03
signal scoring
Signal Scored Account Prioritization for GTM Agencies
Built a live scoring system that reads funding, hiring, and product signals to tell reps which accounts are in a buying window right now.
clayclaygentfind jobsmeta prompting
situation
Reps at a GTM agency were working a flat, unprioritized list, so outreach timing was essentially random and accounts got hit before they were in a buying window. The thesis: agencies want companies big enough to afford a retainer but small enough to move fast, roughly 50 to 1,000 employees. But headcount is a bracket, not a signal. The question was what separates an account ready to buy now from one that just fits the size filter.
the build
- Ranked accounts on live buying signals, not static facts like size or industry. Three signals, each a real sign a company is about to invest:
- Recent funding — weighted so fresher rounds count for more.
- Hiring intent — job posts for GTM engineers, RevOps analysts, SDRs, and BDRs, weighted by how strongly each role says "we're investing in go-to-market."
- A custom "changelog" reader. A changelog is the public list of product updates a company posts; I built an AI reader that scans it and flags anything worth an outreach angle. Everything sorts into three priority tiers, with a "don't contact" bucket left out entirely.
- That changelog reader was the hard part — and the edge. Almost nothing in a changelog matters, so I refined the AI prompt over many passes until it could reliably answer three things: is there a changelog, is there a relevant change, and what exactly changed. A line like "now supports Shopify integration" from last week becomes an opener about the tough first months of partner adoption. That's timing plus context, not a cold template.
process map
1Filter to the right company size
2Score the live buying signals
3Add up the weighted total
4Sort into priority tiers
5Top tiers go to outreach, ranked
impact
- A ranked queue replaces a morning of scrolling a flat list — reps start with who's actually in a buying window.
- The changelog signal turns a cold email warm, grounded in something the company did last week.
- More replies, fewer touches to book a meeting — the payoff of that specificity.
tech stack
currently using
- Clayenrichment, scoring, tiering
- Claygentchangelog reading
- meta promptingthe relevance filter
- Find Jobshiring signals
phase 2
- Web scraping for changelog backfill
- Crunchbase or PitchBook for cleaner funding data
- Slack alerts on Tier 1 entry
- CRM sync to push tiered accounts into a rep's live queue
reflections
- The edge came from process, not tooling. You can and should plan the logic up front, but the real differentiator — the changelog reader — only showed up after a lot of trial and error and sitting with the problem longer than felt comfortable.
- The eureka doesn't arrive on schedule. Sometimes the edge is there and you find it; sometimes it isn't and you ship with what you have. The job is doing the reps that make the breakthrough possible, not assuming it turns up on the first try.