# ASO Rank Tracker with History — App Store & Google Play (`datascoutlab/aso-rank-tracker`) Actor ASO keyword rank tracking for the App Store and Google Play across 60+ storefronts. Each row carries yesterday position, 1-day and 7-day deltas, 30-day best/worst and a trend — no dataset joins. A failed check is never reported as a rank drop. Pay-per-use alternative to AppTweak and AppFollow. - **URL**: https://apify.com/datascoutlab/aso-rank-tracker.md - **Developed by:** [Anton DataScout](https://apify.com/datascoutlab) (community) - **Categories:** SEO tools, Developer tools, Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing $2.00 / 1,000 ranking 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 web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows. 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ## 📈 ASO Rank Tracker with History — App Store & Google Play **Where does your app rank for a keyword today — and did it move since yesterday?** Keyword rank tracking and App Store Optimization (ASO) monitoring for iOS and Android, across 60+ country storefronts, priced per use instead of per seat. Most rank scrapers hand you a fresh snapshot every run and leave you to join yesterday's dataset with today's in a spreadsheet. This Actor keeps the history itself. Every row already carries **yesterday's position, the 1-day and 7-day change, the 30-day best and worst, and a trend** — nothing to merge, nothing to compute. Built by an indie iOS developer who tracks his own portfolio with it daily. --- ### Why this one **🧮 History is included, not homework.** Run it on a schedule and the series builds itself in a named store that survives runs. Row two of your very first week already answers "am I going up or down?" **🛡 A failed check is never reported as a rank drop.** This is the whole point. When a request degrades — bad node, rate limit, timeout — most trackers write "not ranked" and your chart shows a cliff that never happened. Here every measurement is one of three honest states: | `rankStatus` | Meaning | Written to history? | |---|---|---| | `ranked` | found at a real position | yes | | `not-in-results` | search returned normally, app wasn't in it | yes | | `check-failed` | the check itself failed after 3 rotated retries | **never** | A broken check leaves the series untouched, so your trend line stays truthful instead of gaining a phantom crash. **📏 Honest depth.** Apple serves up to ~200 results per search; **Google Play caps far lower — around 28**. Claiming "out of top 200" on both stores would be false on half your rows, so every row reports `checkedDepth` — how deep this specific check actually reached. **🏷 Rows stay identifiable when you drop out.** The app's name, developer and rating are resolved once per run, so a `not-in-results` row still tells you *which* app fell out — exactly when you most need to know. **🎯 Matching is by ID only.** Never by name. A search for "soulmate drawing" returns three different apps with nearly identical titles — name matching would happily track a competitor's position as yours. --- ### Sample output | keyword | store | appName | position | previousPosition | delta1d | delta7d | trend | rankStatus | checkedDepth | |---|---|---|---|---|---|---|---|---|---| | ai chat | apple | ChatGPT | 3 | 9 | +6 | +12 | up | ranked | 200 | | ai chat | apple | My App | — | 41 | — | — | down | not-in-results | 200 | | photo editor | google | Instagram | — | — | — | — | new | not-in-results | 28 | Positive delta = moved **up** (lower position number is better). Full row: `keyword, store, storefront, appId, appName, iconUrl, isMyApp, position, rankStatus, checkedDepth, totalResults, previousPosition, delta1d, delta7d, best30d, worst30d, trend, rating, ratingCount, developer, genre, price, releaseDate, url, error, checkedAt` --- ### Input ```json { "apps": [ "https://apps.apple.com/us/app/chatgpt/id6448311069", "com.openai.chatgpt" ], "keywords": ["ai chat", "chatbot", "ai assistant"], "stores": ["apple", "google"], "storefronts": ["us", "de", "jp"], "myApp": "6448311069", "trackingId": "my-portfolio" } ```` | Field | Notes | |---|---| | `apps` | Apple numeric IDs and Google package names can be mixed. URLs work too | | `keywords` | One search request covers **every** app you track — adding apps costs no extra time | | `stores` | `apple`, `google`, or both. IDs are routed to the matching store automatically | | `storefronts` | ISO country codes — `us`, `gb`, `de`, `jp`, `br`… | | `myApp` | Flags its rows `isMyApp: true` so you can filter yourself out of a competitor set | | `trackingId` | **History lives per tracking ID.** Keep it identical between runs for a continuous series; change it to keep projects separate | **Where to find IDs.** Apple: the digits after `/id` in the store URL. Google: the value after `?id=`. *** ### 🔁 Daily tracking 1. Configure the input once and **Save as Task** 2. Open the Task → **Integrations → Scheduler** → daily 3. Keep `trackingId` unchanged — that's what stitches the runs into a series From the second day on, every row carries its own delta. No dataset joins, no lookup formulas. *** ### 💰 Cost **$0.002 per result row.** No start fee. You are billed for rows you actually receive. | Use case | Volume | Monthly | |---|---|---| | 1 app × 20 keywords × both stores, daily | 40 rows/day | **~$2.40** | | 5 apps × 50 keywords × both stores, daily | 500 rows/day | **~$30** | | One-off competitor check, 10 apps × 10 keywords | 100 rows | **$0.20** | For scale: AppTweak rank tracking starts at $83/month and AppFollow at $179/month, billed per seat whether you check one keyword or a thousand. If you are looking for an AppTweak alternative or an AppFollow alternative for pure rank tracking, this is the same measurement without the subscription. *** ### 🔌 Using it from your own stack The dataset is available the moment a run finishes — as CSV, JSON or XLSX from the console, or straight from the API: ``` https://api.apify.com/v2/datasets/{datasetId}/items?format=csv&view=overview ``` - **Google Sheets** — `IMPORTDATA()` the URL above and your sheet refreshes itself - **BI tools** — point Looker Studio, Metabase or Tableau at the same endpoint - **Webhooks** — fire on run completion to push rows into Slack, a database or your own service - **AI agents** — the Actor is exposed through Apify's MCP server, so an agent can ask for a keyword position and get structured rows back - **Zapier / Make / n8n** — available through Apify's integrations, no glue code Because every row already contains its own deltas, none of these consumers need to keep state or join anything. *** ### Notes and limits - **Search depth.** Apple ~200 per query, Google Play ~28. Reported per row in `checkedDepth` - **Rate limits.** Both stores throttle by IP. Proxy rotation is on by default — leaving it off will turn large runs into `check-failed` rows - **One point per calendar day.** Several runs a day update the same day's value rather than flooding the series, so `delta1d` always means yesterday → today - **History depth.** 90 days per app/keyword/store/country, kept in a named key-value store - **Run report.** `RUN_SUMMARY` records how many checks were ranked, absent or failed, with the reason for each failure This Actor is a **monitor**, not a research suite: it answers "where am I and where am I going". For keyword discovery and market exploration, use a dedicated research tool alongside it. *** *Not affiliated with Apple Inc. or Google LLC. Data comes from publicly accessible endpoints; use it in accordance with each store's terms of service.* # Actor input Schema ## `apps` (type: `array`): Apps to look for in the search results. Paste store URLs or plain IDs — Apple numeric IDs (`6448311069`) and Google package names (`com.instagram.android`) can be mixed freely. Apple: the digits after `/id` in `https://apps.apple.com/us/app/chatgpt/id6448311069` Google: the value after `?id=` in `https://play.google.com/store/apps/details?id=com.instagram.android` ## `keywords` (type: `array`): Search terms to check the positions for. One search request covers every app you track, so adding apps costs nothing extra in speed. ## `stores` (type: `array`): Which stores to check. Apple IDs are only looked up in the App Store, Google package names only in Google Play — mixing both here is safe. ## `storefronts` (type: `array`): ISO country codes — e.g. `us`, `gb`, `de`, `jp`, `br`. Each storefront is checked separately, so 10 keywords × 3 countries = 30 search requests. ## `myApp` (type: `string`): One app ID or URL from the list above. Its rows are flagged `isMyApp = true` so you can filter your own positions out of a competitor set in one click. ## `trackingId` (type: `string`): History is kept per tracking ID. Keep it identical between runs to build a continuous time series; use different IDs to keep separate projects from mixing. ## `maxPosition` (type: `integer`): Positions beyond this rank are reported as `out-of-top-200`. Lowering it does not reduce cost — the search result is fetched in one request either way. ## `proxyConfiguration` (type: `object`): Rotating proxy sessions are what keep large runs accurate: every retry goes out through a fresh session instead of hammering a rate-limited IP. Turning this off on runs with many keywords or countries will produce `check-failed` rows. ## Actor input object example ```json { "apps": [ "https://apps.apple.com/us/app/chatgpt/id6448311069", "com.openai.chatgpt" ], "keywords": [ "ai chat", "chatbot" ], "stores": [ "apple" ], "storefronts": [ "us" ], "trackingId": "default", "maxPosition": 200, "proxyConfiguration": { "useApifyProxy": true } } ``` # Actor output Schema ## `rankings` (type: `string`): Every tracked position with previous position, 1-day and 7-day deltas, 30-day best/worst and trend. ## `rankingsCsv` (type: `string`): The same rows in the readable column order — paste straight into IMPORTDATA() or open in Excel. ## `runSummary` (type: `string`): How many checks were ranked, absent or failed — plus the reason for every failed check. # 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 = { "apps": [ "https://apps.apple.com/us/app/chatgpt/id6448311069", "com.openai.chatgpt" ], "keywords": [ "ai chat", "chatbot" ], "storefronts": [ "us" ] }; // Run the Actor and wait for it to finish const run = await client.actor("datascoutlab/aso-rank-tracker").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 = { "apps": [ "https://apps.apple.com/us/app/chatgpt/id6448311069", "com.openai.chatgpt", ], "keywords": [ "ai chat", "chatbot", ], "storefronts": ["us"], } # Run the Actor and wait for it to finish run = client.actor("datascoutlab/aso-rank-tracker").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 '{ "apps": [ "https://apps.apple.com/us/app/chatgpt/id6448311069", "com.openai.chatgpt" ], "keywords": [ "ai chat", "chatbot" ], "storefronts": [ "us" ] }' | apify call datascoutlab/aso-rank-tracker --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=datascoutlab/aso-rank-tracker", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "ASO Rank Tracker with History — App Store & Google Play", "description": "ASO keyword rank tracking for the App Store and Google Play across 60+ storefronts. Each row carries yesterday position, 1-day and 7-day deltas, 30-day best/worst and a trend — no dataset joins. A failed check is never reported as a rank drop. Pay-per-use alternative to AppTweak and AppFollow.", "version": "1.0", "x-build-id": "8A36UUiEeTdQzfEll" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/datascoutlab~aso-rank-tracker/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-datascoutlab-aso-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/datascoutlab~aso-rank-tracker/runs": { "post": { "operationId": "runs-sync-datascoutlab-aso-rank-tracker", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/datascoutlab~aso-rank-tracker/run-sync": { "post": { "operationId": "run-sync-datascoutlab-aso-rank-tracker", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "apps", "keywords" ], "properties": { "apps": { "title": "Apps", "type": "array", "description": "Apps to look for in the search results. Paste store URLs or plain IDs — Apple numeric IDs (`6448311069`) and Google package names (`com.instagram.android`) can be mixed freely.\n\nApple: the digits after `/id` in `https://apps.apple.com/us/app/chatgpt/id6448311069`\nGoogle: the value after `?id=` in `https://play.google.com/store/apps/details?id=com.instagram.android`", "items": { "type": "string" } }, "keywords": { "title": "Keywords", "type": "array", "description": "Search terms to check the positions for. One search request covers every app you track, so adding apps costs nothing extra in speed.", "items": { "type": "string" } }, "stores": { "title": "Stores", "type": "array", "description": "Which stores to check. Apple IDs are only looked up in the App Store, Google package names only in Google Play — mixing both here is safe.", "items": { "type": "string", "enum": [ "apple", "google" ], "enumTitles": [ "Apple App Store", "Google Play" ] }, "default": [ "apple" ] }, "storefronts": { "title": "Country storefronts", "type": "array", "description": "ISO country codes — e.g. `us`, `gb`, `de`, `jp`, `br`. Each storefront is checked separately, so 10 keywords × 3 countries = 30 search requests.", "items": { "type": "string" } }, "myApp": { "title": "My app (optional)", "type": "string", "description": "One app ID or URL from the list above. Its rows are flagged `isMyApp = true` so you can filter your own positions out of a competitor set in one click." }, "trackingId": { "title": "Tracking ID", "type": "string", "description": "History is kept per tracking ID. Keep it identical between runs to build a continuous time series; use different IDs to keep separate projects from mixing.", "default": "default" }, "maxPosition": { "title": "How deep to look", "minimum": 10, "maximum": 200, "type": "integer", "description": "Positions beyond this rank are reported as `out-of-top-200`. Lowering it does not reduce cost — the search result is fetched in one request either way.", "default": 200 }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Rotating proxy sessions are what keep large runs accurate: every retry goes out through a fresh session instead of hammering a rate-limited IP. Turning this off on runs with many keywords or countries will produce `check-failed` rows.", "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 } } } } } } } } } } ```