# Doordash Email Scraper (`scraperoka/doordash-email-scraper`) Actor 📧 DoorDash Email Scraper extracts business email addresses from DoorDash listings fast and accurately. 🚀 Perfect for B2B lead generation, partnerships, and outreach—save time, boost conversions, and reach the right decision-makers. - **URL**: https://apify.com/scraperoka/doordash-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 ### Doordash Email Scraper 🎯 Manually visiting pages to collect DoorDash contact emails wastes hours you don’t have. **Doordash Email Scraper** extracts email addresses at scale using your chosen keywords and domain filters. This Doordash Email Scraper (DoorDash email scraper, DoorDash marketing email extraction) is ideal for marketers, recruiters, and growth teams who need DoorDash lead generation emails quickly—potentially thousands of records in a single run. --- ### What You Get: Sample Output Here's a sample record from a single run: ```json { "network": "Doordash", "keyword": "pizza", "title": "Community Partner — Pizza Nights", "description": "Order from us | Email: hello@brandpizza.com", "url": "https://www.doordash.com/some-partner-page", "email": "hello@brandpizza.com", "proxyGroups": [ "RESIDENTIAL" ] } ```` **Output fields (what gets pushed into your dataset):** | Field | Type | What It Tells You | |---|---|---| | `network` | string | Confirms the source network as `Doordash` for reporting | | `keyword` | string | Which keyword your run used to discover the contact | | `title` | string | The page title associated with the result (useful for context) | | `description` | string | The text snippet where the email was found (great for auditing) | | `url` | string | The result link related to the extracted email | | `email` | string | The extracted email address (the main value for outreach) | | `proxyGroups` | array | The proxy group configuration used for the run | Export your dataset as JSON, CSV, or Excel — straight from the Apify dashboard. *** ### Why Doordash Email Scraper? There are a lot of ways to pull contact data from DoorDash-related content — here’s what sets **Doordash Email Scraper** (DoorDash email finder tool, DoorDash customer email list) apart. #### Keyword-driven discovery for targeted outreach Doordash Email Scraper uses your `keywords` to focus extraction on the most relevant DoorDash-related bios/posts. Pairing this with `customDomains` helps you collect outreach-friendly emails from domains you actually care about (for example, `@gmail.com`). That makes DoorDash outreach email list building feel much more intentional than generic scraping. #### Controlled harvesting with a clear `maxEmails` cap You can set `maxEmails` to stop once you’ve collected enough results. This helps you manage runtime and cost by preventing unexpectedly long runs while still searching broadly enough to find useful DoorDash marketing email extraction opportunities. #### Deduplicated email collection across the run The actor tracks `seen_emails` so it doesn’t push the same email address repeatedly. This keeps your DoorDash email database cleaner and makes it easier to import into CRMs and outreach tools without extra cleanup. #### Built for resilience with practical fallbacks If a scraping path yields no results, the actor attempts a fallback strategy using an alternative proxy configuration so you can get results more consistently. In other words, it’s designed to keep going rather than fail silently. *** ### Configuring Your Run Drop this into your `input.json` to get started: ```json { "keywords": ["pizza", "new york"], "location": "New York, NY", "platform": "Doordash", "customDomains": ["@gmail.com"], "maxEmails": 50, "proxyConfiguration": { "useApifyProxy": true } } ``` | Parameter | Required | What It Does | |---|---|---| | `keywords` | ✅ | A list of keywords to search for. These drive where the actor looks for matching DoorDash bios/posts and emails | | `location` | ⬜ | Location to filter search results (left empty means no location filter) | | `platform` | ⬜ | Select platform. The only supported value is `Doordash` | | `customDomains` | ⬜ | List of custom email domains to target (for example `@gmail.com`) | | `maxEmails` | ⬜ | Maximum number of emails to collect. The actor stops once this limit is reached | | `proxyConfiguration` | ⬜ | Configure proxies for this Actor run (use Apify Proxy settings for more reliable scraping) | | ↳ `proxy support` | ⬜ | When set, routes the run through Apify Proxy for better reliability | *** ### Core Capabilities #### Keyword-based DoorDash email extraction Doordash Email Scraper searches for emails that appear in publicly available DoorDash bios and posts related to your chosen `keywords`. This makes it easier to build a DoorDash outreach email list that matches your niche instead of collecting random contacts. #### Domain targeting with `customDomains` You can restrict extraction to specific email domains using `customDomains`. This is especially useful when you want DoorDash lead generation emails from trusted consumer providers or specific company domains, improving the relevance of your outreach. #### Resumable progress for long runs The actor persists progress (including already `seen_emails` and cursor state) so you can safely resume after interruptions. For large DoorDash scrape emails jobs, this reduces wasted effort when you need to restart. #### Incremental dataset writing Each time an email is found, the actor pushes a result row into your dataset immediately. That means your results are available as they’re collected—ideal for data analysts and growth teams who want fast iteration. #### Scale controls with `maxEmails` With `maxEmails`, you can bound output volume to match your budget, timeline, and processing capacity. This keeps your DoorDash email database exports predictable, even when you’re running multiple keyword and domain combinations. *** ### Who Gets the Most Out of This Here's how different teams put Doordash Email Scraper to work: **Sales Development Representatives** — Use DoorDash Email Scraper to quickly turn niche keywords into a ready-to-import contact list, focusing on the email domains you prefer. The result is more time spent on outreach and less time hunting for DoorDash contacts email extraction manually. **Marketing Ops & Demand Gen Teams** — Build a DoorDash customer email list aligned to campaign themes by steering discovery with `keywords` and `customDomains`. This supports DoorDash marketing email extraction workflows where relevance matters as much as volume. **Recruiters & Staffing Researchers** — Use the extracted emails for role-specific outreach lists tied to DoorDash bios/posts. You can iterate faster by adjusting keywords and domains instead of repeatedly searching for new leads. **Freelance Researchers** — Deliver structured datasets (from DoorDash email finder tool outputs) for client briefs, including the context fields (`title`, `description`, and `url`) needed to validate findings. It’s a practical way to produce DoorDash email database outputs on a deadline. **Automation & Integration Specialists (Technical)** — Trigger Doordash Email Scraper via the Apify API and pipe dataset records into your downstream pipeline. The consistent JSON rows make it straightforward to integrate DoorDash scrape emails into CRMs, lead scoring, and enrichment steps. *** ### Step-by-Step: How to Use It No coding needed. Here's how to run Doordash Email Scraper from start to finish: 1. **Open the actor on Apify** — go to [console.apify.com](https://console.apify.com) and find **Doordash Email Scraper**. 2. **Enter your inputs** — set `keywords` (required), and optionally `location`, `customDomains`, and `maxEmails` in the input panel. 3. **Configure proxy settings** — use `proxyConfiguration` for best reliability (for example, enabling Apify Proxy). 4. **Hit Run and watch the live log** — monitor progress while the actor searches and pushes results incrementally. 5. **View results in the dataset tab** — open the dataset to see each extracted email record as it arrives. 6. **Export as JSON, CSV, or Excel** — download from the Apify dashboard in the format that fits your workflow. The whole process takes under 5 minutes to set up. *** ### Integrations & Export Options Once your data is collected, Doordash Email Scraper plugs directly into your existing workflow. Export your dataset as JSON, CSV, or Excel from the Apify dashboard, then import into your CRM or spreadsheet for analysis. You can also use Apify’s API to access results programmatically, and integrate with automations via tools like Zapier/Make/webhooks (for example, to notify Slack or trigger downstream enrichment). For implementation details, refer to the Apify documentation and the actor’s dataset outputs. *** ### Pricing & Free Trial Doordash Email Scraper runs on the Apify platform, which offers a **free tier** — no credit card required to get started. You’ll typically pay as you go based on Apify platform usage, and you can scale up once your keyword/domain strategy proves out. For the latest details on plan limits and compute costs, check the Apify pricing page on apify.com. Start for free at [apify.com](https://apify.com) and scale when you're ready. *** ### Reliability & Performance | What We Handle | How | |---|---| | Rate limits & access issues | Uses built-in retry and fallback behavior to keep the job progressing | | Proxy-based reliability | Supports proxy configuration to improve scraping stability | | Duplicate email noise | Deduplicates by tracking `seen_emails` during the run | | Long searches | Persists progress so you can resume with less wasted time | | Dataset completeness | Pushes results incrementally as emails are found | **Limitations:** Results depend on what email addresses are publicly present in DoorDash bios/posts that match your `keywords` and `customDomains`. Private or login-gated content isn’t accessible via this actor, so you may need broader keywords or more domains if you see low volume. For enterprise-scale runs, contact us to discuss custom configurations. *** ### Frequently Asked Questions #### Is there a free plan or trial? Yes. Apify offers a **free tier** with usage credits so you can run tests before committing to larger jobs. You can run smaller keyword/domain combinations to validate results quickly. #### Do I need to log in to Doordash to use this? No. This actor scrapes emails from publicly available DoorDash bios and posts related to your keywords. No login is required to run the actor. #### How accurate is the data? Accuracy depends on what email addresses are actually present in the publicly available text the actor processes. The actor extracts emails that match your `customDomains` and collects contextual fields like `title`, `description`, and `url` for verification. #### How many results can I get per run? You control volume with `maxEmails`, which is the maximum number of emails the scraper will collect before stopping. Note that a higher limit doesn’t guarantee reaching that exact number—available matches determine output. #### How often is the data updated / how fresh is it? Freshness reflects what’s publicly available at run time. If you re-run later with updated keywords or domains, you may find new or changed emails that appear in DoorDash bios/posts. #### Is this legal? Does it comply with GDPR / CCPA? This actor works with **publicly available data**, but legal compliance (including GDPR/CCPA and platform Terms of Service) is your responsibility. If you plan to store, process, or market to individuals, confirm your use case is compliant for your region and policies. #### Can I export results to Google Sheets or Excel? Yes. You can download your dataset in common formats (including JSON, CSV, or Excel) from the Apify dashboard, then import into Google Sheets or Excel. You can also automate transfers via integrations supported by Apify. #### Can I run this on a schedule automatically? Yes. You can schedule actor runs on Apify using Apify’s scheduling options. This is useful if you want to refresh your DoorDash email database periodically. #### Can I access this via API? Yes. You can trigger runs and access dataset results programmatically via the Apify API. Use this if you want your DoorDash email finder tool outputs to feed directly into your systems. #### What happens if the actor hits an error? The actor is designed to be resilient, and it writes results incrementally as it finds emails. If a particular run fails partway through, you can typically resume without losing the already collected records due to persisted progress. *** ### Need Help or Have a Request? Got a question about Doordash Email Scraper or want a new feature added? Reach out at . We welcome feedback and can discuss enhancements like webhook notifications on completion or batch CSV upload for keyword/domain sets. The actor is actively maintained based on user needs. *** ### Disclaimer & Responsible Use *Doordash Email Scraper is the fastest, most reliable way to build a focused DoorDash outreach email list — start your free run today.* This actor collects **publicly available data** and does not access private accounts, login-gated content, or password-protected pages. You are responsible for ensuring your use of extracted data complies with GDPR, CCPA, platform Terms of Service, and any applicable laws. 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. ## `proxyConfiguration` (type: `object`): Configure proxies for this Actor. ## Actor input object example ```json { "keywords": [ "pizza", "new york" ], "location": "", "platform": "Doordash", "customDomains": [ "@gmail.com" ], "maxEmails": 20 } ``` # 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": [ "pizza", "new york" ], "location": "", "customDomains": [ "@gmail.com" ] }; // Run the Actor and wait for it to finish const run = await client.actor("scraperoka/doordash-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": [ "pizza", "new york", ], "location": "", "customDomains": ["@gmail.com"], } # Run the Actor and wait for it to finish run = client.actor("scraperoka/doordash-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": [ "pizza", "new york" ], "location": "", "customDomains": [ "@gmail.com" ] }' | apify call scraperoka/doordash-email-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scraperoka/doordash-email-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Doordash Email Scraper", "description": "📧 DoorDash Email Scraper extracts business email addresses from DoorDash listings fast and accurately. 🚀 Perfect for B2B lead generation, partnerships, and outreach—save time, boost conversions, and reach the right decision-makers.", "version": "0.0", "x-build-id": "FP0Qre5f8wMEsxPyE" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scraperoka~doordash-email-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scraperoka-doordash-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scraperoka~doordash-email-scraper/runs": { "post": { "operationId": "runs-sync-scraperoka-doordash-email-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scraperoka~doordash-email-scraper/run-sync": { "post": { "operationId": "run-sync-scraperoka-doordash-email-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "pizza", "new york" ], "items": { "type": "string" } }, "location": { "title": "Location", "type": "string", "description": "Location to filter search results.", "default": "" }, "platform": { "title": "Platform", "enum": [ "Doordash" ], "type": "string", "description": "Select platform.", "default": "Doordash" }, "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 }, "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 } } } } } } } } } } ```