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Not necessarily. Juan Camilo Auriti nearly localized his developer product after seeing substantial traffic from Italy, but says his demand measurement found that 90.6% of queries were in English—including many from countries where English is not the first language. He cancelled the project. His case shows why visitor geography alone is a poor proxy for the language customers want; it is not a benchmark for other products.
What Auriti’s 90.6% figure does—and doesn’t—show
In a first-person account on DEV Community, Juan Camilo Auriti describes considering Italian and other translations after noticing traffic from Italy. He then measured demand by language and reports that 90.6% was in English. The remaining 9.4% was spread among other languages, none accounting for more than a few percentage points, according to his account.
The article page displays “Posted on Sep 25” but no year, so the publication year is not established there. Auriti does not provide the underlying queries, sample size, measurement period, country breakdown, language-detector accuracy, or independent validation. Treat the result as one developer-product case, not a forecast for your own market.
His central lesson is concise: “Before you localise, aggregate demand by detected language of the query, not by country of the visitor.”
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Why visitor location can mislead
Country analytics tell you where a visitor is located. They do not tell you which language that person uses to search, read about, or evaluate a product. A developer in Italy might search for a technical tool in English, for example. Translating because many visitors live in a particular country can therefore create work without meeting a demonstrated language need.
That distinction is not a reason to ignore geography. Country, language preference, and query language answer different questions, and together they can help identify a market worth investigating. But country share by itself cannot establish demand for translated pages.
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How to measure language demand
Start with the language of search queries
Auriti recommends using query strings from Google Search Console and grouping demand by detected query language. This gives a more direct signal than inferring language from a visitor’s country. Review the queries and the method used to classify them: short technical terms may be used in several languages, so automated detection can mistake a shared word for evidence of a particular language.
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Auriti’s example skips queries with fewer than two words and records detection failures as unknown. That is a safeguard from his approach, not a universally correct threshold. A two-word cutoff may exclude useful searches in some products or languages. Inspect ambiguous terms where possible, keep an “unknown” category rather than silently assigning it, and make the rule consistent when comparing languages.
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Use other signals as supporting evidence
Auriti also points to a browser’s Accept-Language header and unprompted user text, such as signup responses, support emails, and GitHub issues. He says his own inbound messages were overwhelmingly in English, including those from people in non-English-speaking countries. These signals can corroborate a pattern, but they are not interchangeable: browser preference is not proof of search language, and a small number of messages may not represent the wider audience.
Compare candidate languages before committing
If more than one language seems plausible, compare the evidence rather than relying on a single metric. The source account offers no quantified scoring model, so use the categories below as a decision checklist, not as a formula.
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| Signal or cost | What it can tell you | How to interpret it |
|---|---|---|
| Detected language of search queries | Which language people use in relevant searches | Inspect ambiguous technical terms and track unknown detections. |
| Visitor country and browser language | Where visitors are and their stated language preference | Useful context, but neither signal alone proves demand for translated content. |
| Language of inbound user text | Which language users choose in messages and responses | Look for a consistent pattern; treat the source and volume of messages as context. |
| Product and market constraints | Whether local conditions change what users need | Regulation, local competitors, and products involving money or health may make language needs different from those of a developer tool. |
| Ongoing localization work | The continuing effort required after launch | Account for edits and technical maintenance, not just initial translation. |
Count the work after translation
Auriti describes localization as continuing operational work, not a one-time translation task. A localized site may require maintaining multiple page versions, sitemaps, reciprocal hreflang annotations, and future edits. If any version changes, the related language pages and annotations may need updating too.
He also raises a concern that incorrectly implemented reciprocal hreflang annotations could make it less clear that translated and original pages describe the same product. His account does not demonstrate that search engines or AI systems actually confused his pages; treat this as a risk he considered, not an established outcome.
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When translation may still make sense
Auriti explicitly cautions that his finding is specific to his market. Consumer products, regulated products, products facing strong local competition, and products involving money or health may have different language demand. For those businesses, local language can matter for reasons beyond search volume. Evaluate the product and market directly rather than applying the developer-tool result as a rule.
Translation is easier to justify when several independent signals point to a language need and the team can keep the localized experience accurate. If the signals disagree, investigate the gap before committing: high traffic from a country alongside mostly English queries, for instance, is a reason to distinguish location from language preference—not automatic proof for or against localization.
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