Inside the Audit (12/12): Why Most Apps Leave 21 Markets on the Table

Apptonomy is an ASO intelligence and execution platform. Paste an App Store or Google Play URL, and the platform runs a full audit across multiple specialized engines, delivering scored findings and prioritized recommendations in minutes. For the full picture of how an audit works, read What You Get From an Apptonomy Audit.

The Orchestrator

Behind every audit is the Audit Engine, an orchestrator that spins up each specialized subengine in parallel and synthesizes their findings into a single unified report with an ASO Readiness Score (0-100). The current subengines:

Keyword Engine
Store Text Engine
Screenshot Engine
Icon Engine
Sentiment Engine
Competitor Discovery Engine
Policy Checker
Content Engine
AI Discovery Engine
Search Term Engine
Intent Engine
L11n (Localization) Analysis Engine

This post covers the Localization Analysis Engine and what happens when it evaluates your localization coverage.

The ASO Problem: 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 hundreds of 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.

The scale of the missed opportunity is hard to overstate. 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. If you built a fitness app doing well in the U.S., there are users in Japan, Brazil, and Germany searching for exactly what your app does in their own language. Without a localized listing, your app does not appear in those searches.

The problem scales with every app you add. Managing 30 apps and localizing each into even 10 additional languages would meaningfully move install numbers. But manually checking which languages each app already covers, across both iOS and Android, means opening store pages in dozens of language configurations per app. For a 30-app portfolio across 22 languages, that is over 1,300 manual page loads and visual comparisons per audit cycle. Nobody does this consistently because nobody can. The information just does not exist in most ASO dashboards.

Our position: 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, Screenshot 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, available and being refined with early users, can generate store listing text in all 22 inspected languages. That turns the Localization Analysis Engine’s gap list into publishable metadata within the same platform, so the path from “you are missing Japanese” to “here is your Japanese listing ready for review” stays inside a single workflow.

Bringing It Together

Localization is the largest structural growth opportunity most apps never act on. Not because it is technically difficult, but because teams lack visibility into their current coverage. You cannot prioritize markets you do not know you are missing.

The Localization Analysis Engine provides that visibility. It inspects 22 commercially significant languages, compares each against your English baseline, scores your overall coverage, and generates specific recommendations for every gap. The engine is fully deterministic: the coverage baseline it produces is reproducible and auditable every time.

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.

Run the Localization Analysis Engine across a 30-app portfolio and discover that 22 of those apps have coverage below 30%. That single data point reframes the next quarter’s localization priorities. The same scan gives a single metric for localization health per app, per storefront, visible in seconds instead of buried in spreadsheets.

The compounding value comes from running this alongside every audit. As teams localize into new markets, the coverage score increases and the recommendation list shrinks. Building this kind of cross-language, cross-storefront coverage intelligence manually, across 22 languages and both platforms, for every app in a portfolio, is work that almost nobody does consistently. The engine runs it in seconds.

See your localization coverage score in two minutes Paste your App Store or Google Play URL at apptonomy.ai and see exactly which of 22 major languages your listing covers and which ones you are missing. No signup required.