Don’t use visitor geography alone to decide whether to translate a product. Measure the language people use to search for it, then compare that evidence with browser preferences, customer messages, market conditions and the ongoing cost of maintaining localized pages.
What one developer learned from measuring language demand
Juan Camilo Auriti nearly localized a developer product after seeing a noticeable share of traffic from Italy. But he says that when he measured demand by the language of search queries, 90.6% was in English. The other 9.4% was spread across several languages, with no single language accounting for more than a few percentage points. He says he cancelled the localization project.
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That result is a case study, not a benchmark. Auriti’s account does not provide the raw queries, sample size, measurement period, country breakdown or independent validation. It describes his market and should not be generalized to other products. His key distinction is that a visitor’s location does not tell you which language they use to search, read about or evaluate a product. Read Auriti’s account.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteMeasure the language of demand, not just its location
Country analytics answer where visitors are. They do not establish the language those visitors prefer. A developer in Italy, for example, may search for a technical tool in English. If you treat traffic from Italy as evidence of demand for Italian pages, you may mistake geography for language preference.
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Auriti recommends examining search queries and grouping them by detected language. He names Google Search Console as one source of query strings. This approach is more directly relevant to the question “Which language should we translate into?” than country totals alone, though query data still has limitations and does not capture every potential customer.
Handle ambiguous queries carefully
Short technical queries can be hard to classify: a one-word term may be used in multiple languages. Auriti’s example code skips queries with fewer than two words and records language-detection failures as unknown. That is a safeguard from his method, not proof that two words is the right cutoff for every product. Review ambiguous terms where possible, keep an unknown category rather than forcing a guess, and avoid treating automated labels as certain.
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Cross-check search data with other language signals
Search-query language is one useful measure, not the only one. Auriti also mentions browser Accept-Language preferences and unprompted text from signup responses, support emails and GitHub issues. He says the inbound text he observed was overwhelmingly in English, including messages from people in non-English-speaking countries.
These signals describe different things. A browser preference may not match the language someone uses for technical searches, and the language of messages from current users may not represent prospective customers. Consider them alongside query data rather than combining them into a single supposedly precise score.
- Search queries: What language do people use when looking for the product or the problem it solves?
- Visitor geography: Where are visitors located? Use this as market context, not as a substitute for language evidence.
- Browser preference: What language does the browser indicate the user may prefer?
- Inbound text: What language do users choose without being prompted to write in a particular language?
Decide whether the evidence justifies ongoing localization
Translation is not a one-time task. Auriti points to the continuing work of maintaining multiple page versions, sitemaps, reciprocal hreflang annotations and future edits. A translated page that falls out of date can become a poor representation of the product; the decision should account for maintenance as well as initial translation.
He also raises a concern that original and translated pages describing the same product could create entity ambiguity if reciprocal hreflang implementation is wrong. His article presents this as a risk assessment; it does not demonstrate that search engines or AI systems actually confused his pages. Treat correct implementation as a technical consideration, not as proof that localization will cause confusion.
Before committing, compare the evidence and constraints for each candidate language:
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- Other user signals: Do browser preferences and unprompted customer messages support the same opportunity?
- Product and market conditions: Is the product consumer-facing, regulated, tied to money or health, or competing with strong local alternatives? Auriti specifically cautions that these circumstances can change language demand.
- Maintenance capacity: Can the team keep translated pages and technical annotations aligned with product changes?
These are comparison factors, not a validated scoring formula. A market with limited query evidence may still merit localization for product-specific or regulatory reasons; conversely, visits from a country alone do not establish that translation will help.
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What Auriti’s 90.6% figure can—and cannot—tell you
Auriti’s reported 90.6% English share shows why country traffic can be a misleading proxy for language demand in one developer-product case. It does not predict the language mix for another developer tool, much less for a consumer service or a product with regulatory, financial or health implications. The source does not publish enough underlying data to independently assess the estimate or reproduce it.
The practical takeaway is to measure the language people actually use, check that measurement against other customer signals, and weigh the ongoing work before translating. As Auriti puts it: “Before you localise, aggregate demand by detected language of the query, not by country of the visitor.”
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