🎣 Turn LinkedIn job posts into sales signals with Apify, OpenAI, and Notion
⚡ 5 views · 🎣 Lead Generation & Enrichment
💡 Pro Tip — If you’re pulling LinkedIn data through HTTP requests or dealing with API restrictions, there’s a community node called ScraperNode that handles this natively. It has dedicated scrapers for profiles, companies, jobs, and people search — you just pass a URL and get structured data back.
Description
Quick overview
This workflow runs every Monday, scrapes recent LinkedIn job posts via Apify, uses OpenAI to infer hiring-based buying signals and relevance, and saves each signal as a new page in a Notion database.
How it works
- Runs on a schedule every Monday at 08:00.
- Defines the target job search queries, locations, what you sell, and the maximum number of posts to analyze.
- Uses Apify’s LinkedIn Jobs Scraper actor to collect job posts from the past week based on the configured searches.
- Filters out job posts that don’t include a company and enough text (title or description) to analyze.
- Skips job posts that were already processed in previous executions using the job ID or URL.
- Sends each remaining post to an OpenAI chat model to return JSON describing what the company is building, the implied need, timing, and a 1–5 relevance score.
- Creates a Notion database page for each post, storing the company, role, inferred signal fields, relevance, and the job link.
Setup
- Add credentials for Apify (API token), OpenAI (API key), and Notion.
- Select or paste your Notion database ID in the Notion node and ensure it has matching properties (Role, Building, Implied need, Timing, Relevance, Link).
- Update the search queries, locations, what you sell, and maxItems values to match your ideal buyer signals.
🔗 Nodes Used
Google Sheets, HTTP Request, Slack, Gmail, Notion, Schedule Trigger
📥 Import
Download workflow.json and import into n8n:
Workflow menu → Import from File