# ASO Keyword Rank Tracker β€” App Store & Google Play (`slothtechlabs/aso-keyword-rank-tracker`) Actor Track keyword rankings, find top apps per keyword, and discover related keywords across Apple App Store and Google Play (60+ storefronts). Pay-per-use, no subscription. Flat CSV output. The affordable AppTweak / Sensor Tower alternative β€” same data at a fraction of the cost. - **URL**: https://apify.com/slothtechlabs/aso-keyword-rank-tracker.md - **Developed by:** [SlothTechLabs](https://apify.com/slothtechlabs) (community) - **Categories:** SEO tools, Developer tools, Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing $1.00 / 1,000 result rows This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. 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 ## πŸ” ASO Keyword Rank Tracker β€” App Store & Google Play > **Clean ASO data, delivered as a spreadsheet.** One Actor, three actions, both stores, pay per use. Tired of paying **$83+/month to AppTweak** or **$179+/month to AppFollow** just to check whether your app still ranks for your target keywords? This Actor gives you the same kind of data β€” cross-platform (iOS + Android), 60 storefronts, **pay-per-use** β€” as flat CSV rows that open cleanly in Excel / Google Sheets / Looker. **Under 20 seconds** from clicking Start to having sortable ASO data you can pivot, filter, and plot. You get the **real data** (top apps, review counts, ratings, prices, release dates), **raw aggregates** (total results, top-10 review-count median, etc.), and **three convenience estimates** (`popularityEstimate`, `difficultyEstimate`, `opportunityEstimate`) so you can prioritize keywords with one sort. No dashboards, no lock-in. --- ### πŸ“Š Sample output (CSV-friendly) Running with the default input (2 keywords Γ— both stores, top 10 each) produces rows like this: | keyword | store | appRank | appName | rating | ratingCount | price | genre | releaseDate | popularityΒ­Estimate | difficultyΒ­Estimate | opportunityΒ­Estimate | |---|---|---|---|---|---|---|---|---|---|---|---| | fitness app | apple | 1 | Fitness App (Trainerize) | 4.9 | 62,559 | Free | Health & Fitness | 2012-04-12 | 87 | 84 | 14 | | fitness app | apple | 2 | Apple Fitness | 2.9 | 10,796 | Free | Health & Fitness | 2020-09-30 | 87 | 84 | 14 | | fitness app | google | 1 | Home Workout β€” No Equipment | 4.8 | 3,895,736 | Free | Health & Fitness | 2017-11-08 | 98 | 91 | 9 | | meditation | apple | 1 | Insight Timer | 4.9 | 437,965 | Free | Health & Fitness | 2009-11-05 | 87 | 86 | 12 | (plus `iconUrl` rendered as thumbnail, `appId`, `developer`, 4 raw aggregate columns, `url`, `storefront`, `scrapedAt`, `action` β€” 23 columns in total.) 40 rows per prefill (2 Γ— 2 Γ— 10). **Sort by `opportunityEstimate` desc to surface your best targets; filter by `store`; pivot by `keyword`** β€” no JSON parsing required. --- ### 🎯 Who this is for - πŸ§‘β€πŸ’» **Indie developers** β€” track your app's rank for 20 keywords daily, both stores, for **~$1.20/month** - 🏒 **ASO agencies** β€” benchmark 10 clients Γ— 50 keywords Γ— both stores nightly for **~$30/month total** (vs AppFollow's $179/seat) - 🎯 **Product marketers** β€” week-of-launch competitor benchmark (10 apps Γ— 20 keywords Γ— both stores Γ— 7 days) for **~$2.80** - πŸ” **VCs / App scouts** β€” one-shot market scan (5 keywords Γ— both stores Γ— top 10) for **~$0.10** per evaluation - πŸ“Š **Data analysts** β€” pipe the flat CSV into your BI tool and never pay for another dashboard seat --- ### πŸš€ First run β€” 3 steps 1. **Click Start** (prefill is already safe: 2 keywords Γ— both stores Γ— top 10). 2. After ~15 seconds, **open the Dataset tab**. The **Overview** view is shown by default β€” columns are already in a readable order (keyword, store, app rank, app name, then metrics, estimates, and meta). 3. **Export β†’ CSV** and open in Google Sheets / Excel. Sort by `opportunityEstimate` desc β€” the top rows are your best keyword targets. That's it. When you're ready for real workflows, jump to **App rank tracking** below. --- ### 🧭 Three actions #### πŸ“ˆ App rank tracking (most popular β€” pair with Apify Scheduler for daily tracking) "**Where does app X rank for keyword Y?**" β€” one row per (app Γ— keyword Γ— store). ```json { "action": "app-rank-tracking", "stores": ["apple", "google"], "storefront": "us", "keywords": ["instagram", "reels", "social"], "apps": ["389801252", "com.instagram.android"], "myApp": "389801252" } ```` Output columns: `keyword`, `store`, `iconUrl`, `appName`, `appId`, `appRank`, `rankStatus` (`ranked` / `out-of-top-200`), `isMyApp`, `rating`, `ratingCount`, `price`, `genre`, `developer`, `releaseDate`, `storefront`, `scrapedAt`, `action`. πŸ’‘ **Daily tracking tip**: Save this as an Apify **Task** (save-input feature) and enable **Scheduler** (daily at 03:00 UTC). Each run appends a fresh dataset; Apify retains dataset history for free, so you have a time series without extra code. *** #### πŸ†š Competitor comparison β€” just list them in `apps` Want to see how you compare to competitors? Pass them in `apps` alongside `myApp`: ```json { "action": "app-rank-tracking", "stores": ["apple", "google"], "storefront": "us", "keywords": ["photo editor"], "myApp": "389801252", "apps": ["447188370", "547702041", "380726137"] } ``` Filter by `isMyApp=true` in the Dataset to see your app's row; `isMyApp=false` for rivals. Pivot by keyword β†’ side-by-side leaderboard. *** #### πŸ” Top apps for keywords β€” market research "**Who shows up for keyword X?**" β€” top-N flat rows per (keyword Γ— store). ```json { "action": "keyword-top-apps", "stores": ["apple", "google"], "storefront": "us", "keywords": ["fitness app", "meditation"], "topN": 10 } ``` Per row you get the app's full context (appName, rating, ratingCount, price, genre, developer, url, iconUrl) **plus keyword-level columns** β€” repeated on every row so you can filter/sort without losing context. ##### Keyword-level columns **Raw aggregates (deterministic, computed from the top-ranking apps):** | Column | What it tells you | |---|---| | `totalResults` | How saturated the keyword is (8 apps = niche, 200+ = competitive) | | `top10MedianRatings` | Review-count scale of top competitors. 1,000 = approachable; 1M+ = very hard | | `top10AvgRating` | If average rating is 4.8+, users love the top apps β€” quality bar is high | | `top10PaidCount` | Paid apps in top 10. Signals whether paid listings rank | **Per-app context (per row):** | Column | What it tells you | |---|---| | `appRank` | 1–topN rank within this keyword & store | | `appName`, `developer`, `iconUrl`, `url` | Identity + direct store link for the ranking app | | `rating`, `ratingCount` | Star + review volume | | `price`, `genre` | Monetization + category | | `releaseDate` | When the app launched (YYYY-MM-DD). Helps gauge competitive age β€” a top 10 of 5+ year-old apps is harder to unseat than a top 10 of recent launches | **Convenience estimates (directional 0-100 scores for quick prioritization):** | Column | What it suggests | |---|---| | `popularityEstimate` (5-100) | How popular the keyword is likely to be | | `difficultyEstimate` (0-100) | How hard it's likely to be to rank for it | | `opportunityEstimate` (0-100) | Combined "worth targeting" score β€” the higher, the better | > **About the estimates**: `popularityEstimate`, `difficultyEstimate`, and `opportunityEstimate` are directional 0-100 scores derived from the top-ranking apps for each keyword. Treat them as a prioritization aid, not calibrated truth. For important decisions, cross-check against the raw aggregate columns (`totalResults`, `top10MedianRatings`, `top10AvgRating`, `top10PaidCount`) and apply your own judgment. Sort by `opportunityEstimate` desc to surface candidates worth investigating first. *** #### πŸ’‘ Keyword recommendations β€” idea expansion "**Give me related keywords to target.**" β€” one row per recommended keyword. ```json { "action": "keyword-recommendations", "stores": ["apple", "google"], "storefront": "us", "seedKeyword": "fitness", "limit": 30 } ``` Output columns: `seedKeyword`, `store`, `rank` (1..limit), `recommendedKeyword`, `source`, `storefront`, `scrapedAt`, `action`. The `source` column indicates where each suggestion came from β€” use it if you want to weight suggestions differently in your own workflow. *** ### 🧾 Input reference | Field | Required for | Example | |---|---|---| | `action` | all | `"keyword-top-apps"` | `"keyword-recommendations"` | `"app-rank-tracking"` | | `stores` | all | `["apple", "google"]` | | `storefront` | all | `"us"` (60 supported codes) | | `keywords` | keyword-top-apps, app-rank-tracking | up to 100 | | `topN` | keyword-top-apps | 1–20 (default 10) | | `seedKeyword` | keyword-recommendations | one word/phrase | | `limit` | keyword-recommendations | 1–80 (default 50) | | `apps` | app-rank-tracking | Apple trackIds OR Google bundle ids, up to 20 | | `myApp` | app-rank-tracking (optional) | one app ID; flags `isMyApp=true` | #### Where to find App IDs - **Apple**: numeric digits after `/id` in the store URL. Example: `https://apps.apple.com/us/app/instagram/id389801252` β†’ **`389801252`** - **Google**: value after `?id=` in the store URL. Example: `https://play.google.com/store/apps/details?id=com.instagram.android` β†’ **`com.instagram.android`** #### Storefront behavior The `storefront` field drives both the country ranking **and** the locale of the returned app names. For example, `storefront: "jp"` returns the Japan App Store / Google Play rankings with Japanese app names and localized genres. Use `us` for English. *** ### ⏱ How long does it take? | Run size | Expected duration | |---|---| | Prefill (2 KW Γ— 2 stores Γ— top 10) | **~15 seconds** | | Medium (20 KW Γ— 2 stores Γ— top 10) | ~1–2 minutes | | Large (100 KW Γ— 2 stores Γ— top 10) | ~5–8 minutes | | App rank tracking (20 apps Γ— 20 KW Γ— 2 stores) | ~30–60 seconds | | Keyword recommendations (1 seed Γ— 2 stores Γ— 50) | ~10 seconds | All runs stay well under Apify's default 1-hour timeout. Transient failures on individual (keyword Γ— store) pairs are retried 3Γ— automatically; persistent failures are recorded in the `RUN_SUMMARY` key-value entry and the rest of the run continues. *** ### ❓ When does a field come back `null` / empty? This is **not a bug** β€” it means the source store didn't expose that field for that specific app. Common cases: - **`ratingCount` / `rating`** β†’ brand-new apps with zero user ratings yet - **`releaseDate`** β†’ rare; a handful of non-US storefronts redact this - **`iconUrl`** β†’ rare; the store hasn't served an image URL - **`appRank` + `rankStatus = "out-of-top-200"`** β†’ by design, the app is outside the top 200 search results for that keyword (rank tracking covers top 200 per store) - **`genre` / `developer`** β†’ rare; data-source gap *** ### πŸ“¦ Getting your data out After a run completes: 1. **Apify Console β†’ Dataset tab** shows the **Overview** table view by default. Columns are in a user-priority order: keyword β†’ store β†’ app identity β†’ app metrics β†’ keyword estimates β†’ raw aggregates β†’ meta. Icon images render as thumbnails; URLs are clickable. 2. A secondary **All Fields** tab is auto-generated by the Apify platform β€” same data, columns sorted alphabetically. You can safely ignore it in normal use. 3. **Export β†’ CSV** (or XLSX / JSON). All columns are flat β€” Excel / Sheets will just work. 4. **API with Overview column order** (recommended for scripts): `https://api.apify.com/v2/datasets/{datasetId}/items?format=csv&view=overview` 5. **API (default, alphabetical)**: `?format=csv` β€” columns are sorted A–Z by Apify. 6. **Google Sheets**: use Apify's "Open in Sheets" button (bottom of Dataset tab), or `IMPORTDATA("")` in a cell. *** ### πŸ” Daily tracking workflow (free time series) 1. Configure the Actor input once (e.g., `app-rank-tracking` for your 20 target keywords). 2. In Apify Console β†’ **Save as Task** β†’ give it a name. 3. Open the Task β†’ **Integrations β†’ Scheduler** β†’ daily cadence. 4. Each day's run creates a new Dataset; combine by downloading the last N days' datasets and joining on `appId + keyword + store` with a `scrapedAt` timestamp column. No delta-calculation code required on our side; Apify retains dataset history for you. *** ### πŸ’° Cost (pay per result row) **$0.001 per dataset row.** No startup fee. No tiered events. You only pay for the rows you actually receive. #### Typical runs | Use case | Volume | Cost | |---|---|---| | Prefill run (2 KW Γ— 2 stores Γ— top 10) | 40 rows | ~**$0.04** | | Daily rank tracking: 1 app Γ— 20 KW Γ— both stores | 40 rows/day | ~**$0.04/day** / **$1.20/month** | | Agency: 5 apps Γ— 50 KW Γ— both stores daily | 500 rows/day | ~**$0.50/day** / **$15/month** | | Keyword research: seed Γ— both stores Γ— 50 recs | ~100 rows | ~**$0.10** | Compare to **AppTweak Essential** ($83/mo fixed), **AppFollow** ($179+/mo), **Sensor Tower** (~$25k+/yr). Daily 60-KW tracking here costs **~$3.60/month** β€” over **98 % cheaper** than AppFollow. *** ### ℹ️ What we do β€” and what we intentionally don't **We excel at**: - Returning clean, flat ASO rows from both stores in CSV-ready form - Daily rank tracking paired with Apify Scheduler (free time series via dataset history) - Competitor benchmarking in one pivot (set `myApp`, list rivals in `apps`) - 60 storefronts with locale-aware app names **We intentionally don't**: - **Replace calibrated SaaS dashboards** like AppTweak or Sensor Tower. Our estimates are directional prioritization aids, not calibrated indices for precision work. For calibrated scoring, keep those subscriptions. - **Track apps below the top 200** search results (outside that range the row returns `appRank: null` / `rankStatus: "out-of-top-200"`) - **Build dashboards** β€” we produce data rows; your spreadsheet, Looker, or Tableau builds the chart **Disclaimer**: Not affiliated with Apple Inc. or Google LLC. Data is derived from publicly accessible endpoints; use in accordance with each store's Terms of Service. *** ### πŸ”— Related Actors by the same publisher - [slothtechlabs/ios-android-app-rankings-scraper](https://apify.com/slothtechlabs/ios-android-app-rankings-scraper) β€” Top chart rankings (Top Free / Paid / Grossing) for both stores, daily. *** ### ▢️ Ready? **Click Start** at the top with the prefilled input β€” your first 40 rows of ASO intelligence arrive in about 15 seconds, for **$0.04** total. *** ### πŸ“¬ Feedback & support If you have any questions, feature requests, or encounter any issues, please open an [issue on the Actor's page](https://console.apify.com/actors). We'd love to hear from you! # Actor input Schema ## `action` (type: `string`): Pick an action. Each action uses a different subset of the fields below. ## `stores` (type: `array`): Query Apple App Store, Google Play, or both in one run. ## `storefront` (type: `string`): ISO 3166-1 alpha-2 country code. 60 storefronts supported. ## `keywords` (type: `array`): Used by 'Top apps for keywords' and 'App rank tracking'. Enter one keyword per line, up to 100. ## `topN` (type: `integer`): Only used by 'Top apps for keywords'. 1–20. Default 10. Increasing this multiplies the number of result rows. ## `seedKeyword` (type: `string`): Only used by 'Keyword recommendations'. A single seed word to expand (e.g., 'fitness' β†’ 'fitness tracker', 'fitness app for women', ...). ## `limit` (type: `integer`): Only used by 'Keyword recommendations'. 1–80 suggestions per store. Default 50. ## `apps` (type: `array`): Only used by 'App rank tracking'. Enter Apple numeric trackIds (e.g., "389801252" β€” the digits after '/id' in a URL like https://apps.apple.com/us/app/instagram/id389801252) OR Google bundle ids (e.g., "com.instagram.android" β€” the value after '?id=' in a URL like https://play.google.com/store/apps/details?id=com.instagram.android). Up to 20. ## `myApp` (type: `string`): Only used by 'App rank tracking'. Your own app's ID (Apple trackId or Google bundle id). If set, the row for that app will have isMyApp=true β€” handy for side-by-side reports. Will be automatically added to the tracked apps list if not already there. ## `proxyConfiguration` (type: `object`): Defaults to Apify Proxy (datacenter). Enable residential if a specific country returns sparse Google Play results. ## Actor input object example ```json { "action": "keyword-top-apps", "stores": [ "apple", "google" ], "storefront": "us", "keywords": [ "fitness app", "meditation" ], "topN": 10, "seedKeyword": "fitness", "limit": 50, "proxyConfiguration": { "useApifyProxy": true } } ``` # Actor output Schema ## `results` (type: `string`): All rows pushed to the default dataset by this run. ## `runSummary` (type: `string`): JSON object with action, totalRows, errors, errorDetails, durationMs, stores, storefront. # 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 = { "action": "keyword-top-apps", "stores": [ "apple", "google" ], "storefront": "us", "keywords": [ "fitness app", "meditation" ], "topN": 10, "limit": 50, "proxyConfiguration": { "useApifyProxy": true } }; // Run the Actor and wait for it to finish const run = await client.actor("slothtechlabs/aso-keyword-rank-tracker").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`πŸ’Ύ 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); }); // πŸ“š Want to learn more πŸ“–? Go to β†’ 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 = { "action": "keyword-top-apps", "stores": [ "apple", "google", ], "storefront": "us", "keywords": [ "fitness app", "meditation", ], "topN": 10, "limit": 50, "proxyConfiguration": { "useApifyProxy": True }, } # Run the Actor and wait for it to finish run = client.actor("slothtechlabs/aso-keyword-rank-tracker").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "action": "keyword-top-apps", "stores": [ "apple", "google" ], "storefront": "us", "keywords": [ "fitness app", "meditation" ], "topN": 10, "limit": 50, "proxyConfiguration": { "useApifyProxy": true } }' | apify call slothtechlabs/aso-keyword-rank-tracker --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=slothtechlabs/aso-keyword-rank-tracker", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "ASO Keyword Rank Tracker β€” App Store & Google Play", "description": "Track keyword rankings, find top apps per keyword, and discover related keywords across Apple App Store and Google Play (60+ storefronts). Pay-per-use, no subscription. Flat CSV output. The affordable AppTweak / Sensor Tower alternative β€” same data at a fraction of the cost.", "version": "1.0", "x-build-id": "VJNdhLcitRem5szze" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/slothtechlabs~aso-keyword-rank-tracker/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-slothtechlabs-aso-keyword-rank-tracker", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/slothtechlabs~aso-keyword-rank-tracker/runs": { "post": { "operationId": "runs-sync-slothtechlabs-aso-keyword-rank-tracker", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/slothtechlabs~aso-keyword-rank-tracker/run-sync": { "post": { "operationId": "run-sync-slothtechlabs-aso-keyword-rank-tracker", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "action", "stores", "storefront" ], "properties": { "action": { "title": "What do you want to do?", "enum": [ "keyword-top-apps", "keyword-recommendations", "app-rank-tracking" ], "type": "string", "description": "Pick an action. Each action uses a different subset of the fields below.", "default": "keyword-top-apps" }, "stores": { "title": "Stores", "type": "array", "description": "Query Apple App Store, Google Play, or both in one run.", "items": { "type": "string", "enum": [ "apple", "google" ], "enumTitles": [ "🍎 Apple App Store (iOS)", "πŸ€– Google Play Store (Android)" ] }, "default": [ "apple", "google" ] }, "storefront": { "title": "Storefront (Country)", "enum": [ "us", "gb", "jp", "kr", "cn", "de", "fr", "au", "ca", "br", "in", "mx", "it", "es", "nl", "se", "no", "dk", "fi", "ch", "at", "be", "pt", "ie", "nz", "sg", "hk", "tw", "th", "my", "ph", "id", "vn", "sa", "ae", "il", "tr", "ru", "pl", "cz", "hu", "ro", "bg", "hr", "sk", "si", "lt", "lv", "ee", "gr", "ua", "za", "ng", "ke", "eg", "ar", "cl", "co", "pe", "pk" ], "type": "string", "description": "ISO 3166-1 alpha-2 country code. 60 storefronts supported.", "default": "us" }, "keywords": { "title": "Keywords", "maxItems": 100, "type": "array", "description": "Used by 'Top apps for keywords' and 'App rank tracking'. Enter one keyword per line, up to 100.", "items": { "type": "string" } }, "topN": { "title": "How many top apps per keyword?", "minimum": 1, "maximum": 20, "type": "integer", "description": "Only used by 'Top apps for keywords'. 1–20. Default 10. Increasing this multiplies the number of result rows.", "default": 10 }, "seedKeyword": { "title": "Seed keyword", "type": "string", "description": "Only used by 'Keyword recommendations'. A single seed word to expand (e.g., 'fitness' β†’ 'fitness tracker', 'fitness app for women', ...)." }, "limit": { "title": "How many recommendations?", "minimum": 1, "maximum": 80, "type": "integer", "description": "Only used by 'Keyword recommendations'. 1–80 suggestions per store. Default 50.", "default": 50 }, "apps": { "title": "App IDs to track", "maxItems": 20, "type": "array", "description": "Only used by 'App rank tracking'. Enter Apple numeric trackIds (e.g., \"389801252\" β€” the digits after '/id' in a URL like https://apps.apple.com/us/app/instagram/id389801252) OR Google bundle ids (e.g., \"com.instagram.android\" β€” the value after '?id=' in a URL like https://play.google.com/store/apps/details?id=com.instagram.android). Up to 20.", "items": { "type": "string" } }, "myApp": { "title": "My app ID (optional)", "type": "string", "description": "Only used by 'App rank tracking'. Your own app's ID (Apple trackId or Google bundle id). If set, the row for that app will have isMyApp=true β€” handy for side-by-side reports. Will be automatically added to the tracked apps list if not already there." }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Defaults to Apify Proxy (datacenter). Enable residential if a specific country returns sparse Google Play results.", "default": { "useApifyProxy": true } } } }, "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 } } } } } } } } } } ```