Website localisation and SEO are usually handled by separate teams that don’t communicate closely, which leads to content that’s translated correctly but doesn’t perform and often has to be redone. This sets out a model built around content analysts trained in both disciplines, working from strategic roadmaps that are updated on a regular cycle rather than treated as a one-off translation job.
Different methodologies, from machine translation and post-editing through to full localisation, are matched to budget, timeline and business objective, since picking on price alone tends to produce a website that ranks and performs badly. The same approach is applied to Amazon, covering keyword research, listing optimisation and ongoing testing, treating it as its own search environment distinct from Google.
Four practical differences are set out: researching keywords independently by market rather than translating English terms, investing in training a smaller group of linguists in SEO rather than relying on large freelancer pools, requiring technical understanding of both SEO and language in the same team, and measuring the impact of linguistic changes against commercial KPIs. Supporting technology, including a project management platform, translation memory and a centralised term database, is covered alongside case studies from Aman Resorts and Eventbrite showing traffic and conversion gains from this combined approach.
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