Short answer: machine output drafts, people ship

Machine translation drafts an App Store listing in seconds, but you should not ship the raw output. It mistranslates keywords shoppers type, breaks thirty-character limits, and leaves screenshot captions behind. Use it for the first draft of descriptions, then run a native-speaker review on names, keywords, and screenshots before you publish. That hybrid pass costs little and converts far better.

Where machine translation breaks App Store listings

Three failure modes show up in almost every raw-output listing. First, keyword fields stop matching demand: SensorTower's team warns that Google Translate picks terms no shopper types, so a literal translation of a genre term can miss the local word players search while burning all 100 characters on dead terms. AppFollow's 2026 playbook prescribes the fix: machine-draft the candidate list, then hand it to a native speaker who swaps in slang and regional vocabulary. Second, fixed limits break: the 30-character name and subtitle have no mercy, and German compounds or French articles spill past the cap, which means the store truncates your best words. Third, screenshots stay English: translation never touches pixels, so captions, prices, and dates in images keep selling to the wrong market until someone re-renders each shot at 1284x2778 or 2048x2732. OneSky's data frames the stakes: 72.4 percent of shoppers prefer their own language and localized keywords can lift downloads sevenfold, which is exactly the lift raw output leaves behind.

These breaks cost downloads rather than triggering rejections. Apple reviews listings for policy compliance, and a clumsy translation still passes review while quietly converting worse in every storefront it touches. The keyword field hurts most because it's invisible: shoppers never see it, so nobody reports the damage, and all 100 characters can sit on terms with zero searches while your best terms sit out. Our keyword-field guide shows how to pack the field with terms shoppers type. The name and subtitle hurt loudest: truncation shows on the product page, and a cut-off name reads as carelessness before a single screenshot loads.

Screenshots fail silently too. Translation tools process text, and a screenshot is a picture of text, so every caption, price badge, and date in your images keeps speaking English after the metadata ships. Shoppers read images before they read a word of copy, which puts untranslated screenshots at the top of the damage list. The fix is re-rendering: fresh captions set in the new language, layout reflowed so longer words fit, exports at store sizes. Our screenshot-tools buyer guide compares the three ways teams produce them.

Where machine translation is good enough

The description is the safest place to start. It runs 300 to 600 words with no hard character cap, so small awkwardness costs little while the message still lands. OneSky's minimal-viable-localization idea rests on this fact: translate the store description alone and you can test a market for 30 to 80 dollars per locale through humans, or nearly zero through MT, before you touch screenshots or keywords. Google's own support page backs the middle ground, noting MT gives good results for common topics while recommending human translation for the store listing itself. Treat the description as your MT proving ground.

Release notes come next. Shoppers skim them, search engines barely index them, and each version needs fresh words, which makes paying full human rates every two weeks painful. Draft them by machine, skim them yourself against the English source, and ship. Keyword research also starts with MT in most pro workflows: AppFollow's 2026 playbook runs candidate terms through auto-translation first, then hands the list to a native speaker who swaps in slang and regional vocabulary. The machine proposes volume. The human picks winners. Our whats-new guide covers the release-notes layer per locale.

The math explains why teams start here. Machine translation costs a fraction of a cent per word, so a 700-word listing drafts for under a dollar. Human translation at 10 to 30 cents per word puts the same text at 70 to 210 dollars per locale before review. One native review pass over a listing this size runs 30 to 60 dollars per locale at proofreading rates. MT plus review lands near 60 dollars per locale against 200-plus for full human translation, which funds three locales for the price of one. Our cost guide runs the full per-locale totals with screenshots included.

The review pass that catches what machines miss

Review in field order, not reading order. The fields that break rankings and conversion go first, since a team with one paid hour should spend it where MT damage costs most. Work top to bottom through this list and stop when the budget runs out. Each step states what to check and who should check it.

  1. Keywords first, by a native speaker. Hand them the English terms plus the MT candidates and ask which terms shoppers actually type. Replace dead terms, keep the field at 100 characters with no spaces wasted on duplicates. Nothing else in the pass moves rankings this much.
  2. Name and subtitle second, by anyone fluent. Count every character including spaces against the 30-character caps. German compounds and French articles spill first. Shorten by meaning, not by chopping endings, and read the result aloud.
  3. Screenshots third, on device. Proof each caption at full size for spill, check prices and dates use local formats, and confirm no English words survive in badges. Re-render and re-export at store sizes before upload.
  4. Description fourth, by skimming. Read the MT draft against your English source for flipped meaning, wrong product terms, and tone breaks. Fix meaning first and let small awkwardness stand. This field forgives most.
  5. Upload through App Store Connect last, one locale at a time. Apple's model splits app-level and version-level fields, so confirm each locale shows the right text before you submit the version. Our Connect setup guide walks the screens.

Teams that run this pass ship listings that read as native where it counts and merely adequate where it pays. That split is the whole game at indie budgets. Our one-afternoon listing guide folds the pass into a single sitting from draft to upload.

Machine translation and App Store keywords need care

The keyword field deserves its own warning because MT fails it in a way no reader can spot. Shoppers type slang, abbreviations, and local genre words that dictionaries never list. A machine maps your English term to its textbook equivalent, which can be a word with zero searches in the target storefront. SensorTower's keyword team flags exactly this: translated terms that match no demand. Since the field is invisible on the product page, the mistake sits there for months, spending all 100 characters on silence. False friends make it worse: a word that looks right but means something adjacent can pull the wrong shoppers and sink conversion.

Run keywords as a separate mini-workflow. Draft your English core terms from the words buyers use, not your internal feature names. Machine-translate them into candidates, then pay a native speaker for one focused hour to mark which candidates carry demand and add the slang you missed. Check the survivors against our keyword-field guide before you ship, and revisit the field after six weeks of impressions per locale. Our strategy guide shows how to sequence that measurement across waves.

How App Store Localizer fits a machine workflow

App Store Localizer (https://asolocalization.com) fits the draft stage of this workflow. Paste a public App Store URL. The tool pulls your title, description, icon, and screenshots through Apple's public lookup API. It translates the title, subtitle, description, and the 100-character keyword field per target locale. It drafts localized keywords from free heuristics over your listing. It re-renders your screenshots with translated captions in iPhone 14 Plus 1284x2778 and iPad Pro 12.9-inch 2048x2732 sizes. Coverage runs to 20-plus target locales, from Japanese, Korean, and both Chinese scripts through German, French, Spanish, and Brazilian Portuguese down to Hindi, Thai, and Vietnamese. You download the finished assets or publish straight to App Store Connect.

Prices are one-time per listing, which keeps the hybrid math friendly. Starter at $19.99 covers 3 languages with each available screenshot. Global at $99.99 covers all supported languages. No subscription, no per-seat fees. Two honest limits hold. Output is machine-generated, so run the native review pass from this guide on your top locales before you ship, starting with keywords, name, and screenshots. Keyword ideas come from listing heuristics rather than paid search-volume data, so confirm your top market's terms with a native speaker. The tool serves the store listing only, never app screens or code, so pair it with string cover before you chase markets where the UI must be native.

Frequently asked questions

  • Can I use Google Translate for my App Store description? Yes as a first draft, not as the shipped copy. Google's own support page says machine translation gives good results for common topics yet recommends human translation for the store listing itself. Draft the description by machine, then review names, keywords, and screenshots with a native speaker before publishing.
  • Can I rely solely on machine translation for my app localization? For the listing layer, raw MT alone leaves rankings and conversion behind: mistranslated keywords, truncated names, English screenshots. Translation agencies take the strict line and rule it out. The indie answer is MT plus scoped review, which keeps most of the quality at a fraction of the cost.
  • Can AI tools handle App Store translation? For drafts, yes, and one automation study measured AI-only listings at about 60 percent of the conversion lift of human-localized ones. That gap is the review pass: AI drafts the words in seconds, a native speaker fixes demand mismatch and limits, and the hybrid ships close to full quality.
  • Will Apple reject my app over a machine-translated listing? Apple reviews each version for policy compliance, and poor translation quality is not a rejection reason they publish. The risk runs the other way: a clumsy listing passes review and converts poorly in every storefront it touches. Review for conversion, not for approval.
  • Should I machine-translate my keyword field? Draft it by machine and finalize it with a native speaker. The field holds 100 characters nobody sees, so dead terms waste the whole budget invisibly. Ask a speaker which candidates shoppers type, add local slang, and recheck after six weeks of per-locale impressions.

Paste your App Store URL at https://asolocalization.com today and draft every locale this afternoon. Then spend your review budget where this guide points: keywords first, name and subtitle second, screenshots third. Ship the listing, read installs per page view by market in six weeks, and fund full human translation only where the numbers earn it.