# Google Play Email Scraper (`scraperoka/google-play-email-scraper`) Actor 📧 Google Play Email Scraper extracts verified contact emails from Google Play apps fast and accurately. 🚀 Perfect for B2B lead generation, outreach, and market research. Save time, reach more prospects, and boost campaigns! 📈 - **URL**: https://apify.com/scraperoka/google-play-email-scraper.md - **Developed by:** [Scraperoka](https://apify.com/scraperoka) (community) - **Categories:** Lead generation, Automation, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $0.01 / 1,000 results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 ### Google Play Email Scraper 🚀 Manually visiting Google Play profiles to collect emails wastes hours you don’t have. **Google Play Email Scraper** pulls email contacts from publicly available Google Play bios using your keywords and email-domain filters—so you can build outreach lists fast with Google Play email scraper searches. This **Google Play Email Scraper** (also known as a Google Play developer email scraper and Google Play app email finder) is ideal for marketers, recruiters, and growth teams who want thousands of records in minutes from a single run. --- ### What You Get: Sample Output Here’s a sample record from a single run: ```json { "network": "Google Play.com", "keyword": "founder", "title": "Acme Apps — Founder Profile", "description": "Contact: founder@acmeapps.com | Marketing team available for partnerships", "url": "https://example.com/developer-page", "email": "founder@acmeapps.com", "proxyGroups": ["GOOGLE_SERP"] } ```` | Field | Type | What It Tells You | |---|---|---| | `network` | string | Which source network the contact came from (shown as `Google Play.com`) | | `keyword` | string | The keyword you used to discover this contact (useful for segmenting leads) | | `title` | string | The page/search result title associated with the email | | `description` | string | The surrounding text where the email was detected (handy for validation) | | `url` | string | The link associated with the email record so you can verify quickly | | `email` | string | The extracted contact email address | | `proxyGroups` | array | The proxy group configuration used for the run (useful for debugging/reproducibility) | | `status` | string | Not present in the pushed output records (if you need status, track success via your run logs/dataset presence) | | `error_message` | string | Not present in the pushed output records (run-level issues are reflected in logs and missing outputs) | | `emails` | array | Not present as a separate field (each dataset row contains a single `email`) | | `success` | boolean | Not present as a dataset field (use exported row count and logs to assess output) | | `timestamp` | string | Not present as a dataset field (timestamps are handled by Apify dataset metadata/logs) | | `charged_event_name` | string | Not a dataset field; it’s used internally when pushing records | Export your dataset as JSON, CSV, or Excel — straight from the Apify dashboard. *** ### Why Google Play Email Scraper? There are a lot of ways to pull data from Google Play — here’s what sets **Google Play Email Scraper** apart. #### Keyword and domain targeting for cleaner leads You control the search `keywords` and restrict results with `customDomains` (example: `@gmail.com`) so your Google Play developer email scraper results are more relevant to your outreach goals. #### Designed for bulk extraction workflows **Google Play Email Scraper** is built to collect up to your chosen `maxEmails` and keep writing results as it goes, making it practical for bulk email extraction from Google Play. #### Resilient scraping with retry and fallbacks The actor includes retries and fallback behavior to keep extraction running even when pages fail to load or access is blocked, helping reduce wasted runs when using a Google Play listing email scraper workflow. #### Flexible scraping engines for different needs Choose between `engine: "legacy"` and `engine: "cost-effective"` to match your reliability vs. efficiency needs while running a Google Play app email finder job. *** ### Configuring Your Run Drop this into your `input.json` to get started: ```json { "keywords": ["founder", "marketing"], "location": "", "platform": "Google Play", "customDomains": ["@gmail.com"], "maxEmails": 20, "engine": "legacy", "proxyConfiguration": { "useApifyProxy": true } } ``` | Parameter | Required | What It Does | |---|---|---| | `keywords` | ✅ | A list of keywords to search for (used to find relevant Google Play bios where emails may appear) | | `location` | ⬜ | A location string to filter search results (leave empty for no location filtering) | | `platform` | ⬜ | Select platform; this actor supports `Google Play` | | `customDomains` | ⬜ | A list of custom email domains (e.g., `@gmail.com`) to filter which extracted emails you keep | | `maxEmails` | ⬜ | Maximum number of emails to collect; the run stops once this limit is reached | | `engine` | ⬜ | Choose `cost-effective` or `legacy` to balance speed/cost vs. reliability | | `proxyConfiguration` | ⬜ | Configure proxies for this Actor (recommended for larger keyword sets) | | ↳ `proxy support` | ⬜ | Route requests through Apify Proxy when set in `proxyConfiguration` | *** ### Core Capabilities #### Email extraction from Google Play bios **Google Play Email Scraper** finds emails from Google Play bios and posts related to your `keywords`, then filters them using your `customDomains`. This makes it a practical Google Play store email extractor for lead research and list building. #### Output tailored for outreach workflows Each found email is saved as a dataset row containing the source context (`title`, `description`, and `url`) plus the extracted `email`. That structure helps you validate quickly and organize your outreach pipeline when scraping Google Play developer contact information. #### Deduplication across the run The actor keeps track of `seen_emails` so the same email isn’t pushed repeatedly. This keeps results cleaner when doing bulk email extraction from Google Play. #### Pagination control with a hard cap Use `maxEmails` to stop the run once you hit your desired volume. It helps manage runtime and makes this Google Play developer email scraper straightforward to use for both tests and production-scale jobs. #### Engine choice for your constraints If you’re optimizing for cost and speed, use `engine: "cost-effective"`. If you prioritize a more traditional approach, select `engine: "legacy"`. Either way, the goal is reliable data collection for Google Play listing email scraper use cases. *** ### Who Gets the Most Out of This Here’s how different teams put **Google Play Email Scraper** to work: **Growth and Lead Generation Teams** — Use keywords and `customDomains` to build outreach lists from Google Play developer pages, then export results straight from the Apify dashboard for faster campaign launches. **Sales Development Representatives** — Quickly turn a set of app/brand leads into a contact-ready dataset by scraping emails from publicly available Google Play bios related to “founder”, “marketing”, and similar terms. **Recruiters and Talent Sourcers** — Locate developer contact emails tied to app creators and publishers for sourcing, partnership outreach, and hiring pipelines—without manual profile hunting. **Content and Partnerships Managers** — Extract emails that appear in publicly available bios around your topic keywords, so you can find collaboration contacts faster and validate with the provided context fields. **Data Analysts and Researchers** — Run repeatable batches with consistent fields (`network`, `keyword`, `title`, `description`, `url`, `email`) and analyze how email presence varies by keyword strategy and domain filter choices. **Automation & Integration Developers** — Use the Apify API to pull results programmatically after each run and feed them into CRMs or enrichment pipelines as part of an automated workflow for scraping Google Play app contact information at scale. *** ### Step-by-Step: How to Use It No coding needed. Here's how to run Google Play Email Scraper from start to finish: 1. **Open** the actor on Apify — go to [console.apify.com](https://console.apify.com). 2. **Enter** your inputs — start with `keywords`, then set `customDomains` and `maxEmails` based on how targeted your Google Play email scraper results should be. 3. **Set** your proxy settings — configure `proxyConfiguration` if you plan larger searches or want more stable scraping behavior. 4. **Hit Run** and watch the live log — monitor progress and see when the actor continues or stops based on your limits. 5. **Review** the dataset tab — every pushed record includes `email`, plus context like `title`, `description`, and `url`. 6. **Export** your results — download as JSON, CSV, or Excel directly from the Apify dataset. 7. **Re-run** with broader or narrower targeting — if results are low, adjust `keywords` and expand `customDomains` to improve coverage for extract emails from Google Play. The whole process takes under 5 minutes to set up. *** ### Integrations & Export Options Once your data is collected, **Google Play Email Scraper** plugs directly into your existing workflow. Export formats are available from the Apify dataset tab, including JSON and CSV (and Excel via the dashboard export options). This is ideal when you’re building a Google Play developer contact scraper lead list for spreadsheets or CRMs. You can also connect it to automation workflows using Apify’s API access and integration options (including webhooks for downstream actions, and no-code tools like Zapier/Make). For implementation depth, use the Apify documentation: https://apify.com/docs/api *** ### Pricing & Free Trial **Google Play Email Scraper** runs on the Apify platform, which offers a **free tier** — no credit card required to get started. Free tier usage typically includes platform credits for several test runs, and larger jobs can scale on Apify plans. Pricing is based on Apify compute usage (CU) and the plan you choose—check the current pricing page for exact details. Start for free at [apify.com](https://apify.com) and scale when you're ready. *** ### Reliability & Performance | What We Handle | How | |---|---| | Blocked or failed pages | Retries and fallbacks to keep scraping progressing | | Runtime control | Stops once `maxEmails` is reached to avoid endless runs | | Result consistency | Writes extracted contacts as dataset rows with context fields (`title`, `description`, `url`) | | Output cleanliness | Deduplicates using `seen_emails` so you don’t get repeated rows | | Scaling runs | Concurrency is used in the `cost-effective` engine to improve throughput | **Limitations:** This actor extracts emails that are present in publicly available Google Play bios/posts and filters by `customDomains`. If the target bios don’t include emails, results will be limited regardless of settings. For enterprise-scale runs, contact us to discuss custom configurations. *** ### Frequently Asked Questions #### Is there a free plan or trial? Yes. Apify provides a free tier with platform credits so you can test **Google Play Email Scraper** before scaling. #### Do I need to log in to Google Play to use this? No. The actor scrapes emails from publicly available sources on Google Play—no login is required to run the actor. #### How accurate is the data? The accuracy depends on what email addresses are actually visible in the publicly available Google Play bios/posts. The actor extracts emails and keeps surrounding context like `description` and `url` so you can validate easily. #### How many results can I get per run? You control that with `maxEmails`. The actor stops once your requested number of emails is reached, so you can run quick tests or larger bulk email extraction from Google Play. #### How often is the data updated / how fresh is it? Data freshness is based on when you run the actor. If you need fresher results, re-run the actor with updated `keywords` and `customDomains`. #### Is this legal? Does it comply with GDPR / CCPA? You should ensure your use complies with applicable laws and the platform’s terms. The actor collects **publicly available data**, but compliance and lawful processing is your responsibility. #### Can I export results to Google Sheets or Excel? Yes. You can export from the Apify dashboard as JSON and CSV, and you can use integrations (including no-code automation via Zapier/Make) to push results into Google Sheets or similar tools. #### Can I run this on a schedule automatically? Yes. You can schedule Apify actor runs (e.g., via cron scheduling in Apify), which is useful for ongoing Google Play developer contact scraper workflows. #### Can I access this via API? Yes. You can trigger runs and retrieve dataset results via the Apify API. See: https://apify.com/docs/api #### What happens if the actor hits an error? If access fails or pages return no results, the actor uses retries and stop logic governed by your limits (like `maxEmails`). You can check run logs for errors, and review the dataset for what was successfully extracted. *** ### Need Help or Have a Request? Got a question about **Google Play Email Scraper** or want a new feature added? Reach out at . We welcome requests like webhook notifications on completion and batch CSV-style workflows for faster lead operations. *** ### Disclaimer & Responsible Use *Google Play Email Scraper is the fastest, most reliable way to scrape developer contact emails from Google Play — start your free run today.* This actor collects **publicly available data** from Google Play bios and related publicly visible content. It does not access private accounts, login-gated content, or password-protected pages. You are responsible for complying with GDPR, CCPA, and any applicable platform terms when you store and use the results. For data-removal requests, contact . Use responsibly, ethically, and only for lawful purposes. # Actor input Schema ## `keywords` (type: `array`): A list of keywords to search for. ## `location` (type: `string`): Location to filter search results. ## `platform` (type: `string`): Select platform. ## `customDomains` (type: `array`): List of custom email domains ## `maxEmails` (type: `integer`): Maximum number of emails to collect. The scraper will stop once this limit is reached. Setting a higher limit allows for more potential results but doesn't guarantee reaching that number. This helps save costs by controlling scraping time. ## `engine` (type: `string`): Choose scraping engine. 🚀 Cost Effective (New): Uses residential proxies with async requests for faster, cheaper scraping. 🔧 Legacy: Uses GOOGLE\_SERP proxy with traditional selectors - more reliable but slower and more expensive. ## `proxyConfiguration` (type: `object`): Configure proxies for this Actor. ## Actor input object example ```json { "keywords": [ "founder", "marketing" ], "location": "", "platform": "Google Play", "customDomains": [ "@gmail.com" ], "maxEmails": 20, "engine": "legacy" } ``` # 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": [ "founder", "marketing" ], "location": "", "customDomains": [ "@gmail.com" ] }; // Run the Actor and wait for it to finish const run = await client.actor("scraperoka/google-play-email-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": [ "founder", "marketing", ], "location": "", "customDomains": ["@gmail.com"], } # Run the Actor and wait for it to finish run = client.actor("scraperoka/google-play-email-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": [ "founder", "marketing" ], "location": "", "customDomains": [ "@gmail.com" ] }' | apify call scraperoka/google-play-email-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scraperoka/google-play-email-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Google Play Email Scraper", "description": "📧 Google Play Email Scraper extracts verified contact emails from Google Play apps fast and accurately. 🚀 Perfect for B2B lead generation, outreach, and market research. Save time, reach more prospects, and boost campaigns! 📈", "version": "0.0", "x-build-id": "rEYOnRY3DkZ9Qw7c8" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scraperoka~google-play-email-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scraperoka-google-play-email-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scraperoka~google-play-email-scraper/runs": { "post": { "operationId": "runs-sync-scraperoka-google-play-email-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scraperoka~google-play-email-scraper/run-sync": { "post": { "operationId": "run-sync-scraperoka-google-play-email-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": "Keywords", "type": "array", "description": "A list of keywords to search for.", "default": [ "founder", "marketing" ], "items": { "type": "string" } }, "location": { "title": "Location", "type": "string", "description": "Location to filter search results.", "default": "" }, "platform": { "title": "Platform", "enum": [ "Google Play" ], "type": "string", "description": "Select platform.", "default": "Google Play" }, "customDomains": { "title": "Custom Email Domains", "type": "array", "description": "List of custom email domains", "default": [ "@gmail.com" ], "items": { "type": "string" } }, "maxEmails": { "title": "Max Emails", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Maximum number of emails to collect. The scraper will stop once this limit is reached. Setting a higher limit allows for more potential results but doesn't guarantee reaching that number. This helps save costs by controlling scraping time.", "default": 20 }, "engine": { "title": "Engine", "enum": [ "cost-effective", "legacy" ], "type": "string", "description": "Choose scraping engine. 🚀 Cost Effective (New): Uses residential proxies with async requests for faster, cheaper scraping. 🔧 Legacy: Uses GOOGLE_SERP proxy with traditional selectors - more reliable but slower and more expensive.", "default": "legacy" }, "proxyConfiguration": { "title": "Proxy Configuration", "type": "object", "description": "Configure proxies for this Actor." } } }, "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 } } } } } } } } } } ```