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Convert LinkedIn post reactions into qualified leads with AI and Apify

Anna BuiAnna Bui
732 views
2/3/2026
n8n
Official Page

🎯 LinkedIn ICP Lead Qualification Automation

Automatically identify and qualify ideal customer prospects from LinkedIn post reactions using AI-powered profile analysis and intelligent data enrichment.

Perfect for sales teams and marketing professionals who want to convert LinkedIn engagement into qualified leads without manual research. This workflow transforms post reactions into actionable prospect data with AI-driven ICP classification.

Good to know

  • LinkedIn Safety: Only use cookie-free Apify actors to avoid account detection and suspension risks
  • Daily Processing Limits: Scrape maximum 1 page of reactions per day (50-100 profiles) to stay under LinkedIn's radar
  • Apify actors cost approximately $0.01-0.05 per profile scraped - budget accordingly for daily processing
  • Includes intelligent rate limiting to prevent API restrictions and maintain LinkedIn account safety
  • AI classification requires clear definition of your Ideal Customer Profile criteria
  • Processing too many profiles or running too frequently will trigger LinkedIn's anti-scraping measures
  • Always monitor your LinkedIn account health and Apify usage patterns for any warning signs

How it works

  • Scrapes LinkedIn post reactions using Apify's specialized actor to identify engaged users
  • Extracts and cleans profile data including names, job titles, and LinkedIn URLs
  • Checks against existing Airtable records to prevent duplicate processing and save costs
  • Creates new prospect records with basic information for tracking purposes
  • Enriches profiles with comprehensive LinkedIn data including company details and experience
  • Aggregates and formats profile data for AI analysis and classification
  • Uses AI to analyze prospects against your ICP criteria with detailed reasoning
  • Updates records with ICP classification results and extracted email addresses
  • Implements smart batching and delays to respect API rate limits throughout the process

How to use

  • IMPORTANT: Select cookie-free Apify actors only to avoid LinkedIn account suspension
  • Set up Apify API credentials in both HTTP Request nodes for safe LinkedIn scraping
  • Configure Airtable OAuth2 authentication and select your prospect tracking base
  • Replace the LinkedIn post URL with your target post in the initial scraper node
  • Daily Usage: Process only 1 page of reactions per day (typically 50-100 profiles) maximum
  • Customize the AI classification prompt with your specific ICP criteria and job titles
  • Test with a small batch first to verify setup and monitor both API costs and LinkedIn account health
  • Schedule workflow to run daily rather than processing large batches to maintain account safety

Requirements

  • Apify account with API access and sufficient credits for profile scraping
  • Airtable account with OAuth2 authentication configured
  • OpenAI or compatible AI model credentials for prospect classification
  • LinkedIn post URL with reactions to analyze (minimum 10+ reactions recommended)
  • Clear definition of your Ideal Customer Profile criteria for accurate AI classification

Customising this workflow

  • Safety First: Always verify Apify actors are cookie-free before configuring to protect your LinkedIn account
  • Modify ICP classification criteria in the AI prompt to match your specific target customer profile
  • Set up daily scheduling (not hourly/frequent) to respect LinkedIn's usage patterns and avoid detection
  • Adjust rate limiting delays based on your comfort level with LinkedIn scraping frequency
  • Add additional data fields to Airtable schema for storing custom prospect information
  • Integrate with CRM systems like HubSpot or Salesforce for automatic lead import
  • Set up Slack notifications for new qualified prospects or daily summary reports
  • Create email marketing sequences in tools like Mailchimp for nurturing qualified leads
  • Add lead scoring based on company size, industry, or engagement level for prioritization
  • Consider rotating between different LinkedIn posts to diversify your prospect sources while maintaining daily limits

Convert LinkedIn Post Reactions into Qualified Leads with AI and Apify

This n8n workflow automates the process of identifying, qualifying, and managing leads from LinkedIn post reactions using AI and Airtable. It's designed to help you efficiently turn engagement into actionable sales opportunities.

What it does

This workflow streamlines your lead generation by performing the following steps:

  1. Manual Trigger: The workflow is initiated manually, allowing you to control when to process reactions.
  2. HTTP Request (Apify): It makes an API call to Apify to retrieve reactions from a specified LinkedIn post.
  3. Loop Over Items: Each reaction received from Apify is processed individually.
  4. Edit Fields (Prepare for AI): Relevant data from each reaction is extracted and formatted, preparing it for AI analysis.
  5. Basic LLM Chain (AI Qualification): An AI model (via LangChain) analyzes the reaction data to qualify the lead based on predefined criteria (e.g., relevance, potential interest).
  6. Structured Output Parser: The AI's output is parsed into a structured format (e.g., JSON) for easy consumption by subsequent nodes.
  7. If (Filter Qualified Leads): The workflow checks if the AI has successfully qualified the lead.
  8. Airtable (Add Qualified Lead): If the lead is qualified, their information is added to a designated Airtable base.
  9. No Operation, do nothing: If the lead is not qualified, the workflow proceeds without adding them to Airtable.
  10. Wait: A brief pause is introduced between processing items to avoid rate limits or overwhelming services.
  11. Aggregate: After all reactions are processed, the results are collected.
  12. Code: This node likely performs additional custom logic or data manipulation after aggregation.

Prerequisites/Requirements

To use this workflow, you will need:

  • n8n instance: A running n8n instance (self-hosted or cloud).
  • Apify Account: An Apify account with access to a LinkedIn Post Scraper or similar actor to retrieve post reactions.
  • OpenAI API Key (or compatible LLM): For the Basic LLM Chain node to function, you'll need an API key for an LLM (e.g., OpenAI, Cohere, Hugging Face) configured as a credential in n8n.
  • Airtable Account: An Airtable account with a base and table set up to store your qualified leads. You'll need an API key and the Base ID/Table Name.

Setup/Usage

  1. Import the workflow: Download the JSON provided and import it into your n8n instance.
  2. Configure Credentials:
    • Apify: Create an Apify credential in n8n and link it to the HTTP Request node.
    • LLM (e.g., OpenAI): Create an OpenAI (or other LLM) credential in n8n and link it to the Basic LLM Chain node.
    • Airtable: Create an Airtable credential in n8n and link it to the Airtable node.
  3. Update Node Parameters:
    • HTTP Request (Apify):
      • Replace the placeholder URL with your actual Apify API endpoint for LinkedIn post reactions.
      • Ensure the request body and headers are correctly configured to pass your Apify API key and the LinkedIn post URL/ID you want to scrape.
    • Edit Fields (Prepare for AI): Review and adjust the fields being set to ensure they match the data structure expected by your AI model.
    • Basic LLM Chain:
      • Define your prompt for qualifying leads. Be specific about what information the AI should extract and what criteria it should use to determine qualification.
      • Configure the LLM model and any other parameters as needed.
    • Structured Output Parser: Adjust the schema if your AI output format differs.
    • If (Filter Qualified Leads): Update the condition to accurately evaluate the AI's qualification output (e.g., checking a specific field for a "qualified" status).
    • Airtable (Add Qualified Lead):
      • Select your Airtable Base ID and Table Name.
      • Map the fields from the workflow output to your Airtable table columns (e.g., name, company, qualification_status).
  4. Activate the workflow: Once configured, activate the workflow to make it ready for execution.
  5. Execute the workflow: Click "Execute workflow" on the When clicking ‘Execute workflow’ node to start the process.

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