The same AI logic does not have to give the same result in every market.
International search quality requires more than translating buttons and product titles. Customers use local terms, spelling, sizes and buying habits. At the same time, assortment, price, stock and rules per store must remain strictly separate.
Translation is not the same as understanding locally
A literal translation can be grammatically correct and yet not be the term customers are looking for. Product names, language, abbreviations and compound words vary by country. English loanwords are also not used in the same way everywhere.
Therefore, use real search questions per language as a source for synonyms, entity recognition and evaluation. Local search data shows which terms customers choose and where catalog language differs.
Product data by language
Titles and descriptions must be substantively consistent, but should be formulated locally of course. Brands, model codes and technical values often remain the same. Attributes such as color, material and category need a controlled translation or local mapping.
When only part of the product data has been translated, it must be clear which fields may be used for semantic matching. A mix of languages can be useful, but also cause noise when the quality varies greatly per field.
Store isolation
Each market can have a different range, different price, stock, currency and legal visibility. Retrieval must therefore take place within the right store context from the beginning. A product from another market must not end up in the results by semantic agreement.
The same insulation applies to analytics and management. Search terms and optimizations of one store are not automatically applied to another market without control.
Local sizes and units
Size systems, decimal places, currency and technical units differ. A search query with “10.5” uses a comma in many European languages, while other markets use a point. Clothing and shoe sizes often require a category and country-dependent conversion.
Normalize values for comparison, but show the customer local notation. Convert only when the relationship is reliable and prevent a global rule from showing a product in the wrong size or execution.
Multilingual semantic search
Multilingual representations can connect queries and products across languages. That helps when source data is in one language or customers use loanwords. Nevertheless, the quality must be tested by language; a model does not automatically perform equally for every market and every product category.
Lexical signals remain important for local brands, codes and specific terms. Hybrid retrieval can use different weights or glossaries per language.
Evaluate by market
- Use a local query set with popular and difficult search queries.
- Ess categories, brands, attributes and natural questions separately.
- Check zero-result searches and reformulations by language.
- Protect local golden queries with every release.
- Do not compare markets without taking into account assortment and volume.
A global improvement can cause a regression in a smaller language. Release gates should therefore contain minimums per market, not just an international average.
Central rules, local expertise
Security, permissions, release management and basic guard rails can be centrally controlled. Synonyms, category comprehension and merchandising often require local knowledge. Determine who is allowed to make changes and which parts are managed centrally or locally.
Findoviq supports separate store contexts and multilingual search optimization. This allows an organization to use economies of scale without combining local terms, assortments and evaluation into one opaque configuration.
Discuss your search questions
Do you want to know how this approach fits your assortment, product data and customer behavior? Together, we look at which query types have priority and where exact, semantic and business signals need to complement each other.