FAQ block built from real local questions, mark it up with structured data carrying your canonical entity values, and name an author with credentials that mean something in that market. The structure is the same everywhere. The questions, the phrasing, and the trust signals are not.

If you would rather have specialists run the diagnosis and remediation for you, our international SEO agency team does exactly this across every market and language a brand operates in. So, if your brand is showing several of the gaps we’ve discussed, then get in touch today to have our team audit how AI describes you in each of your markets and prioritise the fixes.