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What this is: the rules Clay's GTM Ops team uses to carve accounts into rep territories: the segments, the Account Fit Score that ranks accounts, the single eligibility rule, and what gets excluded before anything is scored. Shared as a reference for How Clay Uses Clay: Territory Planning.
The whole rule in one line: an account is carved into a territory when its Account Fit Score is above 60, it has no open opportunity, and no rep is already assigned. Everything else here is how we get to that score and who the account goes to.
Use it to: decide your own segments, pick and weight your scoring criteria, and write down your exclusions before you carve. The specific lists are ours; the structure is what to copy.
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Apply in order. The first step that decides an account wins.
Segment is derived from the Ultimate Parent's employee count. Rep capacity is the number of accounts a rep holds in their book.
| Segment | Employees | Rep capacity (accounts) |
|---|---|---|
| SA (Sales-Assist) | 50–350 | 60 |
| SL1 / SL2 (Sales-Led, shared territory) | 350–3,000 | 50 (+10 in EMEA) |
| SL3 (Sales-Led) | >3,000, excluding F500 | 45 |
Books are rebalanced on a rolling basis, not in an annual reset. The closer to continuous the better; the limit is how long a rep needs to work an account, not the tooling.
Managers and reps can reject an account from a book, but must give a reason. Rejections feed the next scoring iteration. Expect roughly 10–15% of a book to be flagged at first and drive that down over time.
Score each factor from 0–100, multiply by its weight, add them up → Account Fit Score (0–100).
<aside> 💡
Where the factors came from: research into what actually changes how a company buys: their GTM motion, the tooling they use, who they target, and the shape of their go-to-market team. Some are standard enrichment fields; the rest come from AI research on the account.
No black box: every component score is written back next to the total, so a rep can see exactly why an account scored the way it did.
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| Factor | Weight | How to score (0–100) | Data needed |
|---|---|---|---|
| Technographics | 28% | CRM + data warehouse + competing data vendor = 100 · CRM + DW = 80 · any one = 50 · none = 20 | Tech stack |
| GTM Motion | 20% | PLG + Sales (hybrid) = 100 · PLG only or Sales only = 75 · weak / early = 50 | GTM motion (AI research) |
| 12m Employee Growth | 12% | ≥ 30% = 100 · 15–30% = 70 · 5–15% = 40 · < 5% or shrinking = 10 | Headcount now vs 12 months ago |
| Sales Team | 12% | GTM Engineer hired, or sales HC ≥ 50 or ≥ 5% of employees = 100 · 10–49 = 70 · 1–9 = 40 · none = 10 | Sales headcount, SDR job postings |
| Fundraising | 10% | Equity raise in last 18 months ≥ $20M = 100 · ≥ $5M = 70 · older or smaller = 40 · none or debt only = 0 | Last round date, amount, type |
| Business Model | 10% | B2B = 100 · B2B + B2C = 70 · B2C = 30. Then SaaS +15, non-SaaS −15, AI-native +15 (clamp to 0–100) | Business model, SaaS flag (AI research) |
| Ops Team | 4% | Ops / RevOps HC ≥ 3 = 100 · 1–2 = 70 · 0 = 20 | Ops / RevOps headcount |
| Employees (Size) | 4% | Linear: 0–500 employees → 0–100, capped at 100 | Employee count |
| Grade | Account Fit Score |
|---|---|
| Best Fit | ≥ 80 |
| Good Fit | 65–79 |
| Moderate Fit | 50–64 |
| Low Fit | < 50 |
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An account is carved into a territory only if all three hold: