Localization Analysis Engine

The Localization Analysis Engine inspects your store listing across 22 commercially significant languages and scores your localization coverage. It fetches the actual store listing description in each language, compares it against your English baseline, and identifies which markets are running on the English default.

Localization Is the Biggest Untapped Growth Lever in ASO

The App Store and Google Play together support 88 languages across roughly 200 regions. That is 200 separate keyword economies, each with its own search behavior, its own competitive landscape, and its own conversion dynamics. A user searching for “budget planner” in English, “Haushaltsbuch” in German, and “controle de gastos” in Portuguese is looking for the same app, but stores treat those as three independent discovery paths.

Most apps ignore almost all of them. The pattern is remarkably consistent across app audits: a team launches with an English listing, maybe adds Spanish and French if someone on the team speaks those languages, and leaves every other market running on the English default. The store technically serves the listing in those regions, but search indexing and conversion suffer because the metadata is in the wrong language for local users.

Japan is the third-largest app market globally. Brazil, Germany, South Korea, and Indonesia each represent hundreds of millions of potential users. Hindi alone has over 600 million speakers. Every unlocalized market is a market where your app is invisible in local-language searches and where your listing conversion rate is a fraction of what it could be, because users scanning through search results will skip a listing they cannot read in their own language.

Before you can build a localization strategy, you need to know where you stand right now. You need to see which of the commercially important languages your store listing actually covers and which ones are still running on the English fallback. That baseline audit is what the Localization Analysis Engine provides.

Under the Hood

The Localization Analysis Engine inspects 22 commercially significant languages for your app, fetches the actual store listing description in each language, compares every one against your English baseline, and scores your overall coverage. Here is what each step produces.

Baseline Establishment

The problem: You need a reference point to determine whether a given language has a truly localized description or is just serving the English default. Stores sometimes return the original-language listing when no localization exists, making it ambiguous whether a market is actually covered.

How the engine handles it: The engine starts by fetching your English (U.S.) store listing description through the appropriate store adapter (App Store Connect for iOS, Google Play for Android). It records the full text and uses it as the baseline reference. Every subsequent language comparison checks whether the fetched description matches this baseline or contains distinct content. Under the hood, the comparison uses content hashing to make this determination precise and resistant to minor formatting differences.

What you get: A verified English baseline used as the reference for all 21 remaining language comparisons.

Multi-Language Coverage Scan

The problem: Checking localization status by hand means navigating to your store page in each language configuration, one at a time, and visually comparing the text. For 22 languages, that is 22 page loads and 22 manual comparisons per app, per storefront.

How the engine handles it: The engine fetches descriptions for all 21 non-English languages in parallel, staying within store API rate limits. Each fetch targets the specific language and region codes for that market. For App Store, this uses Apple’s language codes (e.g., de-DE, ja, ko). For Google Play, it uses Play Console’s language and country codes (e.g., de-DE / de, ja-JP / jp).

The 22 inspected languages cover the highest-value app markets globally: English (U.S.), German, French, Spanish, Italian, Portuguese (Brazil), Japanese, Korean, Chinese Simplified, Chinese Traditional, Russian, Arabic, Dutch, Swedish, Norwegian, Danish, Polish, Turkish, Thai, Hindi, Indonesian, and Vietnamese.

For each language, the engine compares the fetched description against the baseline. If the text differs from the English version, the language is marked as localized. If the text is identical, the language is marked as not localized. If the fetch fails (some languages are not supported for all apps on all storefronts), that language is flagged separately so it does not distort the coverage score.

Because the engine runs per-storefront, it surfaces cases where an app is localized into German on iOS but not on Android, or vice versa. That kind of cross-platform inconsistency is nearly impossible to catch manually across a full portfolio.

What you get: A per-language coverage table showing each of the 22 languages with its localization status: localized, not localized, or fetch failed. This is the core deliverable of the engine.

Coverage Scoring

The problem: A list of localized versus not-localized languages is useful, but teams need a single metric to track localization health over time and compare across apps in a portfolio.

How the engine handles it: The engine computes a coverage percentage based on successfully fetched languages only. If 22 languages are inspected, 20 fetches succeed, and 14 of those 20 have unique localized descriptions, the coverage score is 70%. Failed fetches are excluded from the denominator so they do not penalize your score for store-level data availability issues.

The coverage percentage also serves as the engine’s overall score (0-100), which feeds into the audit’s ASO Readiness Score alongside scores from all other engines.

What you get: A coverage percentage (0-100) representing the share of successfully inspected languages where your listing has a unique localized description. This score appears in the audit summary alongside scores from the Keyword Engine, Store Text Engine, and every other subengine.

Targeted Recommendations

The problem: Knowing which languages are not localized is step one. Knowing what to do about each gap is what makes the information actionable.

How the engine handles it: For every language that was successfully fetched but found to be not localized, the engine generates a specific recommendation: localize your store listing description into that language to reach more users in that market. Each recommendation includes the language name and code, estimated effort (medium), and a clear classification: this is something you can act on directly in your store listing, with no code changes or app updates required. All recommendations carry the same priority level, giving teams a complete punch list rather than a partial ranking.

What you get: A list of specific localization recommendations, one per unlocalized language. Each recommendation names the exact language and market, the expected impact (market access), and the work type. Teams can hand this list directly to a localization vendor or use it to scope internal translation sprints.

From Diagnosis to Execution

The coverage audit tells you which markets you are missing. The next step is producing localized metadata for those markets. Apptonomy’s Content Engine and localization workflow can generate store listing text in all 22 inspected languages, turning the Localization Analysis Engine’s gap list into publishable metadata within the same platform. The path from “you are missing Japanese” to “here is your Japanese listing ready for review” stays inside a single workflow.

Coverage Measures Presence, Not Quality

One important scope note: the coverage percentage measures presence of a localized description, not quality. A listing could have a machine-translated description from three years ago and still register as “localized.” The Localization Analysis Engine answers the first question: do you have a localized description at all? Assessing whether that description is high quality, keyword-optimized, and conversion-ready is the job of other engines in the audit (Store Text Engine, Keyword Engine) when run against each locale.

Related Engines

• Store Text Engine — analyzes store text quality per locale
• Keyword Engine — provides per-locale keyword analysis
• Intent Engine — intent coverage varies across markets