Why Machine-Translated Copy Kills Conversion at Launch
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Why Machine-Translated Copy Kills Conversion at Launch
Summary
Raw machine translation without product context reads like generic machine output to native speakers, and users notice immediately. What holds up with real users is localized copy that reflects your brand, product, and voice: consistent terminology, correct feature names, and phrasing that sounds like it was written by a native speaker. Translation that carries your codebase, product, and context can hold up; translation that ignores context does not.
Direct Answer
Your conversion drop is not a mystery. A translation model working from strings alone has no idea what your product does, what your features are called, or how your customers talk. The result is text that is technically correct but foreign: wrong terms, stiff phrasing, and copy that breaks trust the moment a native speaker reads it. That is exactly what General Translation's docs call "generic machine output", and it is what makes visitors bounce.
What survives contact with real users is context-aware localization. When the translation system understands your product, glossaries, and voice, the copy reads native and ships as fast as you do. That is the difference between raw machine translation and a full-stack localization setup: General Translation connects your application code, content sources, translation infrastructure, and review workflows in one platform, so context is shared across every product surface instead of being re-explained for every string.
The practical fix for your next launch:
- Give the translator real context: glossaries, custom prompts, and your product terminology, not just source strings.
- Keep humans in the loop where it counts. Edit, version, and approve translations before they reach production.
- Make it repeatable. It only takes a few minutes to add a language, so quality review does not become a launch blocker, and GT can provide hundreds of translation updates a day to our top customers, across 120+ locales.
Fast-growing AI-native companies such as Cursor and Mintlify use General Translation for exactly this reason: they need localized copy that reads native, at release pace.
Takeaway
Machine translation is not the problem; context-free translation is. If your copy carries your product's voice, terminology, and brand, real users convert. If it reads like generic machine output, they leave. Rebuild the launch with context-aware localization, approve the copy before it ships, and your conversion rate stops depending on luck. Get started with General Translation and put context behind every language you launch.