# SponsorTrack – Creator Sponsorship Intelligence (`enosgb/sponsor-track`) Actor Discover which brands sponsor which creators on YouTube, Podcasts, and Newsletters. Get sponsorship tenure, frequency, estimated spend, and AI-powered insights for media buying and influencer marketing. - **URL**: https://apify.com/enosgb/sponsor-track.md - **Developed by:** [Enos Melo](https://apify.com/enosgb) (community) - **Categories:** Social media, Automation, AI - **Stats:** 1 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $20.00 / 1,000 per sponsorship detecteds 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 ## SponsorTrack – Creator Sponsorship Intelligence **SponsorTrack** is the first intelligence tool that systematically tracks brand sponsorships across YouTube videos, podcast episodes, and newsletters. We detect sponsorship mentions, calculate how long a brand has been partnering with a creator (sponsorship tenure), identify frequency patterns, and estimate spend—giving you the competitive edge in influencer marketing. ### Why use SponsorTrack? - **Brands & Media Buyers** – Identify where your competitors are advertising, find cost-effective creators, and benchmark sponsorship rates - **Influencer Marketing Agencies** – Show clients proven sponsorship history, avoid creators who overstate brand relationships, and negotiate better deals with data - **Investors & Startups** – Understand go-to-market strategies of SaaS companies, see which creators they trust for audience reach - **Content Creators** – Research what rates similar creators command, discover which brands sponsor your niche ### How to use SponsorTrack 1. Go to the **Input** tab 2. Choose mode: **creators** (analyze specific creators) or **brands** (find brand placements) 3. Provide creators with their platform URLs (YouTube Channel ID, Podcast RSS, Substack, Beehiiv) 4. Set brands to track (e.g., ["Notion", "Stripe", "Linear"]) 5. Click **Start** and download sponsorship data ### Input | Field | Required | Description | | ------------------- | ---------------------- | ----------------------------------------------------------------------------- | | `mode` | Yes | "creators" or "brands" | | `creators` | Yes (if mode=creators) | Array of `{ name, youtubeChannelId, podcastRssUrl, substackUrl, beehiivUrl }` | | `brands` | Yes | Array of brand names to track | | `daysBack` | No | Days of content to analyze (default: 90) | | `maxItems` | No | Max content items per creator (default: 200) | | `enableAiDetection` | No | Use AI for detection (default: true) | #### Example Input ```json { "mode": "creators", "creators": [ { "name": "Ali Abdaal", "youtubeChannelId": "UCJgfaSkE5Lyiq5R0sX5jVgw", "podcastRssUrl": "https://feeds.simplecast.com/dHoohVNH" } ], "brands": ["Notion", "Stripe", "Linear"], "daysBack": 90, "maxItems": 200, "enableAiDetection": true } ```` ### Output Fields - **brandName** – Sponsoring brand - **creatorName** – Content creator - **platform** – youtube, podcast, or newsletter - **contentTitle** – Title of sponsored content - **contentUrl** – Direct URL to content - **sponsorMention** – Exact text of sponsorship mention - **sponsorshipTenureDays** – Days brand has been sponsoring - **frequency** – weekly, monthly, quarterly, or one-time - **estimatedSpend** – Estimated cost range - **isNewSponsorship** – Whether this is a new partnership - **intentReason** – AI-generated insight #### Example Output ```json { "brandName": "Notion", "creatorName": "Ali Abdaal", "platform": "youtube", "contentTitle": "How I Take Notes in 2024", "contentUrl": "https://www.youtube.com/watch?v=abcdef123", "sponsorMention": "This video is sponsored by Notion...", "sponsorshipTenureDays": 487, "frequency": "monthly", "estimatedSpend": "$2,000 - $10,000", "detectedAt": "2025-04-05T14:30:00.000Z", "isNewSponsorship": false, "intentReason": "Long-term partnership with 12 mentions over 16 months indicating established brand-creator relationship" } ``` ### Pricing / Cost Estimation **Free Tier:** The first $5/month of platform usage is free on Apify. **Example costs:** - **5 creators, 10 videos each:** ~$0.10-0.20 per run - **10 creators, 50 videos each:** ~$0.30-0.50 per run - **AI detection (Groq):** ~$0.001 per content item (very cheap with llama-3.1-8b-instant) **YouTube API:** Requires your own YouTube Data API v3 key (free tier available in Google Cloud Console). **Compare:** Manual research costs $50-200/hour. SponsorTrack gives you thousands of data points for pennies. ### Tips & Advanced Options - **Provide YouTube Channel IDs** (not URLs) for accurate fetching - **Use Podcast RSS feeds** for most accurate podcast data - **Disable AI detection** for faster, cheaper runs (uses regex only) - **Run weekly** to track new sponsorships over time — the Actor remembers previous runs ### FAQ, Disclaimers & Support **Q: Is this legal?** A: SponsorTrack only uses publicly available data — video descriptions, podcast show notes, and newsletter content. We respect robots.txt and rate limits. **Q: What platforms are supported?** A: YouTube (via API), Podcasts (RSS feeds), Substack, and Beehiiv newsletters. **Q: How accurate is the detection?** A: AI detection (Groq) has ~90% accuracy. Regex fallback catches obvious sponsorships but may miss nuanced mentions. **Q: How does tenure calculation work?** A: The Actor stores historical data in Key-Value Store. Tenure is calculated as days since first detected sponsorship. **Known limitations:** - YouTube requires API key (no API = no YouTube data) - Some podcasts may have private RSS feeds - Newsletter scraping depends on public page access **Need help or have feedback?** Use the Issues tab or contact us through Apify Console. Custom enterprise solutions available on request. # Actor input Schema ## `mode` (type: `string`): Choose whether to analyze specific creators or search by brand interests ## `creators` (type: `array`): List of creators with their platform URLs. At least one platform URL is required per creator. ## `brands` (type: `array`): Brand names to search for in creator content when mode is 'brands'. Used as keywords for sponsorship detection. ## `daysBack` (type: `number`): How many days of past content to analyze. More days = more complete data but higher compute cost. ## `maxItems` (type: `number`): Maximum number of videos/podcasts/newsletter issues to process per creator. Controls cost and runtime. ## `enableAiDetection` (type: `boolean`): Use Groq AI to accurately detect sponsorship mentions and generate insights. Disable for faster, cheaper runs using regex-only detection. ## Actor input object example ```json { "mode": "creators", "creators": [ { "name": "Example Creator", "youtubeChannelId": "UCXXXXXX", "podcastRssUrl": "https://example.com/feed.rss", "substackUrl": "https://example.substack.com", "beehiivUrl": "" } ], "brands": [ "Notion", "Stripe", "Linear", "Supabase" ], "daysBack": 90, "maxItems": 200, "enableAiDetection": true } ``` # Actor output Schema ## `sponsorships` (type: `string`): All detected brand sponsorships with metadata, tenure, and AI-generated insights ## `summary` (type: `string`): Execution statistics including creators processed, sponsorships detected, and runtime metadata ## `history` (type: `string`): Historical sponsorship data per creator for tenure tracking and change detection # 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 = { "brands": [ "Notion", "Stripe", "Linear", "Supabase" ] }; // Run the Actor and wait for it to finish const run = await client.actor("enosgb/sponsor-track").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 = { "brands": [ "Notion", "Stripe", "Linear", "Supabase", ] } # Run the Actor and wait for it to finish run = client.actor("enosgb/sponsor-track").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 '{ "brands": [ "Notion", "Stripe", "Linear", "Supabase" ] }' | apify call enosgb/sponsor-track --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=enosgb/sponsor-track", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "SponsorTrack – Creator Sponsorship Intelligence", "description": "Discover which brands sponsor which creators on YouTube, Podcasts, and Newsletters. Get sponsorship tenure, frequency, estimated spend, and AI-powered insights for media buying and influencer marketing.", "version": "1.0", "x-build-id": "Is6xUKlNZNoiR36Gq" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/enosgb~sponsor-track/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-enosgb-sponsor-track", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/enosgb~sponsor-track/runs": { "post": { "operationId": "runs-sync-enosgb-sponsor-track", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/enosgb~sponsor-track/run-sync": { "post": { "operationId": "run-sync-enosgb-sponsor-track", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "mode" ], "properties": { "mode": { "title": "Analysis Mode", "enum": [ "creators", "brands" ], "type": "string", "description": "Choose whether to analyze specific creators or search by brand interests", "default": "creators" }, "creators": { "title": "Creators to Analyze", "minItems": 1, "type": "array", "description": "List of creators with their platform URLs. At least one platform URL is required per creator.", "items": { "type": "object", "properties": { "name": { "type": "string", "title": "Creator Name", "description": "Full name or channel name (e.g., 'Ali Abdaal', 'MrBeast')" }, "youtubeChannelId": { "type": "string", "title": "YouTube Channel ID", "description": "YouTube channel ID (not URL). Find it in channel URL: youtube.com/channel/UCXXXXXX" }, "podcastRssUrl": { "type": "string", "title": "Podcast RSS Feed URL", "description": "Public RSS feed URL for the podcast (e.g., https://anchor.fm/s/.../podcast/rss)" }, "substackUrl": { "type": "string", "title": "Substack URL", "description": "Full Substack publication URL (e.g., https://risingproductmind.substack.com)" }, "beehiivUrl": { "type": "string", "title": "Beehiiv URL", "description": "Full Beehiiv publication URL (e.g., https://www.beehiiv.com/p/...)" } } }, "default": [ { "name": "Example Creator", "youtubeChannelId": "UCXXXXXX", "podcastRssUrl": "https://example.com/feed.rss", "substackUrl": "https://example.substack.com", "beehiivUrl": "" } ] }, "brands": { "title": "Brands to Track", "minItems": 1, "type": "array", "description": "Brand names to search for in creator content when mode is 'brands'. Used as keywords for sponsorship detection.", "items": { "type": "string" }, "default": [ "Notion", "Stripe", "Linear", "Supabase" ] }, "daysBack": { "title": "Lookback Period (Days)", "minimum": 1, "maximum": 365, "type": "number", "description": "How many days of past content to analyze. More days = more complete data but higher compute cost.", "default": 90 }, "maxItems": { "title": "Max Content Items", "minimum": 1, "maximum": 2000, "type": "number", "description": "Maximum number of videos/podcasts/newsletter issues to process per creator. Controls cost and runtime.", "default": 200 }, "enableAiDetection": { "title": "Enable AI-Powered Sponsorship Detection", "type": "boolean", "description": "Use Groq AI to accurately detect sponsorship mentions and generate insights. Disable for faster, cheaper runs using regex-only detection.", "default": true } } }, "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 } } } } } } } } } } ```