ASO Localization Priorities: A Practical Playbook for Language Selection, What to Localize First, and A/B Test Templates

Don’t translate every language because you can — pick languages using a small set of objective signals: organic traffic opportunity (store search and category volume), competitor presence, English-proficiency friction, and customer LTV/monetization likelihood in the market. These signals are faster and cheaper to measure than hopeful vanity metrics.

Practical rule: score candidate locales on four dimensions (search opportunity, competitors localized, English proficiency, and ARPU or monetization proxy). Prioritize locales with high search opportunity and low English proficiency first — they deliver the largest marginal gains for the least effort.

Tip: For initial expansion, pick 3–7 languages. Common high-priority targets for many apps are Spanish (es-ES / es-MX split depending on user base), Portuguese (pt-BR), German (de-DE), French (fr-FR), Japanese (ja-JP), Korean (ko-KR), and simplified Chinese (zh-Hans) — but always confirm with your signals and revenue potential.

Localize assets in the order that maximizes conversion lift per hour of work. The highest-impact items are app name/title (where allowed), subtitle/short description, first two screenshots (and their headline text), and the first lines of the long description or promotional text. These are the store elements most users see before the “more” fold and heavily influence tap-through and installs.

Second-tier items: keywords (where applicable, e.g., App Store), icon variants with localized microcopy, and app preview videos. Lower-priority items that still matter over time: full description, all screenshots, and in-app translations. Use this order to budget design/translation resources and to phase uploads into the console.

Production heuristic: if localizing a language costs X hours per screenshot and Y hours for translation/QA, prioritize translating copy that appears in the top 1–2 screenshots and headline fields first. For visual assets, use layered design files (Figma) and templated copy to reduce per-locale time.

Adopt simple cost-effort rules to prevent scope creep. Example rules: (A) Don’t localize more than the top two screenshots until you’ve validated lift; (B) Use professional translation plus a single in-market reviewer for top-priority strings; (C) Reuse imagery but localize overlay text and cultural hooks only when A/B tests justify it.

Tooling and workflow: keep master assets in a design system (Figma frames per locale), use string files or CSV for copy, and automate uploads with App Store Connect API and Google Play Console tooling where possible. This reduces repetitive console labor and encourages iterative experiments rather than big-bang launches.

Cost model example: assume a high-quality screenshot localization (designer + translator + QA) takes 2–4 hours per screenshot per locale the first time, then 0.5–1 hour for iterative tweaks. Those numbers make it clear why testing top screenshots first is the most cost-effective approach.

Run experiments that answer one big question at a time. For Google Play use Store Listing Experiments; for iOS use App Store Product Page Optimization or controlled creative tests where available. Below are three repeatable templates with the metric to watch and an expected ROI band (ranges reflect common industry outcomes; validate with your own traffic).

Template A — 'Localized headline test': Variant A = English screenshots and title; Variant B = localized title + localized headline on the first screenshot. Metric: conversion rate (visitors → installs). Expected uplift: 5–25% in markets with low English proficiency. Run for 1–2 app-store product page lifecycle windows or until you reach statistical confidence.

Template B — 'Cultural hook vs. direct benefits': Variant A = literal translation of US copy; Variant B = localized creative emphasizing local cultural hook (imagery, phrasing). Metric: installs per 1k impressions. Expected uplift: 3–15% where competitors haven’t localized creative. Template C — 'Short description / promo text prioritization': Variant A = product-first pitch; Variant B = benefit-first pitch with local social proof. Metric: tap-through rate. Expected uplift: 2–10%.

Benchmarks: small, focused localization experiments (title + top screenshots) commonly return install uplifts in the single- to low-double-digit percent range in prioritized markets. Larger investments (fully localized creatives + deep culturalization) are required to reach higher uplifts and are worth it when LTV per user in the locale justifies the spend.

How to judge success: tie each experiment to a dollar KPI (expected incremental LTV per install × incremental installs). If the experiment’s expected incremental spend (design + translation + A/B test administrative cost) is less than the projected incremental revenue from additional installs, scale the localization. Otherwise, iterate on creative cheaply and retest.

Operational recommendation: keep a rolling pipeline of micro-experiments. The marginal cost of running another variant using templated screenshots is small once the system is in place, but the cumulative effect of continuous improvements compounds faster than one-off massive localizations.