# Walmart Email Scraper Fast Advanced And Cheapest (`scrapers-hub/walmart-email-scraper-fast-advanced-and-cheapest`) Actor 🚀 Walmart Email Scraper “Fast Advanced & Cheapest” extracts Walmart customer emails quickly with advanced targeting. ⚡ Built for efficiency and low cost—ideal for marketing, lead gen, and outreach. 📩 Start growing your campaigns today! - **URL**: https://apify.com/scrapers-hub/walmart-email-scraper-fast-advanced-and-cheapest.md - **Developed by:** [Scrapers Hub](https://apify.com/scrapers-hub) (community) - **Categories:** Lead generation, E-commerce, Automation - **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/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 ### Walmart Email Scraper - Fast, Advanced and Cheapest 📬 **Walmart Email Scraper - Fast, Advanced and Cheapest** is an Apify actor that extracts email addresses from Walmart based on the keywords and filters you choose. If you’re looking for a **walmart email scraper**, a **walmart email finder**, or a **walmart leads email scraper** to help build a targeted list, this tool makes it much easier—whether you’re doing outreach, research, or enrichment. Whether you’re a marketer, recruiter, or data enthusiast, you can use this **walmart business email scraping tool** to save hours of manual searching while collecting structured results at scale. --- ### Why choose Walmart Email Scraper - Fast, Advanced and Cheapest? | Feature | Benefit | |---|---| | ✅ Keyword-based extraction | Find relevant Walmart contacts using your own keyword set (great for **walmart email list builder** workflows) | | ✅ Domain filtering (`customDomains`) | Focus results on email providers/domains you care about (e.g., `@gmail.com`, `@yahoo.com`) | | ✅ Location filtering (`location`) | Narrow results when you want region-specific targeting (for better **walmart contact email scraper** outcomes) | | ✅ Controlled volume (`maxEmails`) | Set a maximum number of emails to collect to manage runtime and cost predictably | | ✅ Reliability with resume support | Resumes progress using saved cursor state so long runs don’t have to restart | | ✅ Structured dataset output | Exports clean records into the **Walmart Emails Dataset** with fields like `keyword`, `url`, and `email` | --- ### Key features - 📌 **Accurate email extraction**: Extracts email addresses from publicly available Walmart-related pages using your `customDomains` filters - 🔎 **Keyword flexibility**: Use a list of `keywords` (e.g., job titles like “manager”, “founder”) to broaden coverage for **walmart supplier email scraper** use cases - 🌐 **Custom email-domain targeting**: Configure `customDomains` (such as `@gmail.com` or `@yahoo.com`) to match the contact types you want - 📍 **Optional location filtering**: Add `location` to help narrow results for **walmart email scraper software** buyers who care about geography - 💾 **Incremental saving**: Each found email is pushed immediately into the dataset - 🔄 **Resumable progress**: Saves a cursor and previously seen emails so you can continue later without losing what you already collected - 🛡️ **Resilient pagination + fallbacks**: Includes stop conditions and logic to handle pages with no new results, helping long runs finish gracefully - 🎯 **Limit-aware runs**: Stops when `maxEmails` is reached (and free runs enforce an upper cap) --- ### Input Provide input via an `input.json` file. Example structure: ```json { "keywords": ["manager", "founder"], "location": "Dallas, TX", "customDomains": ["@gmail.com", "@yahoo.com"], "maxEmails": 20 } ```` #### Input Fields | Field | Required | Description | |---|---|---| | `keywords` | Yes | A list of keywords (or queries) you want to use for email extraction from Walmart. This directly drives what contacts get discovered. | | `location` | No | A location string used to filter results. Leave it blank if you don’t need geo targeting. | | `customDomains` | No | A list of email domains (like `@gmail.com` or `@yahoo.com`). The scraper extracts emails that match these domains. | | `maxEmails` | No | Maximum number of emails to collect. The scraper stops once the limit is reached. Higher limits can take longer and don’t guarantee reaching the target. Minimum is `1`, maximum is `10000`. | *** ### Output The actor saves each lead’s data in JSON format into the **Walmart Emails Dataset** (table view: “Scraped Leads”). Example record: ```json { "network": "Walmart.com", "keyword": "manager", "title": "No title", "description": "No data", "url": "No URL", "email": "example@gmail.com" } ``` #### Output Fields | Field | Type | Description | |---|---|---| | `network` | string | The source network label for the record (set to `Walmart.com`). | | `keyword` | string | The keyword used for the current extraction. | | `title` | string | Title text associated with the extracted item (or `No title`). | | `description` | string | Description text associated with the extracted item (or `No data`). | | `url` | string | Link/URL associated with the extracted item (or `No URL`). | | `email` | string | The extracted email address. | > Note: The dataset is filled incrementally as results are found. The transformation/display for the dataset focuses on: `keyword`, `title`, `description`, `url`, and `email`. *** ### How to use Walmart Email Scraper - Fast, Advanced and Cheapest (via Apify Console) 1. **Open Apify Console**\ Log in at [console.apify.com](https://console.apify.com) and go to the Actors section. 2. **Find this actor**\ Search for **Walmart Email Scraper - Fast, Advanced and Cheapest** and open the actor page. 3. **Go to the INPUT section**\ Use the input form (or switch to raw JSON) and enter your `keywords`. 4. **Add optional filters (recommended)**\ Set `customDomains` to the email domains you want (for example `@gmail.com`) and optionally set `location` for geo targeting. 5. **Control the volume**\ Set `maxEmails` to define when the run stops. If you leave it as the default, you’ll get fewer results faster. 6. **Run the actor**\ Click **Run**. During execution, you’ll see logs for progress and found emails, and the actor will continue until it reaches your email limit or its stopping conditions. 7. **Review results in the dataset**\ After the run, open the **Walmart Emails Dataset** and view **Scraped Leads** (a table with `keyword`, `title`, `description`, `url`, `email`). 8. **Export to your workflow**\ Export the dataset as JSON/CSV from the dataset UI for use in outreach tooling, spreadsheets, or analytics pipelines. No coding required—get accurate Walmart email list building results in minutes with this **walmart email scraper**. *** ### Advanced features & SEO optimization - ⚡ **Engineered for targeted Walmart email harvesting**: Built specifically to help with **walmart email finder** and **walmart leads email scraper** workflows using your `keywords` and `customDomains` filters. - ⏱️ **Timeout-friendly scaling**: Larger searches or higher limits can take longer; you can extend the timeout in **Run Options** (default: 3600 seconds / 1 hour). - 🔁 **Resumable, cursor-based recovery**: If a run stops early, the actor uses saved progress state (cursor + `seen_emails`) to continue without duplicating collected emails. - 🧠 **Practical tuning guidance when results are limited**: If you don’t get enough matches, broaden keywords, add similar terms, or include more domains—common improvements for **walmart supplier email scraper** accuracy. - 📊 **Structured outputs for faster analysis**: Every extracted email record includes `keyword`, `url`, and text fields (`title`, `description`)—handy for downstream enrichment and **walmart marketing email scraper** campaigns. *** ### Best use cases - 📈 **Sales lead generation**: Quickly build a **walmart email list builder** with contact emails linked to relevant Walmart pages for outreach. - 🎯 **Targeted B2B prospecting**: Use `customDomains` and keyword variations to focus on the email types you want when searching for **walmart supplier email scraper** leads. - 🧾 **Data enrichment & cleanup**: Export structured fields (`keyword`, `email`, `url`) and enrich records in your CRM or spreadsheets. - 🔬 **Market research**: Compare outreach targets by keyword and review page context using `title` and `description` to understand what each email is associated with. - ✉️ **Email marketing sourcing**: Assemble lists for campaign segmentation with a clear provenance (`keyword`, source page `url`)—useful for a **walmart marketing email scraper** flow. - 💻 **Automation pipeline integration**: Feed the dataset records into your automation jobs for deduplication and routing to outreach systems. - 🗺️ **Regional targeting**: Add `location` when you want region-specific contact discovery for more relevant **walmart contact email scraper** campaigns. *** ### Technical specifications - **Supported Input Formats** - ✅ `keywords` as an array - ✅ `location` as a string - ✅ `customDomains` as an array of email domains (e.g. `@gmail.com`) - ✅ `maxEmails` as an integer (min `1`, max `10000`) - **Proxy Support** - ✅ Built-in proxy support for reliable scraping - **Retry Mechanism** - ✅ Includes retries and fallbacks for resilience when fetching pages - **Dataset Structure** - ✅ Dataset: **Walmart Emails Dataset** - ✅ View: **Scraped Leads** - ✅ Stored fields (as displayed/transformed): `keyword`, `title`, `description`, `url`, `email` - **Rate Limits & Performance** - ✅ Runtime scales with search size and your selected `maxEmails` - ✅ Bigger searches/higher limits can take longer; adjust timeout in **Run Options** if needed - **Limitations** - ❌ Results depend on publicly available sources and the presence of matching email domains in extracted text - ⚠️ Free user runs enforce a result cap (maximum 100 emails) *** ### FAQ #### What does Walmart Email Scraper - Fast, Advanced and Cheapest extract? ✅ It extracts email addresses from Walmart based on the `keywords` you provide and optional filters like `customDomains` and `location`. The actor saves each result as a dataset record including `email`, `keyword`, `url`, `title`, and `description`. #### Do I need to provide a location? ❌ No. `location` is optional. If you leave `location` blank, the actor runs without location filtering, which can be helpful when you want broader coverage for a **walmart email list builder**. #### How do `customDomains` affect the results? ✅ `customDomains` is used to filter which email addresses get extracted. If you want only certain providers (for example `@gmail.com` or `@yahoo.com`), you should include those domains in `customDomains` to improve relevance for a **walmart email finder** workflow. #### Can I control how many emails I collect? ✅ Yes. Use `maxEmails` to set a maximum number of emails to collect. The actor stops once the limit is reached, which helps manage runtime and cost. #### Why am I getting fewer results than expected? ❌ This usually happens when matches are limited by your `keywords`, your selected `customDomains`, or optional `location`. Try wider keywords, add similar terms, or include more email domains—common adjustments for **walmart supplier email scraper** accuracy. #### Is this output ready for CRM or spreadsheet use? ✅ Yes. The dataset stores structured records with the key fields `keyword`, `title`, `description`, `url`, and `email`, which makes exporting and importing straightforward for analysts and marketers building **walmart leads email scraper** lists. #### Can I resume a run if it stops? ✅ Yes. The actor saves progress and tracks `seen_emails` with a cursor-based resume mechanism, helping you continue without duplicating already collected emails. *** ### Support & feature requests Want to improve **Walmart Email Scraper - Fast, Advanced and Cheapest** for your next **walmart email scraper software** project? Share your ideas! - 💡 **Feature Requests**: Ideas like better CSV-friendly exports, additional filters, or improved output formatting are welcome. - 📧 **Contact**: Reach out at . Your feedback directly shapes what gets improved next for this **walmart email scraper** actor. *** - *Walmart Email Scraper - Fast, Advanced and Cheapest is a fast, structured way to build a targeted Walmart contact email list—optimized for real-world lead generation and analysis.*\ *If you want the most comprehensive Walmart email scraping workflow, this actor is built to help you get there efficiently.*\* *** ### Disclaimer **This tool only accesses publicly accessible sources.** It does not access private profiles, authenticated data, or password-protected pages. Legal compliance (including GDPR, CCPA, spam regulations, and relevant platform terms) is your responsibility. For data removal requests, contact . Use this actor responsibly, ethically, and for legitimate purposes only. # Actor input Schema ## `keywords` (type: `array`): A list of keywords or queries to search for. ## `location` (type: `string`): Location to filter search results. ## `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. ## Actor input object example ```json { "keywords": [ "manager", "founder" ], "location": "", "customDomains": [ "@gmail.com", "@yahoo.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": [ "manager", "founder" ], "location": "", "customDomains": [ "@gmail.com", "@yahoo.com" ] }; // Run the Actor and wait for it to finish const run = await client.actor("scrapers-hub/walmart-email-scraper-fast-advanced-and-cheapest").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": [ "manager", "founder", ], "location": "", "customDomains": [ "@gmail.com", "@yahoo.com", ], } # Run the Actor and wait for it to finish run = client.actor("scrapers-hub/walmart-email-scraper-fast-advanced-and-cheapest").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": [ "manager", "founder" ], "location": "", "customDomains": [ "@gmail.com", "@yahoo.com" ] }' | apify call scrapers-hub/walmart-email-scraper-fast-advanced-and-cheapest --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scrapers-hub/walmart-email-scraper-fast-advanced-and-cheapest", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Walmart Email Scraper Fast Advanced And Cheapest", "description": "🚀 Walmart Email Scraper “Fast Advanced & Cheapest” extracts Walmart customer emails quickly with advanced targeting. ⚡ Built for efficiency and low cost—ideal for marketing, lead gen, and outreach. 📩 Start growing your campaigns today!", "version": "1.0", "x-build-id": "EJIYaprvcbG2KRhuU" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scrapers-hub~walmart-email-scraper-fast-advanced-and-cheapest/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scrapers-hub-walmart-email-scraper-fast-advanced-and-cheapest", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scrapers-hub~walmart-email-scraper-fast-advanced-and-cheapest/runs": { "post": { "operationId": "runs-sync-scrapers-hub-walmart-email-scraper-fast-advanced-and-cheapest", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scrapers-hub~walmart-email-scraper-fast-advanced-and-cheapest/run-sync": { "post": { "operationId": "run-sync-scrapers-hub-walmart-email-scraper-fast-advanced-and-cheapest", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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 or Queries", "type": "array", "description": "A list of keywords or queries to search for.", "default": [ "manager", "founder" ], "items": { "type": "string" } }, "location": { "title": "Location", "type": "string", "description": "Location to filter search results.", "default": "" }, "customDomains": { "title": "Enter Custom Email Domains (e.g. @gmail.com, @yahoo.com)", "type": "array", "description": "List of custom email domains", "default": [ "@gmail.com", "@yahoo.com" ], "items": { "type": "string" } }, "maxEmails": { "title": "Enter 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 } } }, "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 } } } } } } } } } } ```