# Ideal Customer Profile (ICP) Worksheet

> A structured worksheet for defining firmographic, technographic, and trigger-event criteria before a single dial goes out, plus a weighted scoring model to rank accounts.

Free template for sales managers. Topic: [Prospecting & Outbound Strategy](https://www.peerlab.ai/library/topic/prospecting-outbound-strategy/). Published by PeerLab in the [Sales Enablement Resource Library](https://www.peerlab.ai/library/). Source: https://www.peerlab.ai/library/ideal-customer-profile-icp-worksheet/

## How to use it

Complete it as a team using real closed-won and lost-for-fit deal data, then run the 10-deal validation check at the bottom to confirm the definition matches reality before building your outbound list.

## What's inside

- Firmographic criteria table: industry, company size, revenue, geography, growth stage, ownership type
- Technographic criteria table: core stack, maturity signals, integration fit
- Trigger-events checklist: funding, new leadership hire, job postings, expansion, M&A, compliance deadline
- Anti-ICP disqualifiers section (industries, size thresholds, behavioral red flags)
- Weighted scoring model (100-point rubric across 6 criteria)
- 10-deal validation sanity check against closed-won and lost-for-fit deals
- Quarterly review cadence guidance
- Ownership-type field (VC-backed / PE-backed / family-owned / public)

A structured worksheet for defining exactly who belongs on your outbound list, before a single dial goes out. Fill this in as a team, not solo; the biggest ICP mistakes come from one person's assumptions going unchallenged.

**How to use this:** Complete each section, then run 10 of your best closed-won deals through the scoring model at the bottom to sanity-check your definition against reality, not just intuition.

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## Section 1: Firmographic Criteria

| Criterion | Your Definition |
|---|---|
| Industry / vertical (include and exclude) | ______________________ |
| Company size, employee count range | ______________________ |
| Company size, annual revenue range | ______________________ |
| Geography / region served | ______________________ |
| Growth stage (startup / scaling / mature / enterprise) | ______________________ |
| Org structure (centralized vs. distributed teams, single site vs. multi-site) | ______________________ |
| Ownership type (VC-backed, PE-backed, family-owned, public) | ______________________ |

## Section 2: Technographic Criteria

| Criterion | Your Definition |
|---|---|
| Core tech stack they must already have (e.g. CRM, specific platform) | ______________________ |
| Tools that indicate maturity/readiness (e.g. already using a category-adjacent tool) | ______________________ |
| Integration requirements you can/can't support | ______________________ |
| Tools that signal poor fit (e.g. still on spreadsheets for something that needs a system) | ______________________ |

## Section 3: Trigger Events (What Makes Them Ready NOW)

List the events that historically correlate with your best deals moving fast:

- [ ] Recent funding round (Series ___ or later)
- [ ] New leadership hire in relevant function (title: ______________________)
- [ ] Active job posting for a role your solution supports/replaces
- [ ] Recent expansion (new office, new market, new product line)
- [ ] Recent M&A activity
- [ ] Compliance or regulatory deadline
- [ ] Public statement about a relevant initiative (earnings call, press release, LinkedIn post)
- [ ] Other: ______________________

## Section 4: Firmographic & Behavioral Disqualifiers (Anti-ICP)

Be as specific here as in Section 1, knowing who to skip saves as much time as knowing who to call.

- Industries to exclude: ______________________
- Company size below/above which deals historically don't close: ______________________
- Behavioral red flags (e.g. no dedicated budget owner, team of 1, high vendor churn): ______________________
- Past deals that looked like ICP but weren't: what did they have in common: ______________________

## Section 5: Weighted Scoring Model

Score each prospect account 1–5 on each criterion, multiply by weight, sum for a total out of 100. Anything scoring below your threshold (commonly 60) goes to a lower-priority list or gets disqualified.

| Criterion | Weight | Score (1–5) | Weighted Score |
|---|---|---|---|
| Industry fit | 20% | ___ | ___ |
| Company size fit | 20% | ___ | ___ |
| Technographic fit | 15% | ___ | ___ |
| Active trigger event present | 25% | ___ | ___ |
| Geographic fit | 10% | ___ | ___ |
| No major disqualifiers present | 10% | ___ | ___ |
| **Total** | 100% | | **___ / 100** |

*Adjust weights to match what actually predicts closed-won in your historical data, don't guess. Trigger events are weighted highest here because they predict urgency, and urgency is what turns a fit account into a moving deal.*

## Section 6: Validation, The 10-Deal Sanity Check

Pull your 10 most recent closed-won deals and your 5 most recent lost-for-fit deals. Score all 15 against the model above.

- [ ] Do closed-won deals cluster at 70+? If not, your weights or criteria are off.
- [ ] Do lost-for-fit deals cluster below 50? If not, you're missing a disqualifier.
- [ ] Is there a criterion every closed-won deal shares that isn't in your model yet? Add it.
- [ ] Is there a criterion you assumed mattered that shows no correlation? Cut it or reweight it.

**Review cadence:** revisit this worksheet quarterly: trigger events and technographic signals shift faster than firmographics, and a stale ICP quietly inflates your top-of-funnel with dials that were never going to close.