Store Listing Optimizer

An automated pipeline that generates fully keyword-optimized, natively localized App Store and Google Play listings across 86+ locales in under 30 minutes, including research in specific languages, not just a translation of the English keyword research, replacing manual ASO workflows, expensive tooling subscriptions, and translation agencies.

Launching a mobile app in multiple markets means writing store listings that rank, at first we were only translating (which was a correct level of translation and it was ok to allow the people to read our product page) but there were many locales underserved regarding ASO, like our keyword ranking was too bad. Each listing needs keyword research ( on the dedicated country with the specificities of the language!), optimized copy, and native translation not word-for-word machine translation, but per-locale keyword research that finds what users in each country actually search for.

• ASO tools (AppTweak, Sensor Tower) provide historical data but no copy generation. You still write everything manually.
• Translation services translate your English listing, but translated keywords don't rank, you need locally researched keywords per market.
• Manual research across 86 Google Play locales or 40 App Store locales is simply not feasible for a solo developer or small team.

The result: most indie apps launch with English-only listings or generic machine translations that don't rank anywhere.

A Claude Code plugin that runs a 7-phase pipeline, taking a competitor URL as input and producing a store-ready CSV with optimized listings for every supported locale.

Phase 1-3: Context Gathering. The system analyzes the app's codebase (features, strings, architecture), studies a competitor's store listing for tone/persona calibration, and brainstorms seed keywords collaboratively with the developer.

Phase 4: English Keyword Research. A 4-step research pipeline: Gemini with Google Search grounding generates data-backed keyword candidates, Google Autocomplete validates real search behavior, Apple App Store Autocomplete reveals actual queries, and cross-source scoring ranks keywords (terms appearing in both Google and Apple get a 3x confidence bonus).

Phase 5: Source Copy Generation. Optimized English copy is generated respecting platform-specific rules and the system also enforces Apple/Google content policies automatically: no superlatives ("Best", "#1"), no em dashes, no keyword stuffing, and mandatory subscription compliance blocks for iOS.

Phase 6: Per-Locale Research + Native Translation. For each of the 86 (Google Play) or 40 (App Store) locales, the system generates seed keywords in the target language (not English translations), runs local autocomplete queries, ranks keywords using locale context, translates the listing while weaving in locally-researched keywords, and enforces character limits with intelligent fixup.

The key insight: a translated keyword doesn't rank. "weight loss" translated to French gives you "perte de poids", but French users might search "maigrir" or "regime." The system researches each market independently.

The pipeline's strength is its real-time research capability, it finds what users search right now. Its weakness is lack of historical data (search volume trends, seasonal patterns, difficulty scores).

In practice, I combine the pipeline's output with historical signals from AppTweak, Apple Search Ads popularity scores, and aso.dev data. The AI handles the research and copy generation at scale; established tools provide the longitudinal context that only years of data collection can offer.

The AI-generated keyword research consistently matches or outperforms traditional ASO tools for keyword relevance, it finds keywords that tools with millions of data points also surface, plus niche long-tail terms they miss because it's searching in real-time rather than from cached databases.

We have deployed some recent tests on specific regions and we have clearly seen an improvement of impression on app store search. For a young app without review it's difficult to rank these days in the app store, the clever move is clearly to position on long tail.