# Amazon Email Scraper Fast Advanced And Cheapest (`scrapers-hub/amazon-email-scraper-fast-advanced-and-cheapest`) Actor 🚀 Get verified Amazon seller emails fast with Amazon Email Scraper: Fast Advanced & Cheapest. 📩 Built for B2B outreach, lead gen, and sales prospecting. ⚡ Efficient scraping with advanced filters—save time and costs. - **URL**: https://apify.com/scrapers-hub/amazon-email-scraper-fast-advanced-and-cheapest.md - **Developed by:** [Scrapers Hub](https://apify.com/scrapers-hub) (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 $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 ### Amazon Email Scraper - Fast, Advanced and Cheapest 📬 **Amazon Email Scraper - Fast, Advanced and Cheapest** is an Apify actor that extracts email addresses from Amazon based on the keywords and email-domain filters you choose. If you’re looking for an **Amazon email scraper tool** (including an **Amazon seller email finder**, **Amazon outreach email list**, or **Amazon lead email extractor**), this actor helps you turn public web data into a structured dataset—saving you hours of manual work. Whether you’re a marketer building an **Amazon email harvesting software** workflow, a researcher exploring B2B lead generation from Amazon emails, or a data analyst compiling outreach targets, this tool gives you results at scale with practical controls. --- ### Why choose Amazon Email Scraper - Fast, Advanced and Cheapest? | Feature | Benefit | | --- | --- | | ✅ **Email extraction from Amazon using your keywords** | Focuses results on the people you actually want to contact using targeted search keywords | | ✅ **Custom email-domain filtering** | Improves lead relevance by limiting output to domains you specify (for example `@gmail.com`) | | ✅ **Max emails limit to control run size** | Helps you manage scraping time and costs by stopping once you reach your target | | ✅ **Resume support** | Continues from where it left off using saved progress and already-seen emails | | ✅ **Structured dataset output** | Produces consistent records you can export and analyze (dataset table view is ready) | | ✅ **Built-in reliability checks** | Uses resilience strategies (progress saving, handling empty results pages, and safe stopping conditions) | --- ### Key features - 🔍 **Keyword-driven Amazon email extraction**: Uses your provided `keywords` to drive what contacts are discovered and collected - 🎯 **Email-domain targeting for better match rate**: Filters extracted emails by `customDomains` (e.g. `@gmail.com`, `@yahoo.com`) - 🛡️ **Resilient progress & resume**: Saves progress and deduplicates by `seen_emails` so reruns pick up cleanly - ⏱️ **Controlled output with `maxEmails`**: Stops when the limit is reached so you can run fast Amazon email scraper batches - 💾 **Dataset-ready structured records**: Each lead is pushed with consistent fields like `keyword`, `title`, `url`, and `email` - 🔄 **Handles low-yield runs gracefully**: Stops when there are multiple consecutive pages with no new emails - 🌐 **Location filtering (optional)**: Lets you restrict results using a `location` filter when you want region-specific outreach - 📊 **Incremental saving**: Each found lead is pushed to the dataset as soon as it’s extracted --- ### Input Provide input via an `input.json` file. Example structure: ```json { "keywords": ["manager", "founder"], "location": "", "customDomains": ["@gmail.com", "@yahoo.com"], "maxEmails": 20 } ```` #### Input Fields | Field | Required | Description | | --- | --- | --- | | `keywords` | Yes | A list of keywords (queries) to search for and extract matching Amazon contact emails from. | | `location` | No | Location to filter results. Leave it empty if you don’t need regional targeting. | | `customDomains` | No | List of email domains to keep (for example `@gmail.com` or `@yahoo.com`). This improves relevance for outreach. | | `maxEmails` | No | Maximum number of emails to collect. The run stops once this limit is reached. Higher limits can take longer and don’t guarantee you’ll always reach the exact number. | *** ### Output The actor saves each lead record in JSON format to the **Amazon Emails Dataset** with an up-to-date dataset table view. Example output record: ```json [ { "network": "Amazon.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 used for each record (`"Amazon.com"`). | | `keyword` | string | The keyword from your input that led to this record. | | `title` | string | Title text extracted from the result item (or `"No title"` when missing). | | `description` | string | Extracted description text from the result item (or `"No data"` when missing). | | `url` | string | The associated link URL for the record (or `"No URL"` when missing). | | `email` | string | The extracted email address that matches your domain filters. | > Export note: You can export the dataset from Apify Console into common formats like JSON or CSV depending on your workspace setup. *** ### How to use Amazon Email Scraper - Fast, Advanced and Cheapest (via Apify Console) 1. **Open Apify Console**\ Go to [console.apify.com](https://console.apify.com) and open the actor listing for **Amazon Email Scraper - Fast, Advanced and Cheapest**. 2. **Set up your INPUT**\ In the **Input** tab, provide values for `keywords`.\ You can also add `location`, `customDomains`, and `maxEmails`. 3. **Add keywords for your target roles**\ Use terms like `manager` or `founder` (or other role/industry terms) to power an **Amazon email scraper tool** style workflow. 4. **Filter by email domains (recommended)**\ Add domains in `customDomains` such as `@gmail.com` or `@yahoo.com` to build a more precise **Amazon outreach email list**. 5. **Choose a limit with `maxEmails`**\ Set `maxEmails` to control how many emails you want to collect per run. If you need large batches, use higher values (runs may take longer). 6. **Start the run**\ Click **Run**. During execution, watch the actor logs for progress and newly found emails. 7. **Review results in the dataset**\ After completion, open the **OUTPUT** → dataset view titled **Scraped Leads** (inside **Amazon Emails Dataset**). You’ll see records with `keyword`, `title`, `url`, and `email` in a table. 8. **Export and use for outreach or analysis**\ Export the dataset to your CRM, spreadsheets, or analytics pipeline. This is ideal for **B2B lead generation from Amazon emails** and building structured **Amazon seller database email** lists. No coding required — get accurate results in minutes with this SEO-optimized **Amazon Email Scraper - Fast, Advanced and Cheapest** actor. ✅ *** ### Advanced features & SEO optimization - ⚙️ **Resume and deduplication**: The actor saves progress and keeps a `seen_emails` set so reruns avoid duplicates and can continue after interruptions—perfect for a **fast Amazon email scraper** workflow. - 🧠 **Improved targeting with domain filters**: Using `customDomains` helps the actor focus on the email extensions you care about, which is key to an **advanced Amazon email scraper** approach for outreach quality. - 🧾 **Structured dataset for downstream use**: Output fields are consistent, making it easy to power **Amazon email harvesting software** integrations into spreadsheets, databases, or data analysis tools. - ⏳ **Stop conditions to prevent endless runs**: The run stops after reaching `maxEmails` or when it detects multiple pages with no new emails—helpful when building **Amazon email scraper for outreach** campaigns. - 📍 **Optional location filtering**: If you want region-specific lead generation, use `location` to refine results. *** ### Best use cases - 📈 **Sales teams building outreach lists**: Collect contact emails for targeted roles and brands discovered from Amazon listings for faster follow-up. - 🧪 **Researchers validating lead sources**: Analyze how keyword + domain filtering changes results quality when scraping emails from Amazon listings. - 👩‍💼 **Recruiters sourcing candidate/contact references**: Extract emails tied to job-title-like keywords to expand outreach pools. - 🔎 **Data analysts enriching datasets**: Combine extracted emails with other datasets by `keyword` and `url` to evaluate lead coverage. - 🏷️ **B2B marketers launching campaigns**: Create an **Amazon outreach email list** and segment leads by domain preference for better deliverability planning. - 🤖 **Automation builders integrating leads into pipelines**: Use dataset export as an input to CRM imports, enrichment flows, or reporting dashboards. - 💡 **Founder-led growth teams**: Quickly test messaging and offer angles using a structured **Amazon lead email extractor** output. *** ### Technical specifications - **Supported Input Formats** - ✅ `keywords`: array of strings (required) - ✅ `location`: string (optional) - ✅ `customDomains`: array of strings (optional) - ✅ `maxEmails`: integer (optional, minimum `1`, maximum `10000`, default `20`) - **Proxy Support** - ✅ Built-in proxy support for reliable scraping behavior - **Retry Mechanism** - ✅ Includes resilience and stopping logic for low-yield or blocked situations (with progress-saving) - **Dataset Structure** - ✅ Dataset title: **Amazon Emails Dataset** - ✅ View: **Scraped Leads** - ✅ Fields (transformation + display) include: `keyword`, `title`, `description`, `url`, `email` (plus `network` in pushed records) - **Rate Limits & Performance** - ✅ Performance depends on run size, the number of keywords/domains, and the `maxEmails` limit - **Limitations** - ❌ Results depend on **publicly available data** and whether emails appear in those sources - ❌ Higher `maxEmails` can increase run time, and the actor does not guarantee it will always reach the target number if fewer emails are available *** ### FAQ #### Can I run Amazon Email Scraper - Fast, Advanced and Cheapest with just keywords? ✅ Yes. The only required input is `keywords`. You can leave `location`, `customDomains`, and `maxEmails` at their defaults to get started quickly. #### What email types does it collect from Amazon? ✅ It extracts email addresses from Amazon based on your `customDomains` filters and stops when the `maxEmails` limit is reached (if provided). #### How do `customDomains` affect the results? ✅ `customDomains` limits which email addresses are considered valid for output. For example, if you include `@gmail.com`, only emails matching those domains will be pushed to the dataset. #### Does it save progress if the run is interrupted? ✅ Yes. The actor saves progress (including already-seen emails) so you can resume without re-collecting duplicates. #### Is there a maximum number of emails per run? ✅ Yes, you control it with `maxEmails`. The actor stops when it reaches that limit. If you don’t set it, it uses the default value from the input schema. #### Can I filter by location? ✅ Yes. Provide a `location` string to filter results when you want region-specific lead generation. #### How can I export the scraped results? ✅ After the run, open the **Amazon Emails Dataset** → **Scraped Leads** dataset view and export from Apify Console (JSON/CSV depending on your workflow). #### Is it legal and compliant to use this Amazon email scraper for outreach? ✅ The actor works with **publicly accessible sources**, but whether you can use the resulting email lists for outreach depends on applicable laws and the relevant platform rules. You should ensure your use complies with GDPR/CCPA, anti-spam rules, and any terms of service. *** ### Support & feature requests Want to improve **Amazon Email Scraper - Fast, Advanced and Cheapest** or request enhancements to your **Amazon email scraper tool** workflow? 💡 Send feedback and ideas anytime. - 💡 **Feature Requests**: For example, enhancements like better exports, additional filtering options, or custom formatting for CRM imports. - 📧 **Contact**: Email us at Your feedback helps shape the roadmap for this **Amazon seller email finder** and **Amazon outreach email list** experience. *** *If you’re serious about an SEO-optimized Amazon Email Scraper - Fast, Advanced and Cheapest, this is the fastest path from public Amazon data to usable outreach leads.* *** ### Disclaimer **This tool accesses only publicly accessible sources.** It does not access private profiles, authenticated data, or password-protected content. You are responsible for complying with applicable laws and regulations (including GDPR/CCPA), anti-spam regulations, and the terms of service of the sources being scraped. For data removal requests, contact . Please 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/amazon-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/amazon-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/amazon-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/amazon-email-scraper-fast-advanced-and-cheapest", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Amazon Email Scraper Fast Advanced And Cheapest", "description": "🚀 Get verified Amazon seller emails fast with Amazon Email Scraper: Fast Advanced & Cheapest. 📩 Built for B2B outreach, lead gen, and sales prospecting. ⚡ Efficient scraping with advanced filters—save time and costs.", "version": "1.0", "x-build-id": "yFtDG4j80jQGton0S" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scrapers-hub~amazon-email-scraper-fast-advanced-and-cheapest/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scrapers-hub-amazon-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scrapers-hub~amazon-email-scraper-fast-advanced-and-cheapest/runs": { "post": { "operationId": "runs-sync-scrapers-hub-amazon-email-scraper-fast-advanced-and-cheapest", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scrapers-hub~amazon-email-scraper-fast-advanced-and-cheapest/run-sync": { "post": { "operationId": "run-sync-scrapers-hub-amazon-email-scraper-fast-advanced-and-cheapest", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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 } } } } } } } } } } ```