Clay Scoring - AI-Driven Prospect and Vendor Scoring
Satiesh Sheriff
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Posted Aug 1, 2025
Built Clay workflows for prospect scoring and vendor discovery.
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Timeline
Jun 1, 2025 - Jul 31, 2025
Clients
Facebook
Fit Scoring 10,000+ Prospects
Challenge:
Client had large prospect lists but no way to quickly identify top-fit companies.
Solution:
We built a Clay AI fit scoring model that:
Enriches with tech stack, core values, hiring signals, industry vertical, and target customer language
Scores each company 1–5
Marks non-essential fields optional so partial data still runs
Filters for 4–5 scores only
Impact:
From 10,000 prospects to the top 100 targets instantly
No more “spray and pray” prospecting
Scalable across industries
Case Study 6 — Facebook Vendor Discovery
(messy data → structured insight)
Challenge:
Find local phone/accessory vendors in a country without a central database.
Solution:
We used Appify + Clay to:
Scrape Facebook Pages with “phone” keywords
Filter and clean irrelevant results
Use AI to determine products sold, delivery areas, store type, and pricing
Verify ad activity via Meta Ads Library
Classify vendors into structured, searchable categories
Impact:
Created a usable vendor database from unstructured social media data
Repeatable across product categories (clothing, food, etc.)
Local Business Scoring
Challenge:
Identify high-fit local prospects with no pre-built list source.
Solution:
We:
Scraped 27 local businesses from Google Maps
Enriched with facility size, safety/compliance incidents, job titles, and vendor mentions
Used AI to score companies 0–100
Selected the top 4 for outreach
Impact:
Pinpointed top leads from a small dataset
Custom scoring logic makes it scalable by region and industry