Short answer. A plan built on ChatGPT, Gemini and Google AI Overviews quietly assumes every market runs on them. Korea, China and Japan do not. Naver is reported between 42.47% and 63% of Korean search depending on the measurement source, Baidu held roughly 44.62% of all-device Chinese search in April 2026, and Japan splits between Google, Bing and Yahoo Japan. The engines differ, the languages differ, and what transfers from a Western programme is narrower than most proposals assume: the technical fundamentals travel, and nothing else does.
Regional AEO is usually scoped as the same programme run in more languages. That framing survives contact with exactly one region, and it is the one the programme was designed in.
This piece starts with the search-share numbers — including where they disagree with each other — then separates what genuinely transfers between markets from what has to be built locally.
Where does search actually happen?
Start with the numbers, and with the fact that the numbers disagree.
| Market | Reported shares | Source of the disagreement |
|---|---|---|
| Korea | Naver at 42.47% (StatCounter) or about 63% (Internet Trend) | Panel-based and browser-based measurement count portal traffic differently |
| China | Baidu 44.62%, Bing 22.55%, Haosou 18.27% (all-device, April 2026) | Broadly consistent, but shares have moved across a 40–65% range for Baidu since 2025 |
| Japan | Google 59%, Bing 33%, Yahoo Japan 6% on one measure; Yahoo Japan around 40% on another | Yahoo Japan runs on Google's index, so it is counted differently by different methods |
A twenty-point spread on Naver in the same market in the same year is not an error. Both figures are honestly produced by different measurement designs. The practical response is to plan against a range and to distrust any vendor deck quoting one of these numbers without its source.
Two consequences follow before any tactics. First, Baidu's runner-up is Bing at 22.55%, which is unusual and matters because Bing infrastructure has historically underpinned other answer surfaces. Second, Yahoo Japan runs on Google's index, so Japanese coverage is partly a Google problem wearing a different interface — but the interface, the ranking presentation and the user behaviour are not Google's.
What is structurally different, market by market?
| Market | Dominant surfaces | What is structurally different | What that means for AEO |
|---|---|---|---|
| Korea | Naver, Kakao, Google | Portal model: blogs, cafés, Knowledge-iN and shopping sit inside the search product | Owned-domain content is a minor input; presence inside Naver's own properties is the channel |
| China | Baidu, Bing, Haosou, Doubao | Separate index, ICP licensing and hosting realities, distinct AI assistants | Off-shore hosting and unlicensed domains are practical exclusions, not ranking penalties |
| Japan | Google, Yahoo Japan, Bing | Yahoo Japan runs on Google's index but presents differently; unusually high Bing share | Google work transfers partly; Bing share is high enough to warrant its own check |
| Southeast Asia | Google, plus fast assistant adoption | Multilingual within single markets; heavy platform and messaging use | Language coverage per market, not per country, is the unit of planning |
A regional strategy is not one strategy applied in several languages. It is several strategies that happen to share a brand.
The bias that makes this worse
Even where the Western engines do dominate, non-English markets are not competing on level terms.
OMcollective's August 2026 measurement found ChatGPT over-indexed on English pages by a median factor of 2.6 on multilingual sites, fetching the English version 65–79% of the time, while Copilot was close to neutral at 1.07 and Google AI slightly under-weighted English at 0.79. The panels were small — 272 Bing properties for Copilot, 26 multilingual properties for ChatGPT — so the direction is the reliable part.
So in a Japanese or Korean market a brand faces two compounding problems at once. The local dominant engine may not be the one being measured. And on the engines that are being measured, its local-language page is competing against its own English page. The full engine-by-engine breakdown sits in the English bias in AI search.
Regional agencies argue their advantage on exactly this ground: native-language content and familiarity with the platforms that actually dominate each market — Naver in Korea, Baidu and Doubao in China, Yahoo Japan alongside ChatGPT in Japan. Communications groups including Archetype, WE Communications, Hotwire, Sandpiper, Ogilvy and Havas Red now market AI-visibility, GEO and AEO work across APAC, on the argument that buyers across Australia, Singapore, India, Japan, Korea, Southeast Asia, Hong Kong and Greater China use different languages, platforms and publications, so a single regional approach rarely transfers.
How do you scope a multi-market programme?
- Establish the surface mix per market before anything else. Which engines and portals actually carry your buyers' questions there, sized with a named measurement source and its date. This is a research task, not an assumption, and it is the step that gets skipped.
- Write the question set natively per market. Translating an English prompt list measures your translation. A set written by someone who asks questions that way measures the market.
- Separate what transfers from what does not. Crawlability, page structure, entity consistency and sourced specificity transfer everywhere. Platform presence, local corroboration and register do not transfer at all.
- Decide the English page deliberately. On ChatGPT and Copilot the evidence favours having one. It should then be accurate for that market, not a home-market page a buyer stumbles into.
- Put reviewers in-market. The passages engines lift are the specific ones: regulatory wording, service names, units, entity names. Machine translation is fluent and subtly wrong on exactly those.
- Report as a matrix, never averaged. Engine by market. A blended regional score describes nothing that exists.
Why local third-party presence decides most of it
Roughly 85% of AI references point to third-party platforms rather than brand-owned sites, and the top 15 domains account for roughly 68% of all citations produced by the five major answer engines.
In each market the set of high-citation domains is different: different trade press, different directories, different question-and-answer platforms, different encyclopaedic sources. This is the part that cannot be centralised, and it decides most of the outcome. It is also why regional programmes are staffed rather than tooled — a subscription cannot introduce you to a Korean trade publication.
What this cannot promise
- The share figures are contested. Twenty-point spreads on Naver, and a 40–65% range on Baidu across 2025–2026. Plan against ranges, and re-check the source when the number matters.
- Local AI assistants are moving fastest and are least measured. Doubao and the Korean assistant surfaces have far less public citation research behind them than ChatGPT. Measurement there is closer to first-party research than to buying a tool.
- Hosting and licensing constraints are not marketing problems. In China particularly they are prerequisites that a content programme cannot work around.
- Nothing here guarantees a citation in any market, on any engine, in any language.
How Lifewood approaches this
Lifewood scopes the surface mix per market before quoting content, with a named measurement source and its date attached to every share figure used — and a range rather than a point where the sources disagree. That is a short piece of research, and it changes the plan more than any other input: a Korean programme built on Naver's portal properties and a Korean programme built on ChatGPT are different budgets doing different work.
Question sets are authored natively per market rather than translated, and reported as an engine-by-market matrix with each cell carrying its own sample size. Where a market's dominant surface has little public citation research behind it, that is stated as first-party research with its limits declared, rather than presented with the same confidence as a ChatGPT measurement.
The part that cannot be tooled is local presence and local review, which is a staffing question. 50+ languages, 40+ delivery centres across 30+ countries and 56,788 registered contributors are what make in-market authorship and native sign-off the default. See AEO services, top answer engine optimization companies in Asia and choosing multilingual AI visibility services.
Sources and further reading
- StatCounter, Internet Trend and regional trackers, search engine share by country 2026, compiled by Geotargetly.
- SerpSculpt, Search engine statistics by country, 2026.
- OMcollective, English-language bias across AI platforms, 5 August 2026, via Search Engine Land.
- Archetype APAC, B2B PR agencies offering AI visibility, GEO and AEO in APAC.
- Omnibound, Answer Engine Optimization statistics 2026 — third-party citation share.
- AI Platform Citation Source Index 2026, synthesis of six studies covering 680 million citations.