--- name: icp-builder description: "Use this skill when you need to define or sharpen an ideal customer profile (ICP) for B2B sales: starting outbound, the reply rate is low, the pipeline is full of bad-fit deals, or leadership asks who we should really sell to. Analyses closed-won and closed-lost deals, finds the firmographic, technographic and situational traits that predict a win, and produces a one-page ICP with must-haves, nice-to-haves, disqualifiers and list-building filters. Works for founders with five customers and for teams with hundreds." license: MIT metadata: title: ICP builder category: research tags: "Founders, Sales Leaders, Marketing, RevOps" author: prosyo version: "1.0.0" keywords: "ideal customer profile template, icp builder, define icp b2b, claude icp skill, icp framework" inputs: "Closed-won and closed-lost deals, Product and pricing, Sales cycle notes" outputs: "One-page ICP, Disqualifiers, List-building filters, Confidence notes" --- 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: | Layer | Examples | | --- | --- | | Firmographic | Industry, headcount band, revenue band, region, business model | | Technographic | Tools they already run, data maturity, stack that integrates with yours | | Situational | Hiring for a role, new leader in seat, funding, expansion, a regulation deadline | | Behavioural | How they buy: committee size, procurement, self-serve trial or demo | Situational traits usually predict timing better than firmographics predict fit. Read [references/icp-scoring.md](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: Why they buy: | Trigger: Must-haves: <3-5 traits> Nice-to-haves: <2-4 traits that raise priority> Disqualifiers: <3-5 hard noes> Buyer: 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." --- <!-- references/icp-scoring.md --> # ICP trait comparison and scoring ## The comparison table Build one row per candidate trait. Use shares, not counts. | Trait | Top 20% of customers | Lost and churned | Gap | Keep? | | --- | --- | --- | --- | --- | | 5-50 locations | 78% | 31% | +47 | Must-have | | Has an operations leader | 70% | 40% | +30 | Must-have | | Uses a modern POS | 64% | 52% | +12 | Not predictive | | Raised funding in 12 months | 35% | 9% | +26 | Nice-to-have (timing) | | Single location | 4% | 38% | -34 | Disqualifier | 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. | Component | Points | Notes | | --- | --- | --- | | Must-haves met | 0-50 | Split evenly across must-haves; any miss caps the score at 49 | | Nice-to-haves | 0-20 | Split evenly | | Active trigger in last 90 days | 0-20 | Funding, new leader, relevant hiring, expansion | | Reachable buyer identified | 0-10 | Named person with a verified channel | | Disqualifier present | Score = 0 | Overrides everything | 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%.