---
title: "Which locales to localize first"
description: "A data-driven prioritization method: rank markets by un-localized traction and language sensitivity against honest per-locale costs, then ship in readable waves."
excerpt: "Localize where you already have traction you are under-serving, at a pace you can maintain forever — a locale you will abandon should not launch."
source_url: "https://appstorehelper.com/guides/locale-prioritization-app-store"
mirror_url: "https://appstorehelper.com/mirror/guides/locale-prioritization-app-store"
section: "Editorial guides for app-store growth teams"
locale: "en"
published_at: "2026-07-30"
updated_at: "2026-07-30"
reading_time: "6 min read"
tags:
  - "locale prioritization"
  - "localization strategy"
  - "market expansion"
  - "ASO"
---

## Direct answer

"Which locales should we localize first" has a data-driven answer most teams already possess: rank markets by the revenue or install volume you already get from them un-localized, weighted by how much localization typically moves the needle there, against the real cost of doing that locale well. Existing un-localized traction is the strongest signal available — a market sending installs through an English listing is announcing demand that localization will amplify. Second-tier signals: store search volume for your category in that language, competitor localization gaps (a market where rivals are English-only is cheap to win), and support or review activity in that language. The wrong way to prioritize is by population, by GDP, or by "everyone localizes into these five languages" — those rank the world, not your opportunity.

## Prioritization inputs

| Signal | What it tells you | Where to find it |
| --- | --- | --- |
| Un-localized installs/revenue by storefront | Demand that already found you | Store analytics by territory |
| Conversion rate gap vs home market | How much the language barrier costs | Store analytics, same report |
| Category search volume in the language | Ceiling for discovery growth | Keyword tools, store autocomplete |
| Competitor localization gaps | Markets that are cheap to win | Manual review of top rivals per market |
| Reviews/support requests in the language | Users already pushing through friction | Review console, support inbox |

## Recommended flow

### 1. Pull territory data before opinions

One report — installs, revenue, and listing conversion by storefront for the last two quarters — settles most prioritization debates before they start. Markets with traction and below-average conversion are your shortlist.

### 2. Estimate lift per market, not in general

Localization moves metrics differently by market: some storefronts convert dramatically better in the local language, others tolerate English listings. Local-language competitors' presence is a fair proxy — where they dominate, the language matters.

### 3. Cost each locale honestly

A locale costs research, rebuilt metadata, screenshot text production, native review, and — the part everyone forgets — permanent maintenance on every future release. A locale you cannot maintain is a liability with a launch date.

### 4. Sequence in waves, not batches

Ship one to three locales per wave, read results for a cycle, then commit the next wave. The first wave teaches you your real cost-per-locale and lift-per-market numbers; batch launches of ten locales teach you nothing until it is too late to adjust.

### 5. Define the exit as well as the entry

Decide what results justify the next wave — and what results should pause the program. A locale that shows no lift after two release cycles with complete assets is evidence worth respecting, not a reason to localize harder.

## Common failure modes

### Prioritizing by language size

Choosing locales by speaker population produces the same five languages for every product regardless of category, competition, or existing traction. Your data outranks demographics.

### The half-committed locale

Metadata localized, screenshots not, keyword field translated rather than researched. Half-locales spend the budget and produce results that read as "localization doesn't work here," poisoning the next prioritization round.

### Ignoring maintenance in the cost

Each live locale adds to every future release. Teams that localize into eight languages in one quarter often stop updating five of them by the next — worse than never launching, since staleness reads as neglect.

## Locale prioritization checklist

1. Territory report pulled: installs, revenue, conversion by storefront.
2. Shortlist ranked by existing traction × estimated language sensitivity.
3. Full cost per locale computed, including per-release maintenance.
4. Waves defined: one to three locales, read cycle, then next wave.
5. Entry and exit criteria written before the first wave ships.
6. Every launched locale is complete — no metadata-only launches.

## Operating rule

Localize where you already have traction you are under-serving, at a pace you can maintain forever. A locale you will abandon is a locale you should not launch.

## Why this matters in App Store Helper

App Store Helper makes each wave operationally cheap: new locales inherit the project's positioning, keyword structure, and screenshot recipes, and the per-locale review state shows exactly what is complete before launch. Adding a locale becomes a production task, not a re-derivation of strategy.