Create Ideal Customer Profiles from LinkedIn Data with Airtop and Claude AI

Created by

Airtop

Last update

Last update a month ago

Use Case

This automation helps marketing and sales teams define their Ideal Customer Profile (ICP) using real LinkedIn profiles of current high-fit customers. By enriching and analyzing profile data, it generates a clear ICP definition and scoring methodology for future targeting.

What This Automation Does

This automation analyzes LinkedIn profiles of your existing customers and produces:

A structured ICP definition
A scoring model to evaluate future prospects
A Google Boolean search string to find similar prospects

Input:

LinkedIn profile URLs of existing high-fit customers (e.g., https://www.linkedin.com/in/amirashkenazi/)

Output:

A Google Doc containing the ICP analysis and scoring methodology

How It Works

Trigger: Waits for a chat message containing one or more LinkedIn profile URLs.
AI Agent: Parses and processes the URLs.
Airtop Data Enrichment: Uses Airtop to extract structured information from each LinkedIn profile (e.g., job title, company, experience, skills).
Memory: Maintains state between inputs for consistent analysis.
LLM Analysis: Uses Claude 3.7 Sonnet to synthesize enriched data into a meaningful ICP.
Google Docs: Automatically creates a new doc with a timestamped title and appends the ICP definition.

Setup Requirements

Airtop Profile connected to LinkedIn, Insert the profile name in the Airtop Tool
Airtop API credentials. Get it free here
If you choose to activate saving the profiles in Google Docs you will need OAuth2 credentials (or just copy the ICP definition from the chat)

Next Steps

Use the ICP for Scoring: Feed new LinkedIn profiles through the same Airtop enrichment and use the scoring function to evaluate fit.
Automate Target Discovery: Plug the Boolean search output into LinkedIn, Google, or People Data Labs for ICP-matching lead generation.
Refine Continuously: Repeat the workflow as your customer base grows or segments evolve.