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In FTA Global’s test, an average of 2.9 of the top five AI-recommended brands changed outside a “New York + English” baseline. This article breaks down four ways AI buying questions differ across markets (where people ask, local constraints, cited sources and recommended brands) and gives a five-step method to compare them.
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Updated on Oct 22, 2026
In this article, "AI buying questions" means the shopping, comparison and pricing questions people ask ChatGPT, Gemini, Google AI Mode and other AI tools.
A "market" here is three variables stacked together: the user's country or region, the language of the question, and the AI platform. Change any one of them and the same buying question can get a different answer. The UK and the US share a language, while India mixes English, Hindi and Hinglish, so country and language need to be recorded separately.
Location alone can rewrite an answer. In a test published in October 2026, Massive sent ChatGPT the same prompt asking for "a typical price for a cup of coffee at a cafe" from the United States, Brazil and Japan at the same time. The prompt named no country. The US answer quoted dollars, while the Brazil and Japan answers added prices in reais and yen. Each country was run only once, so the test shows that the effect exists, not how often it occurs.
Keep two layers apart in your analysis. The question layer is what users asked and which conditions they attached. The answer layer is which sources the AI used and which brands it recommended. When a brand "disappears" in a market, local users may simply be asking a different question, or the AI's sources and preferences may have changed. The next four sections follow that order.
Four layers of difference, covered in the four sections below.
ChatGPT leads in every major overseas market, but the order behind it differs. Statcounter's September 2026 data puts ChatGPT at roughly 74%–80% in each of the seven markets below. Gemini is above 16% in Brazil, Japan and India but about 10% in the UK. Copilot is above 6% in the US and Japan but below 2% in India. Perplexity is higher in Germany and France than in the other markets shown.
Source: Statcounter Global Stats, AI chatbot referral share, September 2026, seven markets.
Mind the metric. Statcounter measures each chatbot's share of referrals to websites (based on page views on the sites it tracks). It is not a count of questions or users, and it excludes Grok, which does not pass referral data. It tells you which platforms send people to websites in a market; it cannot be used to estimate shopping question volume.
Survey data points the same way. A World Retail Congress survey commissioned by Vercel (May 2026, 4,000 European shoppers), as reported by Pygmalios, found that 49% of respondents in Southern Europe use ChatGPT when shopping, versus 40% in the Nordics, 38% in Germany and 33% in the UK. Half of UK respondents use no AI search at all when shopping, compared with 32% in Southern Europe. These figures are self-reported, Southern Europe and the Nordics are pooled multi-country groups, and Pygmalios itself cautions that "stated intentions routinely overstate observed behavior."
Feature rollout order also creates differences. When OpenAI launched Instant Checkout inside ChatGPT on September 29, 2025, it was open only to US users, and the only merchants were US Etsy sellers. A "buy it for me" request could be completed inside the chat in markets with checkout, but elsewhere it could only send users to an external site. We have not verified which markets the feature covers as of October 2026; check this separately before comparing markets.
Even when the question is "which one should I choose," each market attaches different conditions. Some are written into the question; others are filled in from the user's location.
Not every need varies by market. In the World Retail Congress survey above, price comparison was the most common use among AI shoppers in every region (51%–55%). Our judgment: the action in a question is similar across markets, and the differences sit mainly in budget ranges, available channels and local rules. So when you organize questions, give budget and currency, named platforms, payment and delivery, regulations and device conditions their own columns.
For buying questions in non-English markets, much of the evidence still comes from English-language web pages. Peec AI analyzed more than 10 million ChatGPT prompts and more than 20 million background searches (query fan-outs), keeping only sessions where the user's location matched the prompt language. Nearly 78% of non-English sessions added English-language research, and 43% of the fan-outs for non-English prompts were run in English. By language, Turkish sessions added English research most often (94%) and Spanish sessions least often (66%).
The weight of local sources also varies by country. In FTA Global's test, local domains and publications made up 42% of cited sources in Germany, 34% in France and 17% in India. Where local sources carry less weight, local brands may need coverage on English-language or international sites to enter answers. Peec AI's manual tests include one example: asked in Polish from a Polish IP address about the best auction sites, ChatGPT either left out Allegro, Poland's leading local platform, or buried it far down the list. This is a single example, not a statistical result.
Trust in AI answers differs too. In the same Deloitte survey, 61% of respondents in Spain said they trust information provided by AI, versus 36% in Finland. Our hypothesis: in lower-trust markets, users are more likely to follow up on sources, reviews and after-sales guarantees, so locally verifiable material matters more. No public data tests this directly yet.
Stack the first three differences together and users end up seeing different shortlists. Using "New York + English" as the baseline, FTA Global repeated the same small-business buying questions in London, Madrid, Paris, Berlin and three Indian cities:
Language also changes the top pick. A test by Reza Moaiandin, co-founder of SALT.agency, published in Search Engine Journal, asked the same set of buying questions in English, Arabic, Russian, Simplified Chinese and Persian. The core test used Claude Sonnet 5 with web search off, collected on September 7, 2026, with 40 runs per scenario-language pair. The table shows the share of answers in which each brand was the top pick:
| Scenario | Brand | Highest share | Lowest share |
|---|---|---|---|
| London–Tenerife return flight | easyJet | Russian 72.5% | Persian 12.5% |
| Noise-cancelling headphones under £400 | Sony | Russian and Persian 100% | English 50% |
| CRM for a UK small business | HubSpot CRM | English 22.5% | Arabic 2.5% |
| Robot vacuum under £500 | Roborock | Russian 97.5% | English and Arabic 87.5% |
The robot vacuum row shows that not every category swings widely. In the same test, turning on web search for two of the categories changed the top pick in 7 of 10 category-language pairs. For those same two categories with search off, ChatGPT's top pick matched Claude's in only 1 of 10 pairs, and Gemini's in 5. The author notes that the data was collected in a single afternoon and that the results are directional.
Academic research observes a similar tendency. The EMNLP 2024 paper "Global is Good, Local is Bad?" found that LLMs more often associate positive attributes with global brands and disproportionately recommend luxury gifts to people in high-income countries, although country-of-origin effects may boost local brands in specific contexts. For brands, this means competitors are market-specific: the brands you go head to head with in the US may be replaced by a different set of local brands in India or Poland.
Treat "country or region × language × platform" as the smallest unit of comparison and follow five steps.
| Dimension | What to record for each market | Common misreading |
|---|---|---|
| Where people ask | Main local AI platforms; whether shopping cards, checkout and similar features are available | Testing only ChatGPT, only in US English |
| Question constraints | Original question text; budget and currency; platforms, payments, delivery, regulations and devices mentioned | Treating a translated English question as a local question |
| Evidence sources | Cited URLs, source language, local versus international sites | Counting domains without reading the sources |
| Recommendations | Top pick, top-five list, whether your brand appears and in what context | Letting one screenshot stand for a whole market |
The four dimensions above map onto several Dageno features:
Take the smart watch market in a Dageno demo workspace as an example (Apple is only the example brand and is not a Dageno client). The regions table under "Market overview → Platforms & regions" shows Apple ranked #1 in most markets but #2 in Spain, Poland and Italy, where Garmin leads.
Figure 1: One smart watch market, with brand rank and visibility split by AI platform and region.
The regions table only tells you where to look first; you need to open the answers to see the questions themselves. In Dageno's published case study, AI Visibility by Region: When to Localize Pages, Apple ranks #14 in India with 4.76% visibility. Reading the answers shows that both Indian answers recommend watches under ₹5,000, while Apple's India site listed the Apple Watch SE 3 from ₹29,900 (checked on September 30, 2026, per the case study).
Figure 2: ChatGPT's answer for India (IN), with the question limited to under ₹5,000.
The Polish answer, by contrast, is in Polish with a 1500 zł budget. It recommends Amazfit, Garmin, Huawei, Samsung and other watches, and its citations come mainly from local retail and price-comparison sites such as euro.com.pl, ceneo.pl, mediamarkt.pl and allegro.pl.
Figure 3: ChatGPT's answer for Poland (PL), recommending watches in Polish within a 1500 zł budget.
These two markets match the two kinds of difference described above: in India the budget constraint in the question changed; in Poland the language and the local sources changed. The samples are small (two answers for India, only one comparable answer for Poland), so the case study concludes "mark as a non-target question" for India and "keep observing" for Poland.
These views record saved AI answers. As Dageno's market expansion page puts it, cross-market AI visibility "does not measure total demand, market size, sales potential, market share, or buyer preference." Choosing a market still requires first-party research plus product, channel and compliance review. According to the Dageno blog, the free lookup covers 86 countries and regions and 12,000 market segments, so you can start by checking where your brand stands in a target market.
When comparing markets, the following factors are easy to mistake for "market differences," or can push the cost of comparison beyond expectations.
Only as "the same task asked in the local language." It does not capture local budgets, channels or everyday phrasing. Translated questions work as a cross-market control set; add native phrasings from local search suggestions, forums and customer service records.
Follow the languages local buyers actually use. In multilingual markets such as India, Switzerland and Canada, test at least the main languages separately. If some of your buyers ask in English (for example, multinational procurement teams), record English phrasings separately too.
There is no universal number. In public tests, FTA Global ran each condition 5 times and SALT ran 40. Our recommendation: do not draw market conclusions until the top pick is stable across repeated runs, and sample more in volatile categories.
Not for most markets. ChatGPT has the largest referral share, but Gemini exceeds 16% in some markets, and ChatGPT and Gemini share on average only 3.2 of their top five brands for the same question. At minimum, add the second- and third-largest platforms in each market.
No. First check whether the question itself is different, for example a budget below your product line. Then check whether the product is sold locally and what local sources say. A localized page is worth it only when local facts genuinely differ and repeated sampling keeps showing your brand missing.
AI buying questions in different overseas markets differ at four layers: which platform people ask, which constraints the question carries, where the AI gets its evidence, and whom it finally recommends. Public tests already show that looking only at US English results can mislead you on more than half the shortlist (in FTA Global's software-category test, an average of 2.9 of the top five changed). Public data on how local users actually ask is still scarce, though, so every brand needs to sample its own markets and categories.
Further reading:
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Dageno
Dageno is the research and insights team at Dageno AI, publishing industry reports and expert analysis on AI Search Visibility, Generative Engine Optimization (GEO), and AI-powered search discovery.
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