# Amazon B2B Email Scraper (`scraper-mind/amazon-b2b-email-scraper`) Actor Amazon B2B Email Scraper: πŸ“§πŸš€ Instantly extract Amazon B2B emails, domains, and lead data by keyword and country. Perfect for building targeted B2B lists, seller contacts, and high-value Amazon leadsβ€”fast and effortlessly. - **URL**: https://apify.com/scraper-mind/amazon-b2b-email-scraper.md - **Developed by:** [Scraper Mind](https://apify.com/scraper-mind) (community) - **Categories:** Lead generation, E-commerce, Automation - **Stats:** 23 total users, 2 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing $15.00/month + usage To use this Actor, you pay a monthly rental fee to the developer. The rent is subtracted from your prepaid usage every month after the free trial period.You also pay for the Apify platform usage, which gets cheaper the higher Apify subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#rental-actors ## 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 ## πŸš€ **Amazon B2B Email Scraper – The Ultimate Amazon B2B Lead Extractor** πŸ”πŸ“§ The **Amazon B2B Email Scraper** is a powerful automation tool designed to extract **verified Amazon B2B emails**, contact details, business domains, and product-linked profiles from Amazon listings, seller pages, and content-rich pages. πŸ’Ό It automatically scans Amazon pages based on your **keywords**, **country**, and **Amazon region**, helping you discover real **Amazon B2B leads** at scale. With this tool, you can quickly build targeted outreach lists, enrich your CRM, and boost your sales pipeline. 🧲 From finding **brand contacts** to identifying **Amazon sellers**, this scraper simplifies B2B prospectingβ€”making it a must-have for marketers, agencies, and lead-generation teams looking to dominate Amazon-focused niches. --- ### πŸ“¦ What **Amazon B2B Email** can I extract? Below is a breakdown of the data types extractable using the **Amazon B2B Email Scraper**: | Data Type | Description | | ----------------------- | ---------------------------------------------------- | | **Amazon B2B Email** | Primary business emails found on Amazon-linked pages | | **Email Domain** | Company or brand domain extracted from email | | **Amazon URL** | Product, profile, or listing URL | | **Page Title** | Title extracted from the Amazon page | | **Description Snippet** | Text snippet containing the email | | **Keyword Source** | Keyword used to trigger the search | | **Country** | Target country for scraping | | **Scrape Source** | Amazon region used (e.g., Amazon US) | ✨ *Perfect for building Amazon B2B lead lists, brand contacts, seller outreach, and more.* --- ### 🧾 Input Parameters #### Example Input (JSON) ```json { "keywords": ["fitness", "gym", "workout"], "country": "United States", "scrapeFrom": "Amazon US", "engine": "legacy", "maxEmails": 20 } ```` #### Input Parameters Table | Parameter | Type | Description | | -------------- | ------- | ------------------------------------------------ | | **keywords** | Array | Keywords to search for Amazon emails | | **country** | String | Target country for scraping | | **scrapeFrom** | String | Amazon region (e.g., Amazon US) | | **engine** | String | Select scraping engine: cost-effective or legacy | | **maxEmails** | Integer | Maximum number of emails to extract | *** ### πŸ“€ Output Format #### Example Output (JSON) ```json [ { "keyword": "fitness", "title": "The Art of Health Podcast", "url": "https://www.amazon.com/The-Art-of-Health-Podcast/dp/B0CGXL349Q", "description": "Send me your fitness related question...", "email": "mail@mariesteffen.com", "email_domain": "mariesteffen.com", "country": "us", "scrape_from": "amazon.com" } ] ``` #### Output Table | Field | Description | | ---------------- | --------------------------------- | | **keyword** | Keyword used to find the lead | | **title** | Amazon page title | | **url** | Page URL | | **description** | Text snippet where email appeared | | **email** | Extracted email | | **email\_domain** | Domain of the email | | **country** | Target country | | **scrape\_from** | Amazon region used | *** ### ⭐ H3: Key Features of **Amazon B2B Email Scraper** πŸ”₯ Here are the standout features that make this tool your go-to **Amazon B2B Email Extractor**: - ⭐ **Smart Keyword-Based Discovery** – Input keywords like β€œfitness,” β€œgym,” or β€œworkout” to uncover Amazon emails tied to niche products or brands. - 🌍 **Multi-Country Targeting** – Choose from 100+ countries for ultra-targeted scraping. - πŸ›’ **Amazon Region Selection** – Scrape from Amazon US, UK, India, Germany, Canada, and more. - βš™οΈ **Dual Scraping Engines** – - **Cost-Effective Engine:** Fast, async, and proxy-optimized. - **Legacy Engine:** More stable and reliable for deep, traditional scraping. - πŸ“§ **Max Email Control** – Set how many emails you want (1–10,000). - πŸ” **Clean, Structured Output** – JSON format ideal for CRMs, enrichment tools, or spreadsheets. - πŸš€ **High Scalability** – Process thousands of keywords and URLs automatically. - 🧩 **Zero Coding Required** – Simple UI with just keyword + country + Amazon region input. This scraper is engineered for marketers, Amazon consultants, agencies, SaaS founders, and anyone needing **Amazon B2B leads** fast. *** ### πŸ› οΈ How to use **Amazon B2B Email Scraper** (Step-by-Step) πŸ“˜ Follow this simple guide to start extracting **Amazon B2B emails** in minutes: 1. πŸ” **Log in to Apify** Create an account (free) or sign in. 2. πŸ” **Select the Actor** Search for **β€œAmazon B2B Email Scraper”** in the Apify Marketplace. 3. ✏️ **Enter Input Data** Add keywords such as β€œfitness,” β€œgym,” or β€œworkout” to the *keywords* field. 4. 🌍 **Choose Country & Amazon Region** Example: United States + Amazon US. 5. βš™οΈ **Pick Your Engine** - *Legacy* (more stable), or - *Cost-effective* (faster, cheaper). 6. πŸ“§ **Set Max Emails** Choose how many total emails you want. 7. ▢️ **Run the Actor** The scraper automatically extracts **Amazon emails**, titles, descriptions, and contact domains. 8. πŸ“₯ **Download Your Results** Export in JSON, Excel, or Dataset formats. Easy, fast, and scalable! *** ### 🎯 Use Cases for **Amazon B2B Email Scraper** Here’s how businesses use this scraper in real-world scenarios: - πŸ’Ό **B2B Lead Generation** – Extract brand emails from product listings or podcasts on Amazon. - πŸ“§ **Email Outreach & Sales** – Build lists for cold emailing, influencer partnerships, or brand collaborations. - πŸ“Š **Market Research** – Analyze which Amazon niches contain more publicly listed emails. - πŸ›οΈ **Seller Outreach** – Contact Amazon sellers for partnerships, sourcing, or private-label deals. - 🧲 **Agency Prospecting** – Perfect for marketing agencies, SEO firms, and ecommerce consultants. - 🧡 **Content Research** – Identify creators, authors, and podcasters with contact details listed on Amazon. - πŸ”Ž **Keyword-Based Lead Mining** – Turn niche keywords into targeted Amazon B2B leads. *** ### πŸ’Ž Why Choose Us? - πŸš€ **Advanced Extraction Logic** – Designed specifically to capture relevant **Amazon B2B emails** quickly. - πŸ”’ **Reliable & Stable** – Built with multiple engine options for flexibility and performance. - 🌍 **Global Support** – Scrape Amazon regions worldwide with unmatched coverage. - 🎯 **Precision Targeting** – Keyword + country + Amazon region ensures laser-accurate results. - πŸ“˜ **Clean Data Output** – Ready for CRM import, cold outreach, or analysis. - πŸ’¬ **Dedicated Support Team** – Fast responses at . - πŸͺ„ **No Coding Required** – User-friendly interface for beginners and professionals. *** ### πŸ“ˆ How many results can you scrape with **Amazon B2B Email Scraper**? The scraper is built for **massive scalability**, allowing you to extract **anywhere from 1 to 10,000 emails per run**, depending on your input. Here’s how we ensure high performance: - πŸ”„ **Asynchronous request system** (when using cost-effective engine) - 🧱 **Robust fallback engine (legacy)** for deeper, more consistent scraping - 🧩 **Keyword-driven discovery** helps you uncover dozens of unique emails from a single keyword - 🌍 **Multi-country + multi-region scraping** helps widen email discovery - πŸ› οΈ **Optimized for both short & long keyword lists** - πŸ“ **Outputs structured JSON perfect for scaling across thousands of keywords** Whether you're generating a small batch of **Amazon B2B leads** or running enterprise-level campaigns, the scraper handles large datasets effortlessly. Expect fast performance, low error rates, and highly structured data – perfect for outreach, CRM integration, and mass pipeline building. πŸš€ *** ### βš–οΈ Is it legal to scrape **Amazon B2B Email**? Scraping **publicly available information** is generally legal when done responsibly. However, here are key points to understand: - βœ”οΈ The scraper only extracts *publicly visible emails* found on pages accessible to everyone. - βœ”οΈ It does **not** bypass logins, paywalls, or restricted areas. - ⚠️ Always follow **local regulations**, **Amazon’s terms**, and **email outreach guidelines** (e.g., CAN-SPAM, GDPR). - πŸ“§ Use scraped **Amazon B2B emails** ethicallyβ€”avoid spam or misuse. - πŸ’‘ The scraper is a tool; how you use the data determines compliance. This ensures safe, responsible, and compliant usage of the **Amazon B2B Email Scraper**. *** ### ❓ FAQ – Amazon B2B Email Scraper #### 1. Does it scrape private Amazon seller emails? No. It only extracts **publicly visible emails**. #### 2. Can I scrape multiple keywords at once? Yes! Add as many keywords as you like. #### 3. Does it work for any Amazon region? Yes β€” Amazon US, UK, India, Canada, Germany, and more. #### 4. What’s the best engine to use? Use **Cost-effective** for speed, **Legacy** for stability. #### 5. How many Amazon emails can I scrape? Up to **10,000 per run**. #### 6. Is coding required? No β€” everything runs with simple inputs. #### 7. Can I use the data for outreach? Yes, but follow local laws and Amazon policies. #### 8. Does it extract email domains? Yes β€” every email includes mapped **email\_domain**. # Actor input Schema ## `keywords` (type: `array`): Enter one or more keywords to search for on Amazon. ## `country` (type: `string`): Specify the country to target for Google search results. ## `scrapeFrom` (type: `string`): Choose which regional Amazon site to scrape. ## `engine` (type: `string`): Choose scraping engine. πŸš€ Cost Effective (New): Uses residential proxies with async requests for faster, cheaper scraping. πŸ”§ Legacy: Uses GOOGLE\_SERP proxy with traditional selectors - more reliable but slower and more expensive. ## `maxEmails` (type: `integer`): Enter the maximum number of emails to collect. ## Actor input object example ```json { "keywords": [ "fitness", "gym", "workout" ], "country": "United States", "scrapeFrom": "Amazon US", "engine": "legacy", "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": [ "fitness", "gym", "workout" ] }; // Run the Actor and wait for it to finish const run = await client.actor("scraper-mind/amazon-b2b-email-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`πŸ’Ύ 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); }); // πŸ“š Want to learn more πŸ“–? Go to β†’ 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": [ "fitness", "gym", "workout", ] } # Run the Actor and wait for it to finish run = client.actor("scraper-mind/amazon-b2b-email-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "keywords": [ "fitness", "gym", "workout" ] }' | apify call scraper-mind/amazon-b2b-email-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scraper-mind/amazon-b2b-email-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Amazon B2B Email Scraper", "description": "Amazon B2B Email Scraper: πŸ“§πŸš€ Instantly extract Amazon B2B emails, domains, and lead data by keyword and country. Perfect for building targeted B2B lists, seller contacts, and high-value Amazon leadsβ€”fast and effortlessly.", "version": "1.0", "x-build-id": "3AwV2eyJ8jtvgctNI" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scraper-mind~amazon-b2b-email-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scraper-mind-amazon-b2b-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scraper-mind~amazon-b2b-email-scraper/runs": { "post": { "operationId": "runs-sync-scraper-mind-amazon-b2b-email-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scraper-mind~amazon-b2b-email-scraper/run-sync": { "post": { "operationId": "run-sync-scraper-mind-amazon-b2b-email-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "country", "scrapeFrom", "maxEmails" ], "properties": { "keywords": { "title": "Keywords", "type": "array", "description": "Enter one or more keywords to search for on Amazon.", "items": { "type": "string" } }, "country": { "title": "Country", "enum": [ "United States", "United Kingdom", "Canada", "Australia", "Germany", "France", "India", "Japan", "Brazil", "Italy", "Spain", "Netherlands", "Sweden", "Norway", "Denmark", "Finland", "Poland", "Czech Republic", "Hungary", "Romania", "Bulgaria", "Croatia", "Slovenia", "Slovakia", "Estonia", "Latvia", "Lithuania", "Portugal", "Greece", "Cyprus", "Malta", "Luxembourg", "Ireland", "Belgium", "Austria", "Switzerland", "Liechtenstein", "Iceland", "South Korea", "China", "Mexico", "Argentina", "Chile", "Colombia", "Peru", "Venezuela", "Ecuador", "Uruguay", "Paraguay", "Bolivia", "Guyana", "Suriname", "French Guiana", "Falkland Islands (Malvinas)", "South Georgia and the South Sandwich Islands", "Bonaire, Sint Eustatius and Saba", "CuraΓ§ao", "Aruba", "Sint Maarten (Dutch part)", "Turks and Caicos Islands", "British Virgin Islands", "Anguilla", "Montserrat", "Antigua and Barbuda", "Barbados", "Dominica", "Grenada", "Saint Kitts and Nevis", "Saint Lucia", "Saint Vincent and the Grenadines", "Trinidad and Tobago", "Jamaica", "Bahamas", "Belize", "Costa Rica", "Guatemala", "Honduras", "Nicaragua", "Panama", "El Salvador", "Cuba", "Dominican Republic", "Haiti", "Puerto Rico", "U.S. Virgin Islands", "American Samoa", "Guam", "Northern Mariana Islands", "Saudi Arabia", "United Arab Emirates", "Bahrain", "Iraq", "Iran, Islamic Republic of", "Israel", "Jordan", "Kuwait", "Lebanon", "Oman", "Qatar", "Syrian Arab Republic", "Yemen", "Afghanistan", "Bangladesh", "Bhutan", "Maldives", "Nepal", "Pakistan", "Sri Lanka", "Myanmar", "Cambodia", "Lao People's Democratic Republic", "Thailand", "Viet Nam", "Malaysia", "Singapore", "Brunei Darussalam", "Philippines", "Indonesia", "Timor-Leste", "Papua New Guinea", "Fiji", "New Zealand", "Solomon Islands", "Vanuatu", "New Caledonia", "French Polynesia", "Wallis and Futuna", "Samoa", "Tonga", "Tuvalu", "Kiribati", "Nauru", "Federated States of Micronesia", "Marshall Islands", "Palau", "Cook Islands", "Niue", "Tokelau" ], "type": "string", "description": "Specify the country to target for Google search results.", "default": "United States" }, "scrapeFrom": { "title": "Scrape From", "enum": [ "Amazon US", "Amazon India", "Amazon Germany", "Amazon UK", "Amazon Japan", "Amazon Canada", "Amazon France", "Amazon Italy", "Amazon Spain", "Amazon Brazil", "Amazon Australia", "Amazon Mexico", "Amazon UAE", "Amazon Saudi Arabia", "Amazon Netherlands" ], "type": "string", "description": "Choose which regional Amazon site to scrape.", "default": "Amazon US" }, "engine": { "title": "Engine", "enum": [ "cost-effective", "legacy" ], "type": "string", "description": "Choose scraping engine. πŸš€ Cost Effective (New): Uses residential proxies with async requests for faster, cheaper scraping. πŸ”§ Legacy: Uses GOOGLE_SERP proxy with traditional selectors - more reliable but slower and more expensive.", "default": "legacy" }, "maxEmails": { "title": "Max Emails", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Enter the maximum number of emails to collect.", "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 } } } } } } } } } } ```