# ICP Builder

An ICP written in the vocabulary LinkedIn search understands, derived from deals you actually closed rather than from who you wish would buy.

- Version 1.0, updated 18 September 2026
- Works with: Claude, ChatGPT, Gemini or any capable assistant
- Source: https://www.linkediz.com/prompts/icp-builder

## Use when

- You are about to build your first Sales Navigator search and do not want to guess the filters.
- Your reply rate is fine on some accounts and zero on others, and you cannot articulate the difference.
- You are briefing an agency, an SDR or an AI agent and need the target definition in writing.
- You are re-doing an ICP that was written before you had customers.

## Variables to replace

- `{{COMPANY}}` — Your company. Name plus the one line you would use to introduce it.
  Example: Cadence — payroll reconciliation software for multi-entity finance teams
- `{{PRODUCT}}` — What you sell. The thing, the outcome it produces, and who signs for it.
  Example: A reconciliation layer that closes multi-entity payroll in two days instead of nine. Bought by the Finance Director, used by the payroll manager.
- `{{EXCLUSIONS}}` — Exclusions. Who is not a fit, and what you must never say. Both save more time than the inclusions.
  Example: Not a fit: single-entity companies, agencies, anyone under 200 staff. Never claim SOC 2 — certification is in progress, not complete.

## How to use

1. Export your last 10–20 closed deals — won and lost. Lost deals are half the signal and the half everyone leaves out.
2. Paste them in place of the bracketed block. One line each is enough; the model does not need your CRM schema.
3. Fill the three variables. If you have no exclusions yet, write "none known" rather than deleting the section — it makes the model ask for them at the end.
4. Run it. Read the "What I could not determine" section first: it tells you whether the rest is worth trusting.
5. Keep the search-ready summary. It is the input to the Sales Navigator Search Builder and to ICP.md.

## Do not

- Feeding it only won deals. Without losses the model has nothing to contrast against and will describe your customer base rather than your ideal one.
- Accepting a trigger you cannot observe. "They are scaling" is not a trigger; "they posted two roles in the same function in 60 days" is.
- Letting it produce more than five firmographic attributes. Anything below the top five does not change who you contact.

## Context files this expects

- `ICP.md` — https://www.linkediz.com/ai/files/icp

## Where this sits

- Next step: Sales Navigator Search Builder — https://www.linkediz.com/prompts/sales-navigator-search-builder

---

## The prompt

You are a revenue operations analyst. Your job is to turn closed-deal evidence into a targeting definition that can be typed into LinkedIn Sales Navigator without further interpretation.

## Objective
Produce an ideal customer profile for {{COMPANY}} that another person — or a search tool — could act on without asking me a single follow-up question.

## What I sell
{{PRODUCT}}

## Evidence
Below are my recent deals. For each one I have given what I know; some fields will be missing.

[PASTE 10–20 DEALS. One line each: company, headcount, industry, country, buyer title, won or lost, deal size, and — if you know it — what changed at that company shortly before they bought.]

## Exclusions I already know
{{EXCLUSIONS}}

## Method
1. Separate the won deals from the lost ones before looking for patterns. A pattern that appears in both is not a qualifier, it is background.
2. For each attribute, state the RANGE the wins cluster in, not the average. "200–800 staff" is actionable; "average 470 staff" is not.
3. Identify the trigger: the observable event that preceded the wins. It must be something visible from outside the company — a funding round, a new hire in a specific role, an office opening, a product launch, a regulatory date, a job posting. "They realised they had a problem" is not observable and does not count.
4. Where the evidence does not support a conclusion, say so. An ICP with three confident attributes and two honest gaps is more useful than five confident-sounding guesses.
5. Rank the attributes by how much they separate wins from losses. The top two are the ones that go in the search; the rest are tiebreakers.

## Constraints
- Every attribute must map to something LinkedIn can filter or something visible on a company page or profile. If an attribute cannot be observed on LinkedIn, put it in the "qualify on the call" section instead of the ICP.
- Do not invent industry categories. Use LinkedIn's own industry names.
- Headcount must be expressed in LinkedIn's bands: 1-10, 11-50, 51-200, 201-500, 501-1000, 1001-5000, 5001-10000, 10001+.
- No personas, no jobs-to-be-done narrative, no empathy map. This is a targeting document.

## Output format
Return exactly these sections and nothing else.

### ICP in one sentence
One sentence. It must contain a headcount band, an industry or category, a geography and the trigger.

### Firmographics
| Attribute | Value | Confidence | Evidence |
|---|---|---|---|
Confidence is high / medium / low and must reflect how many wins support it.

### Buying committee
| Title | Role in the deal | What they are measured on |
|---|---|---|
Economic buyer, user and blocker, at minimum. Use the titles that appeared in my deals, not generic ones.

### The trigger
The observable event, how to spot it on LinkedIn, and how long the window stays open after it happens.

### Exclusions
Who to leave out and why. Include anything my evidence shows losing, not just what I told you.

### What I could not determine
List the attributes the evidence was too thin to support, and say what data would settle each one.

### Search-ready summary
A short block I can paste into the next prompt: headcount band, industries, titles, geographies, trigger, exclusions.

---

## Notes

Written to a fixed structure and run through Claude and ChatGPT to confirm each one returns the output format it declares. That is not the same as a measured reply-rate benchmark, and nothing here claims one — if you run these at volume and have numbers, we want them.

---

Linkediz LinkedIn AI Toolkit — v1.0, 18 September 2026
Source and updates: https://www.linkediz.com/prompts/icp-builder
LinkedIn plan limits and prices in this file: https://www.linkediz.com/linkedin-limits