# Newsletter Sponsorship Rate Extractor (`seeb/newsletter-sponsorship-rate-extractor`) Actor Extract newsletter audience, placement, rate, niche, and booking signals for sponsorship research. - **URL**: https://apify.com/seeb/newsletter-sponsorship-rate-extractor.md - **Developed by:** [Techionik](https://apify.com/seeb) (community) - **Categories:** Marketing, Lead generation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $15.00 / 1,000 dataset items 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 ## Newsletter Sponsorship Rate Extractor Extract newsletter audience, placement, rate, niche, and booking signals for sponsorship research. This actor is built for buyers who do not want a noisy scrape. It turns simple public text, snippets, or URLs into a compact dataset with only the fields that matter for the workflow. It is intentionally limited to clean, useful rows instead of dumping page text, actor names, internal scores, or duplicate metadata. ### What it is best for - Fast marketplace-ready research runs - Lead qualification and enrichment workflows - Competitive intelligence and monitoring - CSV exports for sales, marketing, product, and operations teams - AI-agent workflows that need predictable fields ### Input The input is intentionally simple: - `items` - paste snippets, page text, job posts, review text, announcements, or public URLs. - `maxResults` - choose how many rows to save, from 1 to 20. You do not need selectors, CSS rules, browser settings, login cookies, or complicated configuration. ### Output Each saved dataset item contains only these useful fields: - `publicationName` - PublicationName extracted or inferred from the supplied text. - `audienceSize` - AudienceSize extracted or inferred from the supplied text. - `niche` - Niche extracted or inferred from the supplied text. - `adPlacement` - AdPlacement extracted or inferred from the supplied text. - `publishedRate` - PublishedRate extracted or inferred from the supplied text. - `bookingSignal` - BookingSignal extracted or inferred from the supplied text. - `contactUrl` - ContactUrl extracted or inferred from the supplied text. ### Example input ```json { "items": [ "The SaaS Brief reaches 42,000 founders and operators. Primary sponsor placement is $1,200 per issue with booking via sponsorship form.", "Local Eats Weekly offers featured restaurant sponsorship for $350. Audience: 18,500 food lovers in Chicago.", "AI Builder Digest has 75k subscribers, ad slots include top banner and dedicated send. Contact sponsors@aibuilder.example." ], "maxResults": 3 } ```` ### Example output ```json { "publicationName": "The SaaS Brief", "audienceSize": "42,000", "niche": "SaaS founders and operators", "adPlacement": "primary sponsor placement", "publishedRate": "$1,200 per issue", "bookingSignal": "booking form mentioned", "contactUrl": "manual input" } ``` ### Why this actor is useful Many scrapers return too much raw data. This actor is shaped for a buyer who wants a ready-to-use spreadsheet: one row per useful signal, clean columns, and no unnecessary filler fields. That makes it easier to plug into Google Sheets, Airtable, CRMs, lead scoring tools, research reports, and AI workflows. ### Recommended pricing Best model: **Pay per event / per dataset item**. Recommended event: `apify-default-dataset-item` Suggested price: **$0.0040 per newsletter sponsor row**. This is predictable for users because every paid event maps to one visible dataset row. ### Limitations - The actor works best with public pages or pasted text. - It does not bypass logins, paywalls, CAPTCHA, or private data restrictions. - Fields are extracted from visible text and may need human review for high-stakes decisions. - For very large monitoring jobs, split inputs into batches to keep results easy to inspect. ### Categories MARKETING, LEAD\_GENERATION ### Search keywords newsletter sponsorship, media buying, ad rates, creator economy # Actor input Schema ## `items` (type: `array`): Paste simple page text, job posts, review snippets, announcements, or URLs. Each item can produce one clean result. ## `maxResults` (type: `integer`): Maximum rows to save. Keep 3 for a quick sample or 20 for a larger test. ## Actor input object example ```json { "items": [ "The SaaS Brief reaches 42,000 founders and operators. Primary sponsor placement is $1,200 per issue with booking via sponsorship form.", "Local Eats Weekly offers featured restaurant sponsorship for $350. Audience: 18,500 food lovers in Chicago.", "AI Builder Digest has 75k subscribers, ad slots include top banner and dedicated send. Contact sponsors@aibuilder.example." ], "maxResults": 20 } ``` # Actor output Schema ## `results` (type: `string`): No description ## `runSummary` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = {}; // Run the Actor and wait for it to finish const run = await client.actor("seeb/newsletter-sponsorship-rate-extractor").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 = {} # Run the Actor and wait for it to finish run = client.actor("seeb/newsletter-sponsorship-rate-extractor").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 '{}' | apify call seeb/newsletter-sponsorship-rate-extractor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=seeb/newsletter-sponsorship-rate-extractor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Newsletter Sponsorship Rate Extractor", "description": "Extract newsletter audience, placement, rate, niche, and booking signals for sponsorship research.", "version": "1.0", "x-build-id": "IuqVvn41zzvfJZtC5" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/seeb~newsletter-sponsorship-rate-extractor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-seeb-newsletter-sponsorship-rate-extractor", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/seeb~newsletter-sponsorship-rate-extractor/runs": { "post": { "operationId": "runs-sync-seeb-newsletter-sponsorship-rate-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/seeb~newsletter-sponsorship-rate-extractor/run-sync": { "post": { "operationId": "run-sync-seeb-newsletter-sponsorship-rate-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "items" ], "properties": { "items": { "title": "Text snippets or URLs", "type": "array", "description": "Paste simple page text, job posts, review snippets, announcements, or URLs. Each item can produce one clean result.", "items": { "type": "string" }, "default": [ "The SaaS Brief reaches 42,000 founders and operators. Primary sponsor placement is $1,200 per issue with booking via sponsorship form.", "Local Eats Weekly offers featured restaurant sponsorship for $350. Audience: 18,500 food lovers in Chicago.", "AI Builder Digest has 75k subscribers, ad slots include top banner and dedicated send. Contact sponsors@aibuilder.example." ] }, "maxResults": { "title": "Maximum results", "minimum": 1, "maximum": 20, "type": "integer", "description": "Maximum rows to save. Keep 3 for a quick sample or 20 for a larger test.", "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 } } } } } } } } } } ```