icp

name	icp
description	Defines the Ideal Customer Profile — the firmographic and behavioral traits of accounts that get the most value and convert and retain best, plus qualifying signals, anti-signals, and where to find them. Use when you say "who's our ICP", "who should sales target", "which accounts retain best", "we're closing the wrong customers", or "tighten our targeting" — for B2B accounts, not individual buyer personas.
tier	guided
time	45-60 min
inputs	your best and worst accounts, and why
outputs	strategy/icp.md

Ideal Customer Profile

An ICP describes the accounts worth winning — the firmographic and behavioral shape of companies that extract the most value, convert efficiently, and stick. It is a filter, not a wish list. The job is to point everyone (sales, marketing, product, success) at the same narrow target and give them the signals to recognize it and the anti-signals to walk away.

A real ICP is grounded in your own retention and conversion data, not aspiration. The sharpest move is naming who to avoid: the accounts that close fast, churn faster, and poison your metrics and your roadmap. An ICP that excludes nobody isn't an ICP — it's a market description.

Grounded in: Crossing the Chasm — Geoffrey Moore: define the best-fit account by who gets the most value and retains. Go deeper (The Product Channel): How to Create a Customer Persona

When to use this

• Sales is closing logos that churn in two quarters, or win rates are sliding and you suspect mis-targeting.
• Marketing spend is spraying — high lead volume, low qualified-pipeline, rising CAC.
• You're entering a new segment and need to define who you're actually going after.
• Success is drowning because too many low-fit accounts need hand-holding to get any value.
• Annual planning: you need one shared definition of "good customer" to align GTM and roadmap.

Before you start (gather these)

• Customer outcome data — your best and worst accounts by retention, expansion, and value realized (NRR, churn cohort, NPS, product usage, time-to-value). This is the ground truth the ICP is reverse-engineered from.
• Firmographics for those accounts — industry, size (employees/revenue), geography, tech stack, business model, GTM motion.
• Win/loss and sales-cycle data — who closes fast vs. stalls, deal size, who you lose to and why.
• Your product's core value — the specific problem you solve and the conditions under which it pays off (this defines fit at the mechanism level, not just the surface).

If you have fewer than ~10 retained accounts to learn from, or two-plus of the above are missing or vague, ask 2–4 sharp questions first rather than inventing a profile: e.g. "Which 5 accounts would you be most upset to lose, and why?" / "What's true about every account that gets fast value — and every account that churns?" / "What deals do you regret closing?" / "Where does the product stop working?" If the data is already provided, proceed and state your assumptions inline (e.g. "Assuming the 6 named accounts are representative of fit; flag if any were lighthouse deals").

Cold start (genuine zero-data / pre-PMF). If there are no retained accounts to reverse-engineer from — pre-launch, pre-PMF, or a brand-new segment — say so plainly and label the entire output "ICP HYPOTHESIS — not evidence" in the title and set Confidence: Low. Do not present inferred bands as measured. Then list the validation steps to convert hypothesis → evidence before GTM locks: (1) run 8–12 problem-discovery interviews against the hypothesized segment; (2) ship a small paid pilot / design-partner cohort and watch time-to-value and willingness-to-pay; (3) instrument activation + early-retention for the first cohort; (4) define the kill criteria that would falsify the hypothesis (e.g. "if pilots in segment X don't reach value in 30 days, drop X"). Revisit the ICP once ~10 accounts have real outcome data.

Process

1. Anchor on your best and worst accounts. List your top retainers/expanders and your churned/regretted accounts. Don't theorize yet — just observe. The ICP lives in the contrast between these two sets.
2. Extract the firmographic shape. Find the traits the winners share and the losers don't: size band, industry, business model, tech stack, geo, growth stage. Be specific — "mid-market" is useless; "150–800 employees, Series B–C, US/EU" is targetable.
3. Extract the behavioral and situational fit. What's true about good-fit accounts beyond their stats — a trigger (just raised, new exec, regulation), a workflow they already run, a pain acute enough to pay now, the technical or organizational readiness to adopt. This is where most weak ICPs are thin; it's also where the real signal is.
4. Name the anti-signals. Write down who looks tempting but burns you: wrong size, wrong maturity, a use case the product half-serves, buyers with no budget authority, accounts that need heavy customization. Make these disqualifying, not "proceed with caution."
5. Define qualifying signals you can actually observe. For each fit trait, name the observable proxy a rep or marketer can check in the wild (a job posting, a funding event, a tool in their stack, a title on the buying committee). A trait you can't detect can't drive targeting.
6. Map where they congregate. Translate the profile into sourcing: data filters (LinkedIn/Apollo/Clearbit criteria), communities, events, integrations/marketplaces, search intent, partner channels. End on go find them here, not just they look like this.
7. Pressure-test and bound it. Sanity-check the profile against accounts it would wrongly include or exclude. State the addressable size implied. If the ICP describes most of your funnel, it's too loose — tighten until it excludes real, named deals.

Output template

# Ideal Customer Profile — [Product / Segment]
Owner: [name/role] · Date: [date] · Based on: [N retained + N churned accounts]
Confidence: [High / Medium / Low — and why. State explicitly if any figures are
illustrative/inferred rather than measured, and name the exact dataset to validate
against before GTM filters lock — e.g. "Medium: bands inferred from 6 named accounts,
not the full book; validate against NRR-by-cohort export in [warehouse/CRM table] before
sales filters go live."]

## One-line ICP
[A targetable sentence. e.g. "Series B–C B2B SaaS companies, 150–800 employees,
US/EU, with a 3+ person data team and a modern warehouse, feeling reporting pain
acute enough to have a budgeted line item this year."]

## Why these accounts (the value mechanism)
- We create the most value when: [the condition under which the product pays off]
- Time-to-value for a good-fit account: [e.g. < 30 days]
- Unit economics: [avg ACV $___ · CAC $___ · gross margin ___% · payback ___mo · LTV:CAC ___]
  — so exclusions are defensible on margin, not just retention (a segment that retains
  but carries negative margin or a 24mo payback is still a disqualify).
- Evidence: [best accounts realize X; this trait separates retainers from churners]

## Firmographic fit
| Dimension       | Target                         | Observable signal                          |
|-----------------|--------------------------------|--------------------------------------------|
| Company size    | [150–800 employees]            | [LinkedIn headcount, Clearbit]             |
| Stage / funding | [Series B–C]                   | [Crunchbase, recent raise news]            |
| Industry        | [B2B SaaS, fintech]            | [SIC/NAICS, site, sales filter]            |
| Geography       | [US, UK, EU]                   | [HQ / billing region]                      |
| Business model  | [recurring revenue, PLG+sales] | [pricing page, motion]                     |
| Tech stack      | [uses Snowflake + dbt]         | [job posts, BuiltWith, integrations]       |

## Behavioral & situational fit
- **Trigger** — [just raised / new VP / hit a scale wall / new regulation]. Signal: [______]
- **Existing workflow** — [already runs X manually in spreadsheets]. Signal: [______]
- **Pain intensity** — [acute enough to have budget *now*, not "someday"]. Signal: [______]
- **Readiness** — [has the team/tooling/exec sponsor to adopt]. Signal: [______]
- **Buying committee** — [econ buyer: ___ · champion: ___ · user: ___]

## Anti-signals — who to AVOID (disqualifying)
| Anti-signal                        | Why it burns us                          |
|------------------------------------|------------------------------------------|
| [< 50 employees]                   | [can't realize value; churns; high touch]|
| [no in-house ___ team]             | [needs us to do the work; never adopts]  |
| [requests heavy customization]     | [drags roadmap; one-off; low margin]     |
| [no budget authority in the room]  | [long cycle, no close]                   |
| [use case we only half-serve]      | [sets false expectations; bad reviews]   |

## Qualifying checklist (rep/marketer can run this live)
- [ ] [Size in band] · [ ] [Stack present] · [ ] [Trigger fired]
- [ ] [Champion identified] · [ ] [Budget plausible] · [ ] [No disqualifying anti-signal]
→ 5+ checked = ICP. 3–4 = monitor / nurture. <3 or any anti-signal = disqualify.

## Tiers (route effort, don't just gate)
| Tier        | Definition                                  | What GTM does with them                    |
|-------------|---------------------------------------------|--------------------------------------------|
| ICP (Tier 1)| 5+ checks, no anti-signal, margin-positive  | [named sales motion, calendar time, ABM]   |
| Adjacent    | 3–4 checks, no disqualifying anti-signal    | [nurture / product-led / wait for trigger] |
| Disqualify  | <3 checks OR any anti-signal OR bad margin  | [politely decline / self-serve only]       |

## Where to find them
- **Data/filters:** [Apollo/LinkedIn Sales Nav query: ______]
- **Intent:** [keywords / G2 category / review activity: ______]
- **Communities & events:** [______]
- **Integrations / marketplaces / partners:** [______]

## Boundaries
- Implied addressable accounts: [~N]
- Deliberately excluded (and why): [______]
- Revisit when: [data refresh date / segment shift]

Avoid (anti-patterns)

• Describing your dream customer instead of your real one. If the profile isn't reverse-engineered from accounts that actually retained, it's marketing fan-fiction. Anchor on data.
• An ICP with no anti-signals. If nobody is disqualified, GTM will chase everything. The exclusions are the point.
• Traits you can't observe in the wild. "Values data-driven decisions" can't be filtered or detected. Every trait needs an observable proxy or it's decoration.
• Confusing the ICP (the account) with the persona (the human buyer). This skill defines which companies; the buying committee inside them is a related but separate question — keep them distinct.
• Too broad to be useful. If your ICP matches 70% of inbound, you haven't made a decision. Tighten until it excludes named, real deals you'd otherwise have taken.

Tips

• The strongest evidence is your churn list. Whatever every churned account shares — and your retainers don't — is your sharpest anti-signal. Mine the failures first.
• Tier it, don't just gate it. A clean "ICP / adjacent / disqualify" tiering routes effort better than a single yes/no — adjacent accounts get nurture, not sales calendar time.
• Make it a one-liner a rep can repeat from memory. If the ICP can't be said in a sentence on a sales floor, it won't change behavior, and behavior change is the only reason to write it.
• Re-derive it every couple of quarters. As the product matures and you move upmarket (or down), the accounts that fit shift. A stale ICP quietly mis-aims the whole funnel.