# Amazon Influencers Profile (`scraperoka/amazon-influencers-profile`) Actor 📊 Data-driven insights for modern professionals—powered by smart research and real-world clarity. From company intel to actionable summaries, I help you make faster decisions. 🚀 Follow for trusted, easy-to-use guidance! - **URL**: https://apify.com/scraperoka/amazon-influencers-profile.md - **Developed by:** [Scraperoka](https://apify.com/scraperoka) (community) - **Categories:** Agents, Lead generation, Social media - **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 ### Amazon Influencers Profile 🎯 — Extract Influencer Storefront Metadata at Scale Manually visiting Amazon Storefront pages to collect influencer metadata wastes hours you don’t have. **Amazon Influencers Profile** scrapes Amazon influencer profiles in bulk and returns structured results you can analyze right away. This Amazon Influencers Profile actor pulls an Amazon influencer profile **name**, **description**, **post counts**, and **affiliate status** so marketers, recruiters, and growth teams can build shortlists fast. It’s a practical way to turn lists of Amazon creator profile usernames into thousands of clean records in a single run. --- ### What You Get: Sample Output Here's a sample record from a single run: ```json { "influencer_username": "sweetsavingsandthings", "name": "Sweetsavingsandthings", "country": "US", "domain": "www.amazon.com", "profile_description": "A community of creators sharing product picks and curated recommendations.", "profile_link": "https://www.amazon.com/shop/sweetsavingsandthings", "is_top_creator": true, "affiliate_status": "Earns revenue", "has_curations": true, "posts_count": 124, "scraped_at": "2026-06-03T12:34:56.000Z", "success": true } ```` | Field | Type | What It Tells You | |---|---|---| | `influencer_username` | string | The influencer username you requested, so you can match results back to your input list | | `name` | string | The display name from the storefront page (useful for reporting and validation) | | `country` | string | Country code used for the storefront domain (e.g., `US`) | | `domain` | string | The Amazon domain the profile was scraped from (helps with multi-country studies) | | `profile_description` | string | The storefront bio/description text for context, filtering, and segmentation | | `profile_link` | string | Direct link to the influencer storefront profile for quick manual verification | | `is_top_creator` | boolean | A flag to quickly prioritize high-performing creators using storefront signals | | `affiliate_status` | string | Whether the profile is marked as earning revenue (or shows as `None`) | | `has_curations` | boolean | Indicates whether the storefront shows curated sections/tabs | | `posts_count` | number | The parsed number of posts to help compare creator activity levels | | `scraped_at` | string | Timestamp (UTC) of when this record was captured for freshness tracking | | `success` | boolean | Whether the scrape succeeded (`true`) or failed (`false`) | Export your dataset as JSON, CSV, or Excel — straight from the Apify dashboard. *** ### Why Amazon Influencers Profile? There are a lot of ways to pull data from Amazon storefront pages — here’s what sets **Amazon Influencers Profile** apart. #### Storefront-ready metadata for influencer research This actor focuses specifically on Amazon influencer profile essentials—like name, description, post count, and affiliate status—so your Amazon creator profile analytics are immediately usable. #### Bulk input for Amazon influencer profile setup workflows You can submit an array of `influencer_names` to scrape many Amazon influencer pages in one run, making it ideal for building discovery lists or doing batch audits of an Amazon influencer program profile. #### Resilient scraping with built-in handling It includes reliability measures for the journey from input to dataset: if a profile fails, you still get a record back with `success: false` and an `error` field, so you can rerun only what’s needed. #### Structured output you can plug into your pipeline Every result is returned in a consistent JSON structure including `profile_link` and `scraped_at`, which makes it easier to deduplicate, filter, and combine with your existing CRM, spreadsheets, or BI tools—perfect for Amazon influencer profile analytics. *** ### Configuring Your Run Drop this into your `input.json` to get started: ```json { "influencer_names": [ "sweetsavingsandthings", "another_creator_username" ], "country": "US" } ``` | Parameter | Required | What It Does | |---|---|---| | `influencer_names` | ⬜ | List of Amazon influencer usernames to scrape (example prefilled value: `sweetsavingsandthings`) | | `country` | ⬜ | Country code (e.g., `US`, `UK`, `CA`) used to select the storefront domain for each Amazon influencer profile page | *** ### Core Capabilities #### Bulk Profile Processing Use `influencer_names` to scrape multiple Amazon influencer profile pages in one run. This is designed for shortlist building and recurring audits of an Amazon influencer program profile’s public storefront details. #### Clean, consistent influencer profile output Each record includes storefront metadata fields like `name`, `profile_description`, `posts_count`, and `affiliate_status`, plus `profile_link` for quick validation. That makes Amazon influencer profile setup and vetting workflows much faster. #### Resilience for partial failures If a profile can’t be fetched successfully, the actor returns a result object with `success: false` and an `error` field. You won’t lose visibility into which Amazon influencer account usernames failed. #### Output completeness for analysis You get useful boolean flags like `is_top_creator` and `has_curations`, along with activity signals like `posts_count`. This helps you compare creator storefronts for Amazon influencer bio and overall performance-style reporting. #### Automation-friendly dataset writes Results are pushed to the Apify dataset as a batch once processed, with `scraped_at` included for freshness tracking. It’s ready for downstream processing in your analytics stack. *** ### Who Gets the Most Out of This **Influencer Marketing Managers** — Use this Amazon Influencers Profile workflow to compile an Amazon influencer profile shortlist from a list of usernames, then prioritize by `posts_count`, `is_top_creator`, and `affiliate_status`. You’ll save time on manual reviews of Amazon influencer pages and move faster into outreach. **Affiliate Program Managers** — When you’re assessing an Amazon affiliate influencer profile, the actor’s `affiliate_status` and `profile_description` fields help you quickly filter storefronts that align with your program goals, without digging through each Amazon influencer storefront page by page. **Ecommerce Growth Teams** — Build recurring audits of your creator ecosystem by scraping the same set of Amazon creator profile usernames over time and tracking changes via `scraped_at`, `posts_count`, and storefront description updates. **Freelance Researchers & Data Analysts** — Turn lists of Amazon influencer account identifiers into structured datasets suitable for Amazon influencer profile analytics. The included `profile_link` makes it easy to verify outliers and explain findings with source references. **Developers & Automation Specialists** — If you manage data pipelines, you can ingest the actor output into your existing workflows and enrich your reporting with storefront metadata (name, description, flags, and counts). The predictable JSON structure makes integration straightforward. *** ### Step-by-Step: How to Use It No coding needed. Here's how to run Amazon Influencers Profile from start to finish: 1. **Open the actor on Apify** — go to [console.apify.com](https://console.apify.com) and find **Amazon Influencers Profile**. 2. **Enter your inputs** — provide `influencer_names` (array of usernames) and optionally set `country` (default is `US`). 3. **Configure proxy settings** — if needed for your use case, set proxy preferences in the Apify run configuration (for more reliable access at scale). 4. **Hit Run and watch the live log** — review progress as each `influencer_names` entry is processed. 5. **View results in the dataset tab** — confirm `success`, inspect fields like `profile_link`, and spot any `error` records. 6. **Export as JSON, CSV, or Excel** — use the Apify dataset export options to share results with your team. The whole process takes under 5 minutes to set up. *** ### Integrations & Export Options Once your data is collected, **Amazon Influencers Profile** plugs directly into your existing workflow. You can export your Apify dataset as **JSON**, **CSV**, or **Excel** from the dataset tab. For operational workflows, you can connect the actor to tools via Apify’s API and common automation options like Zapier/Make, and you can also use scheduled runs on the Apify platform. For deeper automation, pull results programmatically using the Apify API: https://apify.com/docs/api\ You can then pipe the Amazon influencer profile data into your analytics jobs, dashboards, or CRM imports. *** ### Pricing & Free Trial Amazon Influencers Profile runs on the Apify platform, which offers a **free tier** — no credit card required to get started. You can use it for several test runs, then scale using Apify’s pay-as-you-go model based on compute. For heavier usage, Apify also provides subscription plans for different workloads. For exact pricing and credits, check the Apify pricing page on [apify.com](https://apify.com). Start for free at [apify.com](https://apify.com) and scale when you're ready. *** ### Reliability & Performance | What We Handle | How | |---|---| | Platform rate limits | Requests are paced with delays to reduce pressure during batch runs | | Proxy support | Built-in proxy support helps improve reliability at scale | | Failed profile requests | Each influencer entry returns either `success: true` or `success: false` with an `error` field | | Data freshness | Each successful record includes `scraped_at` in UTC | | Error visibility | Failures are captured per influencer username so you can isolate and re-run only what’s needed | | Scale in a single run | Processes multiple `influencer_names` entries in one job | Limitations: the actor only works with publicly accessible storefront profile pages. If a profile page does not expose certain fields, those values may be missing (for example `posts_count` may remain `0`). 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 so you can run Amazon Influencers Profile without a credit card. Availability and credit amounts depend on your Apify account and current plan. #### Do I need to log in to Amazon to use this? No—this actor scrapes publicly accessible Amazon Storefront profile pages. You provide influencer usernames and a `country` code, and it returns storefront metadata. #### How accurate is the data? The data is extracted from the storefront page content and returned in structured fields like `name`, `profile_description`, `posts_count`, and `affiliate_status`. Accuracy depends on what the storefront displays publicly for each Amazon influencer profile. #### How many results can I get per run? You can provide a list of many `influencer_names` in a single run. The practical limit depends on your Apify run configuration and account limits. #### How often is the data updated / how fresh is it? Each record includes `scraped_at` (UTC) to show when it was captured. If you need fresher Amazon influencer storefront link data, run the actor again on your schedule. #### Is this legal? Does it comply with GDPR / CCPA? The actor accesses **publicly available data** from Amazon Storefront pages. You are responsible for using the results in compliance with GDPR, CCPA, and applicable laws and platform terms. #### Can I export results to Google Sheets or Excel? Yes. You can export from the Apify dashboard, including **Excel** formats via dataset export. If you need Google Sheets specifically, use an integration or import flow based on your export format. #### Can I run this on a schedule automatically? Yes. You can set up scheduled runs within the Apify platform so Amazon Influencers Profile executes automatically at intervals you choose. #### Can I access this via API? Yes. You can use the Apify API to trigger runs and retrieve dataset results programmatically. Full documentation is available at https://apify.com/docs/api #### What happens if the actor hits an error? If a profile scrape fails, you still receive a result record for that `influencer_username` with `success: false` and an `error` field. Successful profiles still return their extracted data in the same run. *** ### Need Help or Have a Request? Got a question about Amazon Influencers Profile or want a new feature added? Reach out at and we’ll help you get unstuck. We welcome requests like adding better filtering options for Amazon influencer bio-style fields and supporting webhook-style notifications when a run completes. *** ### Disclaimer & Responsible Use *Amazon Influencers Profile is the fastest, most reliable way to extract Amazon influencer profile metadata at scale — start your free run today.* This actor uses **publicly available data** and does not access private accounts, login-gated content, or password-protected pages. You are responsible for ensuring your use complies with GDPR, CCPA, and applicable platform terms. For data removal requests, contact . Use responsibly, ethically, and only for lawful purposes. # Actor input Schema ## `influencer_names` (type: `array`): List of Amazon influencer usernames to scrape. ## `country` (type: `string`): The country code (e.g., US, UK, CA). ## Actor input object example ```json { "influencer_names": [ "sweetsavingsandthings" ], "country": "US" } ``` # 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 = { "influencer_names": [ "sweetsavingsandthings" ] }; // Run the Actor and wait for it to finish const run = await client.actor("scraperoka/amazon-influencers-profile").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 = { "influencer_names": ["sweetsavingsandthings"] } # Run the Actor and wait for it to finish run = client.actor("scraperoka/amazon-influencers-profile").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 '{ "influencer_names": [ "sweetsavingsandthings" ] }' | apify call scraperoka/amazon-influencers-profile --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=scraperoka/amazon-influencers-profile", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Amazon Influencers Profile", "description": "📊 Data-driven insights for modern professionals—powered by smart research and real-world clarity. From company intel to actionable summaries, I help you make faster decisions. 🚀 Follow for trusted, easy-to-use guidance!", "version": "0.1", "x-build-id": "Bf63jpInHTb1Q077s" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/scraperoka~amazon-influencers-profile/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-scraperoka-amazon-influencers-profile", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/scraperoka~amazon-influencers-profile/runs": { "post": { "operationId": "runs-sync-scraperoka-amazon-influencers-profile", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/scraperoka~amazon-influencers-profile/run-sync": { "post": { "operationId": "run-sync-scraperoka-amazon-influencers-profile", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "influencer_names": { "title": "Influencer Names", "type": "array", "description": "List of Amazon influencer usernames to scrape.", "items": { "type": "string" } }, "country": { "title": "Country Code", "type": "string", "description": "The country code (e.g., US, UK, CA).", "default": "US" } } }, "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 } } } } } } } } } } ```