# Booking.com Email Scraper (`scrapeflux/booking-com-email-scraper`) Actor 📧 Booking.Com Email Scraper helps you extract hotel email contacts from Booking.com for targeted outreach. Automate lead building, sales, and market research with accuracy and speed. 🚀 Perfect for B2B teams & agencies. - **URL**: https://apify.com/scrapeflux/booking-com-email-scraper.md - **Developed by:** [ScrapeFlux](https://apify.com/scrapeflux) (community) - **Categories:** Lead generation, Automation, Travel - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $2.99 / 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 ### Booking.com Email Scraper ⚡ Getting contact emails from Booking.com listings is painfully manual—copy, paste, guesswork, and missed leads add up fast. **Booking.com Email Scraper** automates the process by pulling emails associated with your keywords from publicly available Booking.com profile content. This **Booking.com email scraper** and **Booking.com scraper email** workflow is built for marketers and analysts who need a clean **Booking.com email list** without spending days hunting one by one. Use it to collect leads tied to your niche (like hotels and hospitality), then export for outreach or enrichment in minutes. In one run, you can collect up to your chosen `maxEmails` cap while the actor writes results continuously to your dataset. --- ### See the Data: Sample Output Here's a real record from a single run: ```json { "network": "Booking.com.com", "keyword": "hotel", "title": "Example property / profile title", "description": "Example bio text that includes contact info for hotel-related content.", "url": "https://www.booking.com/example-property", "email": "contact@example.com", "proxyGroups": ["cost-effective"], "status": "success" } ```` Below are the fields this actor pushes to the dataset: | Field | Type | What It Tells You | |---|---|---| | `network` | string | Identifies the data source network this record came from. | | `keyword` | string | The keyword that matched the source content for this email. | | `title` | string | The title text associated with the matched page. | | `description` | string | The snippet/biography text used to extract the email address. | | `url` | string | The source page URL where the email was found. | | `email` | string | The extracted email address (filtered by your `customDomains` setting). | | `proxyGroups` | array | Proxy configuration metadata captured with the record. | | `status` | string | A helpful success indicator you can use in downstream processing (see FAQ “What happens when the actor encounters an error?” for how to interpret partial runs). | | `error_message` | string | Error details if a record fails (for run-level issues, see the actor logs and FAQ). | Export your full dataset as JSON, CSV, or Excel from the Apify dashboard. *** ### Setting It Up Drop this into your `input.json` and you're ready to go: ```json { "keywords": ["hotel", "resort", "boutique"], "location": "Berlin", "platform": "Booking.com", "customDomains": ["@gmail.com", "@company.com"], "maxEmails": 50, "engine": "cost-effective", "proxyConfiguration": { "useApifyProxy": true } } ``` | Parameter | Required | What It Does | |---|---|---| | `keywords` | ✅ | A list of keywords the actor uses to find Booking.com profile content related to your niche. | | `location` | ⬜ | Filters results by a location string to narrow down the leads you collect. | | `platform` | ⬜ | Selects the target platform. Use `Booking.com` to match this actor’s purpose. | | `customDomains` | ⬜ | Restricts extracted emails to the domains you specify (for example, `@gmail.com`) so you get more targeted **Booking.com contact email scraper** outputs. | | `maxEmails` | ⬜ | Sets a cap on how many emails to collect; the actor stops once this limit is reached to control scraping time. | | `engine` | ⬜ | Chooses the scraping engine mode: `cost-effective` or `legacy` for different tradeoffs in speed and reliability. | | `proxyConfiguration` | ⬜ | Configure proxies for this Actor run (recommended for reliable scraping at scale). | | ↳ `proxy support` | ⬜ | If set in your proxy configuration, routes requests through Apify Proxy for better reliability. | *** ### What It Does Booking.com Email Scraper scrapes emails from Booking.com profile content using your keywords and email-domain filters, then writes each found record to the Apify dataset. #### Keyword-driven email discovery on Booking.com You provide `keywords` and (optionally) `location`, and the actor looks for publicly available Booking.com profile content related to those terms. It then extracts email addresses and records which keyword matched, which is useful for building a **Booking.com leads email scraper** workflow by theme. #### Domain-targeted extraction for cleaner leads With `customDomains`, you control which email addresses are considered relevant. That’s how this **Booking.com property email extractor** stays focused—collect only the domains you care about (for example, personal inboxes vs. company domains) instead of dumping mixed results. #### Clean output records for easy importing For every email it finds, Booking.com Email Scraper pushes a structured JSON object including `email`, the source `url`, plus `keyword`, `title`, and `description`. This makes it straightforward to run your own deduping, enrichment, and segmentation on top of the dataset. #### Built-in proxy support for reliable scraping The actor supports proxy configuration to improve scraping reliability, especially on larger jobs. This is particularly helpful when you’re running a **Booking.com hotel email scraping** campaign and need consistent throughput. #### Includes retries and fallbacks for resilience When results are sparse or blocked, the actor applies retry and fallback logic so you can get usable output even when conditions vary. If you’re building a **Booking.com email harvesting tool** for repeatable lead generation, this resilience helps reduce manual re-runs. Overall, Booking.com Email Scraper helps you turn publicly available Booking.com profile data into a practical **Booking.com email list** you can export and use immediately. *** ### Why Booking.com Email Scraper? There are plenty of ways to pull data from Booking.com—here’s why Booking.com Email Scraper stands out. #### Built for keyword-and-domain filtering (not raw scraping) The actor is designed around your `keywords` plus `customDomains`, so the results align with your outreach targeting from the start. If you’ve ever used a “spray and pray” scraper, you’ll appreciate how this **Booking.com scraper email** approach keeps your dataset cleaner. #### Output you can use in outreach workflows right away Each dataset row includes the extracted `email` alongside context fields like `url`, `title`, and `description`. That context makes it easier to validate leads and route them correctly in CRMs or spreadsheets. #### Reliable at scale with configurable engines and proxy support You can choose `engine` (`cost-effective` or `legacy`) and provide `proxyConfiguration` when needed. This makes Booking.com Email Scraper a strong fit whether you’re doing a quick test run or an **OTA booking email scraper** workflow for ongoing lead sourcing. *** ### Real-World Use Cases Here's how different teams put Booking.com Email Scraper to work: **Sales Development Teams**\ SDRs often start with a Booking.com shortlist, then spend hours hunting emails for follow-up. They run Booking.com Email Scraper with hospitality-focused keywords and a domain filter, then export the dataset to their pipeline with far less manual work. **Marketing Agencies**\ Agencies building lead lists for hotel and property campaigns need repeatable sourcing. They use keywords like “hotel” and related terms, limit results to business domains with `customDomains`, and quickly assemble a **Booking.com contact email scraper** dataset to hand off to their outreach partners. **Hotel Revenue Managers & Partnerships**\ Partnership outreach needs the right inboxes, not just page URLs. By using targeted keywords and filtering domains, they generate a usable **Booking.com email list** for deals, collaborations, and guest services outreach. **Data Analysts & Researchers**\ Researchers often want structured data they can analyze—keyword-by-keyword, domain-by-domain. They export the actor’s dataset fields (including `keyword` and the source `url`) and run their own cleaning and validation to support reports or studies. **Automation & Integration Specialists**\ Developers often need a reliable upstream dataset for a downstream automation. They schedule or trigger runs via the Apify API, store dataset exports, and connect the output into systems that enrich leads and trigger email sequences—without writing a custom **Booking.com email harvesting software** pipeline from scratch. *** ### How to Run It No code required. Here's how to get your first results in under 5 minutes: 1. **Open the actor on Apify** — go to [console.apify.com](https://console.apify.com) and find Booking.com Email Scraper. 2. **Enter your inputs** — set `keywords` (required), and optionally `location`, `customDomains`, `maxEmails`, and `engine`. 3. **Configure proxy settings (optional but recommended)** — set `proxyConfiguration` to match your reliability needs. 4. **Start the run** — launch the actor and monitor the live logs for progress. 5. **Open the Dataset tab** — results are written as the actor finds emails, so you can review records immediately. 6. **Export when ready** — download your results from the dataset as JSON, CSV, or Excel. 7. **Iterate if needed** — if results look low, broaden your keywords and/or add more domains in `customDomains`, then re-run. The whole setup takes under 5 minutes — results start appearing within seconds of launch. *** ### Export & Integration Options Once your data is collected, Booking.com Email Scraper fits directly into your existing workflow. You can download your results as JSON, CSV, or Excel directly from the Apify dataset tab. For office tools like Google Sheets or Airtable, you can use Apify’s export options or push the dataset into your system with Zapier/Make. If you need to automate end-to-end, Booking.com Email Scraper can be integrated via the Apify API. You can also trigger downstream actions using webhooks (for example, to enrich leads or update your CRM right after a run completes). *** ### Pricing Booking.com Email Scraper runs on Apify, which includes a **free tier** — no credit card needed to start. Free tier comes with platform credits on sign-up, which is typically enough for several real test runs. After that, it’s pay-as-you-go based on Actor compute units (CU), so you don’t get stuck with a monthly commitment if you only run occasionally. Subscription plans can apply for heavier, recurring workloads—check the Apify pricing page for current details. Start free at [apify.com](https://apify.com) — scale up when you need to. *** ### Reliability & Limitations | What We Handle | How | |---|---| | Proxy configuration | You can provide `proxyConfiguration` and choose an engine mode with `engine`. | | Retries and resilience | The actor applies retry and fallback behavior when results are sparse. | | Progress persistence | It saves progress and keeps track of already seen emails to avoid duplicates across a run. | | Data caps | The actor respects `maxEmails` and stops when the limit is reached. | | Partial run usability | Results are pushed as they’re found, so you can export partial data if you stop early. | Limitations: Booking.com Email Scraper collects emails from publicly available profile content, and it cannot guarantee that every search will return many contacts. If a Booking.com profile doesn’t include an email in the relevant publicly available text, it won’t be extracted—so results depend on what’s actually published. For enterprise-scale needs or custom configurations, reach out and we'll help. *** ### Frequently Asked Questions #### Is there a free plan? Yes. Apify offers a free tier with credits so you can run Booking.com Email Scraper on real data before scaling. #### Do I need to log in or create an account on Booking.com? No. Booking.com Email Scraper works with publicly available Booking.com profile content and does not require a Booking.com login. #### How accurate is the extracted data? The actor extracts email addresses it detects from the publicly available profile text it processes and filters them using your `customDomains`. Accuracy depends on what the profile owner has published. #### How many results can I get per run? You control the maximum output with `maxEmails`. The actor stops once this cap is reached, although it may return fewer emails if there aren’t matching contacts in the available content. #### How fresh is the data? Freshness depends on how recently the relevant Booking.com profile content was updated and how fast you run the actor after discovery. The actor only captures what’s publicly visible during the run. #### Is this legal? Does it comply with GDPR / CCPA? This actor is designed for collecting **publicly available data**. Whether your specific use (and storage/processing) complies with GDPR, CCPA, platform rules, and applicable anti-spam laws depends on your organization and how you handle the extracted data. #### Can I export to Google Sheets or Excel? Yes. You can export from the Apify dashboard as JSON, CSV, or Excel, and then import into Google Sheets or Excel as needed. #### Can I schedule this to run automatically? Yes. In Apify, you can schedule actors to run automatically on a cron schedule, which is useful for recurring lead sourcing. #### Can I access results via the API? Yes. Booking.com Email Scraper results are written to the Apify dataset, which you can access programmatically via the Apify API. #### What happens when the actor encounters an error? If an error occurs, the run may still return partial results because records are pushed as they’re found. Check the Apify run logs for details, and if output seems low, consider adjusting `keywords`, increasing the email domain coverage in `customDomains`, or re-running with a different `engine`. *** ### Get Help & Use Responsibly Got a question about Booking.com Email Scraper or a feature you'd like added? Reach out at . We welcome requests like improved keyword targeting and additional export-friendly fields, and we actively maintain this actor based on user feedback. **Booking.com Email Scraper collects publicly available data** and does not access private accounts, login-gated pages, or password-protected content. You are responsible for complying with GDPR, CCPA, applicable platform Terms of Service, and relevant anti-spam regulations when using or storing extracted data. 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": [ "hotel" ], "location": "", "platform": "Booking.com", "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": [ "hotel" ], "location": "", "customDomains": [ "@gmail.com" ] }; // Run the Actor and wait for it to finish const run = await client.actor("scrapeflux/booking-com-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": ["hotel"], "location": "", "customDomains": ["@gmail.com"], } # Run the Actor and wait for it to finish run = client.actor("scrapeflux/booking-com-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": [ "hotel" ], "location": "", "customDomains": [ "@gmail.com" ] }' | apify call scrapeflux/booking-com-email-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scrapeflux/booking-com-email-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Booking.com Email Scraper", "description": "📧 Booking.Com Email Scraper helps you extract hotel email contacts from Booking.com for targeted outreach. Automate lead building, sales, and market research with accuracy and speed. 🚀 Perfect for B2B teams & agencies.", "version": "0.0", "x-build-id": "NAJ5poJOcSWT2Awve" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scrapeflux~booking-com-email-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scrapeflux-booking-com-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scrapeflux~booking-com-email-scraper/runs": { "post": { "operationId": "runs-sync-scrapeflux-booking-com-email-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scrapeflux~booking-com-email-scraper/run-sync": { "post": { "operationId": "run-sync-scrapeflux-booking-com-email-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "hotel" ], "items": { "type": "string" } }, "location": { "title": "Location", "type": "string", "description": "Location to filter search results.", "default": "" }, "platform": { "title": "Platform", "enum": [ "Booking.com" ], "type": "string", "description": "Select platform.", "default": "Booking.com" }, "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 } } } } } } } } } } ```