Find the growth opportunities worth prioritizing across market demand, the competitive landscape, Brand Value Props, and citation sources.
Translating your home-market content is not enough when you enter a new country or category. This guide shows, in four steps, how to find the buying questions local users ask first: define the market, collect questions from local search, communities, sales records, and AI answers, filter them by product fit, demand evidence, and local facts, rank them by buying stage, and plan your first content by question type.
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Updated on Oct 23, 2026
In this article, “ask first” means two things. The first is early in time: the questions users ask right at the start, when they begin considering a purchase in this market, such as “how do I choose this kind of product” or “what can I get for this budget.” The second is high in frequency: questions that come up again and again locally and show up in several sources. Your first content should cover the overlap of the two.
Why not just move your home-market content over? Public tests give two reasons. First, when the market changes, the shortlist AI recommends changes too: in FTA Global’s test, once queries moved away from the “New York + English” baseline, an average of 2.9 of the top five recommended brands were replaced. Second, what AI draws on changes as well: Peec AI’s analysis of more than 10 million ChatGPT prompts found that nearly 78% of non-English conversations also ran English-language searches, so local and English sources get mixed together. What users in a new market ask, and what AI uses to answer them, both need to be checked on their own.
Another common mistake is to assume nobody is asking because keyword tools show low or zero search volume in the new market. Our view: buying questions in AI often carry conditions such as budget and use case, and keyword tools have trouble fully covering long phrasings like these. To judge whether such questions reflect real demand, see How to Find New Demand in AI Questions Keyword Tools Miss.
How you define the market sets the scope of question collection. Entering a new country or language market and entering a new category require you to write down different things.
| What to write down | New country or language market | New category |
|---|---|---|
| Comparison unit | Country or region × language × AI platform; split multilingual countries by language | Category boundary, subcategories, and target users; then add target countries and languages |
| What you sell locally | Available models, local prices, purchase channels, delivery and warranty | Launch products, price bands, how they relate to your existing product lines |
| What users already know | The category is usually known; your brand may be unfamiliar | Your brand may not yet be recognized in this category |
| Platforms to watch | The main local platforms, not just ChatGPT | The platforms your target users commonly use |
Don’t skip the platform row. In Statcounter’s September 2026 data, Gemini’s share of chatbot referrals was above 16% in Brazil, Japan, and India, and about 10% in the UK. This is the share of referrals to websites, not question volume, but it is enough to show that each market needs its own mix of platforms to watch.
Write down “what you sell locally” before you collect questions. If prices and availability are unclear, you cannot judge later whether a question is worth pursuing. These commercial facts should be confirmed by the product and local teams; the content team should not guess them.
Collect candidate questions from several local sources, because each source shows only part of the picture. The table below lists common sources by what you can see and what to watch out for.
| Source | What you can see | What to watch out for |
|---|---|---|
| Local search suggestions and related questions | The words and phrasing local people actually use | Search in the local language from a local location; Google has explained that autocomplete takes the searcher’s language and location into account |
| Search Console | Queries for which your existing site already gets impressions in the target country | Look at the “Country” and “Query” dimensions together; it only reflects what you can already reach |
| Local communities and review sites | Concerns and comparisons users write in their own words | First confirm the questions come from local users, not from discussions in other countries |
| Sales, support, and distributor records | The questions asked most often before a deal closes | A new market usually has none yet; ask local partners first |
| Sampling AI answers locally | How AI understands the question, which sources it cites, and whom it recommends | Use the local language and location, and sample each question several times |
| Competitors’ local content | Which questions competitors already answer | Only a lead; it does not mean users actually ask these questions |
When you sample AI answers, location alone changes the answer. In Massive’s October 2026 test, the same coffee-price question, with no country named, was put to ChatGPT from the US, Brazil, and Japan. The US answer quoted prices in dollars, while the Brazil and Japan answers added reais and yen. So your sampling log should record the location, language, platform, and date.
You can translate your home-market questions, but use them only as a control group: to spot which questions nobody asks in the new market, and which questions only the new market asks. For every candidate question, keep the original wording, the source, the collection date, and a translation in your team’s working language.
Merge first, then rank. Put near-identical phrasings of the same buying task, with the same conditions, into one group, and keep one representative phrasing that is closest to how locals say it. If the budget, use case, device, or regional conditions differ, split them into separate groups.
Check each group against four criteria:
Both surveys are self-reported. CI&T’s press release and the coverage of it disclose neither when the survey was conducted nor whether these shares are based on all respondents or only on AI agent users. They can show which uses are common, but they cannot replace sampling in your target market. Our view: when entering a country market, start your first content with selection, comparison, price, and where-to-buy questions; when entering a new category, also add questions that help users understand the category (see the new-category section below). Of the four criteria, product fit, demand evidence, and local facts decide whether a question can enter the first batch, and buying stage decides its order within the batch, as shown below.
Diagram: how to filter questions for your first content
Different types of questions need different content formats, and each needs different local facts spelled out. The table below maps them. The “Example phrasing” column only shows the structure; use the original wording you collected locally.
| Question type | Example phrasing | First content format | Local facts you must spell out |
|---|---|---|---|
| Choosing by need or scenario | How to choose a product for a certain use | Scenario buying guide | Locally available models, conditions of use |
| Choosing by budget | Options under a certain price | Price-band guide or product line comparison | Prices in local currency and the date checked |
| Comparing specific options | Which is better, A or B | Like-for-like comparison page | Comparison conditions, data sources |
| Features and compatibility | Does it work under a certain condition | FAQ or compatibility notes | Local specifications, certifications, language support |
| Price and costs | How much does it cost in total | Pricing and costs page | Taxes, shipping, subscriptions, or consumables |
| Channels and service | Where to buy, how the warranty works | Purchase and after-sales page | Local channels, delivery, returns, and warranty |
When you plan your first content, keep four points in mind:
Entering a new category and entering a new country follow the same four steps, but they start from different places. When you enter a new country, the category usually already exists, and users ask “which one should I buy here.” When you enter a new category, your brand is not yet known in it, and users’ questions come from an earlier point in the journey.
| Difference | Entering a new country or language market | Entering a new category |
|---|---|---|
| Where questions start | Selection, comparison, price, channels | Also “what problem does this kind of product solve” and “do I need one at all” |
| Competitors | Local brands and international brands already in the market | Brands already leading in AI answers for the category |
| Evidence most lacking | Local prices, channels, certifications | Specifications, tests, third-party reviews |
| First content focus | Localized selection and purchase information | Category buying guides, like-for-like comparisons with leading brands |
This comparison is our judgment, not a research finding. To check it, look at how the questions you sample are distributed: if a new category has few “understand the category” questions, you don’t need separate content for them.
Also watch for a bias. The EMNLP 2024 paper “Global is Good, Local is Bad?” found that large language models more often link positive attributes with global brands. A 2026 arXiv preprint by Chu and Hou (the page does not say it has been formally published or peer-reviewed) tested GPT-4o-mini, Claude Sonnet, and Gemini 3 Flash with skincare products. When product specifications were identical, the well-known brand was recommended 100% of the time, but that advantage disappeared once a competitor’s rating was higher by less than 0.1 stars. Our inference: newcomers make the shortlist with differences that can be checked (specifications, tests, real reviews), not with slogans.
If you are launching a new product in an existing category rather than entering a new category, you can start choosing questions from the buying tasks where your previous-generation product already appears. For the method, see New Product GEO Strategy: Choose AI Buying Questions.
Here is how the four steps above map to Dageno:
Take the Dageno demo workspace as an example (Apple is only a sample brand; this does not mean Apple is a Dageno customer). The Market segment map puts one brand’s position in different categories side by side: Apple ranks #1 in Smart Watches and #3 in Headphones, where Sony leads with 54.6%. Before you enter a new category, first see who leads in the answers for that category.
Figure 1. Market segment map: the same brand’s position varies widely across categories.
In the Search intents view for the Smart Watches market, the analyzed questions fall into 7 primary intents and 25 sub-intents. Recommendations has a demand share of 55.1%, and within it, Choose for your needs accounts for 81.2%. Capabilities, fit & availability is at 10.5%, Evaluation & comparison at 9.7%, Pricing & cost at only 3.0%, and Providers & purchase channels at 2.4%.
Figure 2. Search intent overview: Recommendations accounts for 55.1% of the analyzed questions.
These numbers seem to conflict with the surveys above: in the surveys, price comparison is the most common use, but here Pricing & cost is only 3.0%. One possible explanation is that budget is often written into selection questions rather than asked as a separate price question: Choose by budget or tier is a sub-intent under Recommendations, at 5.5%. This is a guess. What it means for content is that price information belongs inside your selection and comparison content, not only on a standalone pricing page.
Demand share describes how the analyzed questions are distributed; it is not search volume or real purchase demand. The Market entry analysis page also states that AI answer visibility and AI Search Intents “do not measure total demand, market share, revenue, or buyer preference.” Whether to enter still requires first-party evidence plus product, financial, channel, and compliance review.
Start with public sources: local search suggestions, community discussions, and competitor content. Then sample AI answers repeatedly in the local language and location. Questions that appear in several sources and hold up across samples go on the candidate list first. You can also ask local partners or distributors which questions they hear most often.
There is no universal number. Base it on how many local facts your team can verify, how much price and channel information it can maintain, and how many questions it can retest. We suggest covering the top few groups of selection, comparison, price, and channel questions first, then expanding after you retest.
As a first draft, yes, but not as your first content as it stands. Rewrite titles and questions to match local phrasing, swap in local prices, channels, and specifications, and have native speakers review it. If a piece exists in your home market but nobody asks about it in the new market, don’t bring it over.
When you enter a new country, your first content leans toward localized selection, price, and purchase information. When you enter a new category, you also need category buying guides and like-for-like comparisons with the category’s leading brands.
Retest with the same set of questions you used before publishing, on the same platforms and in the same regions and languages, and save the new answers and citations. A change in visibility or citations does not by itself prove the content caused it, so read it together with first-party data. For the detailed method, see AI Recommendations: Verify Improvement After Content Updates.
When you plan your first content before entering a new market, the key is to learn what local users ask at the start and whether you can answer it well there. Define the market, collect questions from local sources, rank them by evidence and fit, and match content to question types. Once these four steps are done, your first content has a starting point you can retest. Public data can help you list candidates, but only sampling in your target market can tell you which questions come first.
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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