AI Influencer Fit/Scoring Automation
Scores influencer alignment with brand narratives using Claude 3.7 Sonnet and Apify scrapes, eliminating manual guesswork from marketing campaigns.
Impact Summary
The Problem
Marketing teams spend days manually researching influencers, often relying on gut-feel to pick creators. This leads to poor brand fit, diluted messaging, and wasted campaign budgets on influencers with fake engagement or misaligned audiences.
This automation uses LLMs to cross-reference scraped influencer metrics against the brand's exact ICP, target audience, and campaign goals—automatically generating a weighted score and surfacing top-tier creators for every brief.
Creator acceptance rate
Profiles scored per hour
Quick Video Demo
How the workflow runs end to end
From raw data to booked meetings in 4 autonomous steps
Data Ingestion
Loads brand ICP, campaign goals, and raw scraped influencer profiles (followers, engagement rate, bio) from Google Sheets.
Pre-Filtering
Runs an initial logic pass to remove low-engagement, low-follower, or incomplete profiles, saving LLM tokens.
Claude 3.7 Analysis
Claude evaluates the influencer against the brand's exact values, scoring them across 5 weighted dimensions including Brand Safety and Audience Match.
Tiering & Export
Calculates a final weighted score, assigns a Priority Tier (1-4), and exports the ranked shortlist back to Sheets with exact reasoning.
Before vs After
The Old Way
- Manual influencer research
- Gut-feel creator selection
- High risk of brand safety issues
- Days spent vetting profiles
The Automated Way
- AI-ranked creator shortlists
- 80% creator acceptance rate
- Scored against exact brand ICP
- Minutes, not days
Under the
hood
Built with modern, scalable low-code tools and enterprise-grade APIs to ensure reliability and speed.