# B2B Buyer Intent from Job Descriptions (`knotted_tussock/b2b-buyer-intent-from-job-descriptions`) Actor Convert job postings into sales intelligence by detecting tools, initiatives, departments, pains, and likely vendor buying triggers. - **URL**: https://apify.com/knotted\_tussock/b2b-buyer-intent-from-job-descriptions.md - **Developed by:** [Ralph T](https://apify.com/knotted_tussock) (community) - **Categories:** Jobs, Lead generation, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 ## B2B Buyer Intent from Job Descriptions Find companies with likely budget and pain by reading what they are hiring for. ### Best for - B2B sales teams building account lists - recruiters and staffing firms qualifying growth areas - investors and market researchers tracking demand by category - data products that turn job posts into company-intent datasets ### What it detects The Actor analyzes job titles and descriptions for signals such as: - sales/revenue tooling: Salesforce, HubSpot, ZoomInfo, RevOps - data/AI stack: Snowflake, dbt, Airflow, Databricks, LLMs - security/compliance: SOC 2, HIPAA, SIEM, vulnerability management - DevOps/cloud: AWS, Kubernetes, Terraform, observability - HR/people ops, finance/ERP, ecommerce/marketing, customer support ### Example input ```json { "jobRecords": [ { "company": "Acme SaaS", "jobTitle": "Senior RevOps Manager", "description": "Own Salesforce, HubSpot, ZoomInfo, sales automation and pipeline operations." } ], "minIntentScore": 20 } ```` ### Output Each dataset item includes the company, job, intent categories, detected tools, buying triggers, recommended vendor angles, and a plain-English sales angle. ### Suggested workflow Run a career-page or job-board Actor first, then feed its dataset ID into this Actor to turn raw postings into sales intelligence. # Actor input Schema ## `jobRecords` (type: `array`): Job records from career pages, job boards, or another Actor. Supported fields: title/jobTitle, company/companyName, description/descriptionSnippet/text, url/applyUrl, location. ## `datasetId` (type: `string`): Optional Apify dataset ID to read when jobRecords is empty. ## `targetCategories` (type: `array`): Limit or prioritize categories. Leave empty for all. ## `minIntentScore` (type: `integer`): Only export jobs whose detected buyer-intent score is at or above this value. ## `maxRecords` (type: `integer`): Maximum number of input job records to analyze. ## Actor input object example ```json { "jobRecords": [ { "company": "Acme SaaS", "jobTitle": "Senior RevOps Manager", "description": "Own Salesforce, HubSpot, ZoomInfo, sales automation, reporting, data quality, and pipeline operations.", "applyUrl": "https://example.com/job/1" } ], "targetCategories": [ "sales-tech", "data-ai", "security-compliance", "devops-cloud" ], "minIntentScore": 20, "maxRecords": 1000 } ``` # Actor output Schema ## `results` (type: `string`): No description ## `summary` (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 = { "jobRecords": [ { "company": "Acme SaaS", "jobTitle": "Senior RevOps Manager", "description": "Own Salesforce, HubSpot, ZoomInfo, sales automation, reporting, data quality, and pipeline operations.", "applyUrl": "https://example.com/job/1" } ], "targetCategories": [ "sales-tech", "data-ai", "security-compliance", "devops-cloud" ] }; // Run the Actor and wait for it to finish const run = await client.actor("knotted_tussock/b2b-buyer-intent-from-job-descriptions").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 = { "jobRecords": [{ "company": "Acme SaaS", "jobTitle": "Senior RevOps Manager", "description": "Own Salesforce, HubSpot, ZoomInfo, sales automation, reporting, data quality, and pipeline operations.", "applyUrl": "https://example.com/job/1", }], "targetCategories": [ "sales-tech", "data-ai", "security-compliance", "devops-cloud", ], } # Run the Actor and wait for it to finish run = client.actor("knotted_tussock/b2b-buyer-intent-from-job-descriptions").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 '{ "jobRecords": [ { "company": "Acme SaaS", "jobTitle": "Senior RevOps Manager", "description": "Own Salesforce, HubSpot, ZoomInfo, sales automation, reporting, data quality, and pipeline operations.", "applyUrl": "https://example.com/job/1" } ], "targetCategories": [ "sales-tech", "data-ai", "security-compliance", "devops-cloud" ] }' | apify call knotted_tussock/b2b-buyer-intent-from-job-descriptions --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=knotted_tussock/b2b-buyer-intent-from-job-descriptions", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "B2B Buyer Intent from Job Descriptions", "description": "Convert job postings into sales intelligence by detecting tools, initiatives, departments, pains, and likely vendor buying triggers.", "version": "0.1", "x-build-id": "dfmEXJxacI4ysdQmB" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/knotted_tussock~b2b-buyer-intent-from-job-descriptions/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-knotted_tussock-b2b-buyer-intent-from-job-descriptions", "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/knotted_tussock~b2b-buyer-intent-from-job-descriptions/runs": { "post": { "operationId": "runs-sync-knotted_tussock-b2b-buyer-intent-from-job-descriptions", "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/knotted_tussock~b2b-buyer-intent-from-job-descriptions/run-sync": { "post": { "operationId": "run-sync-knotted_tussock-b2b-buyer-intent-from-job-descriptions", "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": { "jobRecords": { "title": "Job records", "type": "array", "description": "Job records from career pages, job boards, or another Actor. Supported fields: title/jobTitle, company/companyName, description/descriptionSnippet/text, url/applyUrl, location." }, "datasetId": { "title": "Input dataset ID", "type": "string", "description": "Optional Apify dataset ID to read when jobRecords is empty." }, "targetCategories": { "title": "Target intent categories", "type": "array", "description": "Limit or prioritize categories. Leave empty for all.", "items": { "type": "string" } }, "minIntentScore": { "title": "Minimum intent score", "minimum": 0, "maximum": 100, "type": "integer", "description": "Only export jobs whose detected buyer-intent score is at or above this value.", "default": 20 }, "maxRecords": { "title": "Max records", "minimum": 1, "maximum": 50000, "type": "integer", "description": "Maximum number of input job records to analyze.", "default": 1000 } } }, "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 } } } } } } } } } } ```