The ICP rubric — scored, not qualified.
Most ICP exercises produce a checklist — fit/not-fit binary. Mama's ICP rubric scores accounts 0-100 across 4 weighted dimensions, which lets you rank instead of filter. The rubric is the single most-load-bearing setting in the product.
TL;DR
Four weighted dimensions out of 100: Firmographic fit · Technographic fit · Trigger signals · Persona density. Default weights are 25/25/25/25 (which produces middling scores for everyone — don't stay there). Push one dimension to 35-40% based on your motion: outbound-heavy → Trigger signals; PLG → Persona density; vertical-specific → Firmographic; stack-replacement → Technographic. Tuning is one screen. Re-scoring is automatic.
01Why scoring beats qualifying
Traditional ICP exercises produce a binary: "fit" or "not fit." The problem is that ~70% of your TAM passes the basic filter — industry, employee count, geography — and you're left with 50,000 "fit" accounts that you can't rank. The binary doesn't help you decide what to do this Tuesday.
Scoring solves this. Instead of "fit/not fit," every account gets a 0-100 score. Now you can rank. The top 200 are what you brief this week. The next 800 sit in the watch list. The rest, you ignore until a fresh signal pulls them up.
02The 4 weighted dimensions
Each dimension is scored 0-100 independently, then weighted into the composite. The weights sum to 100.
| Dimension | What it measures | Default weight |
|---|---|---|
| Firmographic fit | Industry, employee count, geography, company stage — the table-stakes filters | 25% |
| Technographic fit | Tech stack overlap — what they use that your product complements, integrates with, or replaces | 25% |
| Trigger signals | Active signals — funding, hiring, exec moves, tech changes — weighted by freshness | 25% |
| Persona density | Number of decision-maker-grade humans at the account on LinkedIn, with verified emails | 25% |
03Why the default 25/25/25/25 is wrong for almost everyone
Even weights produce the middling-score problem: most accounts land between 60 and 75. Nothing differentiates "brief today" from "brief in 60 days." The rubric becomes background noise.
One dimension should always be 35-40% based on your motion. The others drop accordingly. The skew is the point — it's what lets Mama tell you which 20 accounts to act on this week.
04Tuning patterns by motion
Four common motions, with the recommended weight skew per pattern.
Firmographic 20 · Technographic 20 · Persona 20.
Recent signals are your edge. You want the rubric to surface fresh-signal accounts to the top of the queue.
Firmographic 25 · Technographic 20 · Trigger 20.
You need many threadable champions, not just one VP. The rubric should reward accounts with depth.
Technographic 25 · Trigger 20 · Persona 15.
If your tool only fits one industry, industry fit is the gate. Don't let other dimensions over-promote out-of-vertical accounts.
Firmographic 20 · Trigger 25 · Persona 15.
You need the right preceding stack to even pitch. Without that, the account isn't qualified — they have nothing to replace.
05A worked example: Notion Labs
Hypothetical scoring for Notion (Series C SaaS, you sell a data analytics tool):
| Dimension | Raw score (0-100) | Reasoning | Weighted (outbound motion) |
|---|---|---|---|
| Firmographic | 92 | SaaS, 450 employees, Series C — matches ICP precisely | 92 × 20% = 18.4 |
| Technographic | 78 | Uses Snowflake + dbt — your tool integrates well | 78 × 20% = 15.6 |
| Trigger signals | 96 | Just raised + hired 14 data roles in 30 days | 96 × 40% = 38.4 |
| Persona density | 71 | 4 decision-makers verified — strong | 71 × 20% = 14.2 |
| Composite | 86.6 / 100 |
Composite 86.6 → top decile. Brief this week.
06What re-scoring does (and when)
Re-scoring is automatic. The trigger:
- Weight change in the rubric: all saved accounts re-score in ~30 seconds
- New signal detected on an account: trigger dimension re-scores in real time
- Persona graph updated: persona density re-scores on next overnight run
- You manually click "Re-score": from the ICP rubric screen
The score history is kept — you can see how an account's score moved over time. Useful for "is this account warming up?" diagnostics.