# App Store Keyword Discovery Scraper (`automation-lab/app-store-keyword-discovery-scraper`) Actor Discover ranked Apple App Store apps by keyword and country for ASO research, competitor tracking, and market discovery. - **URL**: https://apify.com/automation-lab/app-store-keyword-discovery-scraper.md - **Developed by:** [Stas Persiianenko](https://apify.com/automation-lab) (community) - **Categories:** Marketing, SEO tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per event This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## App Store Keyword Discovery Scraper Discover ranked Apple App Store search results for seed keywords across countries. Use this actor to collect ASO keyword discovery data, competitor app rankings, ratings, pricing, genres, and keyword candidates from public Apple/iTunes search metadata. ### What does App Store Keyword Discovery Scraper do? App Store Keyword Discovery Scraper searches the public Apple App Store search API for your seed keywords. It returns ranked apps for each keyword and country combination. Each dataset row represents one app result in one localized App Store search. You can use it to understand which apps rank for a keyword, which categories dominate a niche, and which words appear repeatedly in winning app names and genres. ### Who is it for? ASO marketers use it to build keyword research spreadsheets. App publishers use it to monitor where competitors appear in country-specific search results. Mobile agencies use it to compare client app categories across markets. Growth teams use it to find app ideas, niche competitors, and localized ranking opportunities. Analysts use it to enrich dashboards with ratings, prices, genres, and developer metadata. ### Why use this actor? ⚡ It is HTTP-only and does not need a browser. 🌍 It supports multiple App Store countries in one run. 🔎 It starts from keywords instead of requiring known app IDs. 📊 It outputs ranking, app, developer, pricing, rating, and genre fields. 🧩 It is easy to connect to Sheets, BI tools, databases, and ASO workflows. ### What data can you extract? | Field | Description | | --- | --- | | `keyword` | Seed keyword searched | | `country` | App Store country code | | `rank` | Result position for that keyword/country | | `appName` | App Store app name | | `appId` | Apple numeric app ID | | `bundleId` | iOS bundle identifier when Apple returns it | | `developer` | Seller or developer name | | `primaryGenre` | Main App Store genre | | `genres` | Full genre list | | `price` | Numeric app price | | `currency` | Price currency | | `averageRating` | Average user rating | | `ratingCount` | Number of ratings | | `keywordCandidates` | Candidate words/phrases derived from app names and genres | | `appStoreUrl` | Public App Store URL | | `scrapedAt` | Timestamp for the scrape | ### How much does it cost to discover App Store keywords? This actor uses pay-per-event pricing. There is a small start event and a per-result event for every ranked app saved. Final tiered pricing is set after cloud cost validation. For typical ASO keyword research, start with 2-5 seed keywords, 1-3 countries, and 25-50 results per keyword. That gives enough signal while keeping the first run inexpensive. ### Input options #### `keywords` Seed App Store search terms. Examples: - `fitness tracker` - `meditation` - `budget planner` - `meal planner` - `habit tracker` #### `countries` Two-letter App Store country codes. Examples: - `us` - `gb` - `ca` - `au` - `de` - `fr` #### `language` Apple Search API language parameter. Default is `en_us`. Use localized values such as `de_de` or `fr_fr` when analyzing non-English markets. #### `maxResultsPerKeyword` Maximum apps saved for each keyword and country pair. The actor supports up to 200 results per request when Apple returns that many. #### `includeDescriptions` Turn this on if you need app description copy for ASO text analysis. Leave it off for smaller exports and faster downstream processing. #### `requestDelayMs` Delay between Apple API calls. The default is conservative and suitable for normal batches. ### Example input ```json { "keywords": ["fitness tracker", "meditation"], "countries": ["us", "gb"], "language": "en_us", "maxResultsPerKeyword": 25, "includeDescriptions": false, "requestDelayMs": 250 } ```` ### Example output ```json { "keyword": "fitness tracker", "country": "us", "language": "en_us", "rank": 1, "appName": "Example Fitness App", "appId": 123456789, "bundleId": "com.example.fitness", "developer": "Example Developer", "primaryGenre": "Health & Fitness", "averageRating": 4.8, "ratingCount": 120000, "keywordCandidates": ["fitness", "health fitness"], "appStoreUrl": "https://apps.apple.com/...", "scrapedAt": "2026-06-15T00:00:00.000Z" } ``` ### How to run it 1. Open the actor on Apify. 2. Enter one or more seed keywords. 3. Choose App Store countries. 4. Set the maximum apps per keyword. 5. Run the actor. 6. Export the dataset as JSON, CSV, Excel, or via API. ### ASO workflow ideas Use the output to build keyword maps. Group apps by primary genre. Compare rank differences between countries. Find competitor developers that appear repeatedly. Extract candidate words from winning app names. Combine ratings and review counts to prioritize high-value niches. Track the same keywords weekly to observe ranking changes. ### Country comparison workflow Run the same keywords across several countries. Pivot the dataset by `keyword`, `country`, and `rank`. Look for apps that rank well in one market but not another. Use those gaps to discover localization opportunities. ### Competitor monitoring workflow Search phrases that matter to your app. Filter by a competitor app name or developer. Store repeated runs in a database. Chart rank movement over time. ### Tips for better results Start with broad seed phrases. Then use `keywordCandidates` to create follow-up keyword lists. Use localized country and language pairs. Keep descriptions disabled unless you need text mining. Use 25-50 results for exploration and higher limits for final research exports. ### Integrations Send results to Google Sheets for ASO planning. Load results into BigQuery or Snowflake for trend analysis. Connect the dataset to a dashboard to monitor competitor rankings. Use webhooks to trigger weekly keyword checks. Combine with app detail or review scrapers for deeper App Store intelligence. ### API usage with Node.js ```js import { ApifyClient } from 'apify-client'; const client = new ApifyClient({ token: process.env.APIFY_TOKEN }); const run = await client.actor('automation-lab/app-store-keyword-discovery-scraper').call({ keywords: ['fitness tracker'], countries: ['us'], maxResultsPerKeyword: 25 }); console.log(run.defaultDatasetId); ``` ### API usage with Python ```python from apify_client import ApifyClient client = ApifyClient('YOUR_APIFY_TOKEN') run = client.actor('automation-lab/app-store-keyword-discovery-scraper').call(run_input={ 'keywords': ['fitness tracker'], 'countries': ['us'], 'maxResultsPerKeyword': 25, }) print(run['defaultDatasetId']) ``` ### API usage with cURL ```bash curl "https://api.apify.com/v2/acts/automation-lab~app-store-keyword-discovery-scraper/runs?token=$APIFY_TOKEN" \ -H 'Content-Type: application/json' \ -d '{"keywords":["fitness tracker"],"countries":["us"],"maxResultsPerKeyword":25}' ``` ### MCP usage Use this actor from Claude Desktop or Claude Code through the Apify MCP server. MCP URL: `https://mcp.apify.com/?tools=automation-lab/app-store-keyword-discovery-scraper` Claude Code setup: ```bash claude mcp add apify-app-store-keywords https://mcp.apify.com/?tools=automation-lab/app-store-keyword-discovery-scraper ``` Claude Desktop JSON configuration: ```json { "mcpServers": { "apify-app-store-keywords": { "url": "https://mcp.apify.com/?tools=automation-lab/app-store-keyword-discovery-scraper" } } } ``` Example prompts: - "Find the top App Store apps for meditation in the US and UK." - "Compare fitness tracker search results across US, Canada, and Australia." - "Run App Store keyword discovery for meal planner and summarize high-rating competitors." ### Data freshness Apple search results can change frequently. For monitoring, schedule recurring runs with the same input. For one-off research, export the timestamped dataset and keep the `scrapedAt` field. ### Limits and caveats The actor uses public Apple search metadata. Apple may return fewer results than requested for niche keywords. Some metadata fields are unavailable for some apps or countries. Rank means the order returned by Apple for the query at run time. ### Legality This actor collects publicly available app search metadata from Apple endpoints. You should use the data responsibly, respect applicable laws, and avoid collecting personal data. If you have legal obligations for your project, consult your own counsel. ### FAQ #### Why did I get fewer apps than requested? Apple may not have enough matching apps for a narrow keyword, country, or language combination. Try a broader seed keyword or another country. #### Why are some ratings or prices empty? Apple does not return every field for every app in every market. Empty values mean the public API did not provide that field for that result. #### How do I reduce dataset size? Lower `maxResultsPerKeyword`, use fewer countries, and leave `includeDescriptions` disabled. ### Related scrapers Use `https://apify.com/automation-lab/apple-app-store-scraper` when you already know app IDs or app URLs. Use `https://apify.com/automation-lab/apple-app-store-reviews-scraper` when you need review text for known apps. Use this actor when you need keyword-first App Store discovery. ### Changelog Initial version searches public Apple App Store metadata by keyword and country. ### Support If a run fails or you need a field added, open an Apify issue with your run URL and expected output. # Actor input Schema ## `keywords` (type: `array`): Apple App Store search terms to analyze, such as app categories, competitor themes, or ASO seed phrases. ## `countries` (type: `array`): Two-letter App Store country codes to search. Use multiple countries to compare localized rankings. ## `language` (type: `string`): Apple Search API language parameter, for example en\_us, en\_gb, de\_de, or fr\_fr. ## `maxResultsPerKeyword` (type: `integer`): How many ranked App Store apps to save for each keyword and country combination. ## `includeDescriptions` (type: `boolean`): Save long App Store descriptions. Turn off for smaller datasets and faster exports. ## `requestDelayMs` (type: `integer`): Politeness delay between keyword/country API calls. Increase if you run very large batches. ## Actor input object example ```json { "keywords": [ "fitness tracker", "meditation" ], "countries": [ "us", "gb" ], "language": "en_us", "maxResultsPerKeyword": 20, "includeDescriptions": false, "requestDelayMs": 250 } ``` # Actor output Schema ## `overview` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "keywords": [ "fitness tracker", "meditation" ], "countries": [ "us", "gb" ], "language": "en_us", "maxResultsPerKeyword": 20 }; // Run the Actor and wait for it to finish const run = await client.actor("automation-lab/app-store-keyword-discovery-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "keywords": [ "fitness tracker", "meditation", ], "countries": [ "us", "gb", ], "language": "en_us", "maxResultsPerKeyword": 20, } # Run the Actor and wait for it to finish run = client.actor("automation-lab/app-store-keyword-discovery-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "keywords": [ "fitness tracker", "meditation" ], "countries": [ "us", "gb" ], "language": "en_us", "maxResultsPerKeyword": 20 }' | apify call automation-lab/app-store-keyword-discovery-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=automation-lab/app-store-keyword-discovery-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "App Store Keyword Discovery Scraper", "description": "Discover ranked Apple App Store apps by keyword and country for ASO research, competitor tracking, and market discovery.", "version": "0.1", "x-build-id": "ScQkGiAUcJjH1vme4" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/automation-lab~app-store-keyword-discovery-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-automation-lab-app-store-keyword-discovery-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/automation-lab~app-store-keyword-discovery-scraper/runs": { "post": { "operationId": "runs-sync-automation-lab-app-store-keyword-discovery-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/automation-lab~app-store-keyword-discovery-scraper/run-sync": { "post": { "operationId": "run-sync-automation-lab-app-store-keyword-discovery-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "keywords" ], "properties": { "keywords": { "title": "Seed keywords", "type": "array", "description": "Apple App Store search terms to analyze, such as app categories, competitor themes, or ASO seed phrases.", "items": { "type": "string" } }, "countries": { "title": "Countries", "type": "array", "description": "Two-letter App Store country codes to search. Use multiple countries to compare localized rankings.", "default": [ "us" ], "items": { "type": "string" } }, "language": { "title": "Language", "type": "string", "description": "Apple Search API language parameter, for example en_us, en_gb, de_de, or fr_fr.", "default": "en_us" }, "maxResultsPerKeyword": { "title": "Maximum apps per keyword/country", "minimum": 1, "maximum": 200, "type": "integer", "description": "How many ranked App Store apps to save for each keyword and country combination.", "default": 20 }, "includeDescriptions": { "title": "Include app descriptions", "type": "boolean", "description": "Save long App Store descriptions. Turn off for smaller datasets and faster exports.", "default": false }, "requestDelayMs": { "title": "Delay between Apple requests (ms)", "minimum": 0, "maximum": 10000, "type": "integer", "description": "Politeness delay between keyword/country API calls. Increase if you run very large batches.", "default": 250 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```