Machine vs human translation for localized pages: the SEO tradeoff

Machine vs human translation for localized pages: the SEO tradeoff

The translation method behind your locales is an SEO decision, not just a content one. We compared raw machine output, post-edited machine, and full human localization across indexed multilingual sites.

Findings:

— Raw machine translation at scale risks being treated as auto-generated, low-value content. We have seen large auto-translated sections fail to gain traction even with perfect hreflang. The technical signals were flawless; the content earned nothing.
— Post-edited machine translation (a human passes over MT output) is the pragmatic middle. It localizes idiom and search intent — 'sneakers' vs 'trainers', local query phrasing — which pure MT misses and which directly affects whether you match real searches.
— Full human localization wins on quality but rarely scales to 30 locales economically.

The data suggests the decisive factor is not translation fidelity but search-intent localization. A grammatically perfect translation that uses the wrong regional term for a product will not rank, because nobody searches the translated phrase.

Our tactic: machine-translate to draft, then human-edit specifically for local query language and currency/units, not just grammar. Caveat — Google has softened its stance on quality translation regardless of method; the risk is in unedited, intent-blind output, not in machine assistance per se.