ICP builder Claude skill

Use this when the question is "who should we sell to?". It produces a one-page ICP that a rep, a marketer and a list builder can all act on the same day.

Inputs

Ask for as much as exists, then work with what you get:

• Closed-won deals for the last 12 to 24 months: company, size, industry, region, deal value, cycle length, who signed, why they bought.
• Closed-lost and churned accounts with the reason.
• The product’s core job and price range.
• If there are fewer than 10 customers: the founder’s notes on the best five conversations, plus the best five failures.

The play

1. Rank customers by value, not by count

Sort won deals by a blend of revenue, cycle speed and retention. The top 20% is the real ICP; the long tail is noise. A segment with many small, slow, churn-prone deals looks big on a pie chart and loses money.

2. Find the traits that separate winners from losers

For each trait, compare the share among top customers with the share among lost or churned ones. Traits worth keeping show a gap of at least 20 points. Look at four layers:

Situational traits usually predict timing better than firmographics predict fit. Read references/icp-scoring.md for the comparison table and the scoring weights.

3. Write the pain in the buyer’s words

Pull phrases from call notes, emails and reviews. "We lose two days a month reconciling payouts" beats "operational inefficiency".

4. Set disqualifiers

Hard noes save more time than must-haves. Typical ones: below a headcount that can afford the price, a region you cannot serve, a tech stack you cannot integrate with, a business model where the pain does not exist.

5. Translate it into filters

Every trait becomes a filter a list builder can run: industry codes, headcount ranges, job titles, keywords in job listings, technologies detected. Flag traits that cannot be filtered and must be checked by research.

Output format

ICP: <one-line name, e.g. "Multi-location restaurant groups, 5-50 sites">
Why they buy: <pain in their words>  |  Trigger: <what makes now the moment>
Must-haves: <3-5 traits>
Nice-to-haves: <2-4 traits that raise priority>
Disqualifiers: <3-5 hard noes>
Buyer: <title that signs>  Champion: <title that feels the pain>  Users: <who uses it>
Filters: <list-builder ready criteria>
Confidence: <high / medium / low and why: sample size, data gaps>

What good looks like

• Narrow enough that a rep can name 50 target accounts from memory after reading it. "B2B SaaS companies" is not an ICP.
• It includes a trigger. Fit without timing produces polite "not right now" replies.
• Disqualifiers are as specific as the must-haves.
• The confidence note is honest: an ICP built from six customers says so and lists what to test next.
• The common mistake is describing the customers you want instead of the customers who bought, renewed and expanded.

Rules

• MUST base every trait on deal data or explicit notes, and say which.
• MUST include at least three disqualifiers.
• NEVER mix the economic buyer and the end user into one persona.
• NEVER present a trait with fewer than five supporting accounts as proven; mark it as a hypothesis.

Example prompts

• "Here are our last 40 closed-won and 25 closed-lost deals. Build our ICP."
• "We have 7 customers. Help me write a first ICP and tell me what to test."
• "Our reply rate dropped to 1%. Check whether our ICP is too broad."

The comparison table

Build one row per candidate trait. Use shares, not counts.

Rules of thumb:

• Gap of 20 points or more: keep the trait.
• Gap of 10 to 20 points: nice-to-have, retest next quarter.
• Gap under 10 points: drop it, however intuitive it sounds.
• Strong negative gap: candidate disqualifier.

Account fit score

Turn the ICP into a 100-point score so lists and inbound leads can be ranked the same way.

Tiers: 80+ is Tier 1 (research and personalise), 60-79 is Tier 2 (segment-level personalization), under 60 stays out of outbound.

Small-sample mode (under 10 customers)

1. Interview the founder about the five best and five worst conversations.
2. List the traits the best five share. Each becomes a hypothesis, not a rule.
3. Pick the two most testable hypotheses and design a 200-contact test for each.
4. After 2 to 3 weeks, keep the segment with the higher positive reply rate (a 2x gap is meaningful at this size; a 20% gap is not).

Worked example

A payroll tool for restaurants had 34 customers. Revenue-weighted, the top seven were all groups with 8 to 30 locations and a dedicated operations manager. Single-location restaurants were 40% of logos but 70% of churn. The new ICP dropped single sites entirely, added "hiring an operations or payroll role" as the trigger and moved headcount from a filter to a check. Positive reply rate on the next 600 contacts rose from 1.1% to 3.4%.