| name | icp-definer |
| description | Define an Ideal Customer Profile from observed paying-customer data, churn data, and qualitative signals. Use when defining or tightening ICP, when sales efficiency drops (rising CAC, falling close rate), or when support load suggests wrong-fit customers. |
| tier | core |
| category | strategy |
| rr_companion | none |
| needs_mcp | [] |
| license | MIT |
| version | 1.0.0 |
| maintainer | jarektkaczyk |
| created-by | jarektkaczyk |
ICP Definer
An ICP describes the customer who gets the most value, fastest, with the lowest support cost, and stays longest. It is data-driven, not aspirational. Written ICPs that read like marketing personas ("Marketing Mary, 32, loves coffee") fail — they describe the human, not the buying conditions.
When to use
- Pre-Series A / pre-PMF: too many segments, can't pick
- Post-PMF: tightening for efficient growth
- Diagnosing rising CAC, falling LTV, or support overload
- Multi-audience product (internal users vs external SaaS buyers) — produce separate ICPs per audience
- Entering a new market where existing ICP may not transfer
When NOT to use
- You have fewer than 10 paying customers — the data is too thin; use qualitative hypothesis docs instead
- You haven't shipped yet — no customers means no ICP data; use a target-customer hypothesis with an explicit review date
- You're trying to justify a segment you've already decided to target — this process surfaces truth, not confirmation
- The goal is messaging polish, not targeting discipline — use
positioning-canvas instead
Use this instead
- positioning-canvas — once ICP is locked, use this to build the messaging that speaks to those buyers; ICP feeds positioning step 4
- audience-builder — for sourcing and scoring outbound lists against a defined ICP
- gtm-motion-picker — use this if the question is "which go-to-market motion fits our ICP" rather than "who is our ICP"
Required inputs
Ask the user for these before running. If missing, name what's missing in the artifact.
- Customer list — paying customers with: company, plan/MRR, signup date, activation date, churn date (if churned), support ticket volume, NPS/CSAT if available
- Top 10 healthiest accounts — the user's gut pick (cross-check against data later)
- Top 5 worst-fit accounts — high support, low usage, churned, refund requests
- Lost-deal notes if available — common reasons prospects didn't buy
- Win-call recordings or notes if available — exact words customers use
If the user has fewer than 20 paying customers, mark the ICP provisional — the data is too thin for statistical confidence.
The 6-dimension framework
Each dimension answers a different sales/marketing question.
1. Firmographics (who they are)
- Industry / sub-industry
- Company size (employees, revenue, or whatever predicts best)
- Geography (only if it predicts — not by default)
- Tech stack signals (e.g., "uses HubSpot", "has a product team")
- Stage / maturity (seed vs Series B vs public — affects buying process)
Rule: only include a dimension if your healthiest cohort clusters on it AND your worst cohort doesn't. Otherwise it's noise.
2. Trigger events (when they buy)
What changed that made the problem urgent? Examples:
- New VP of Sales hired (new playbook)
- Just raised a round (now has budget)
- Lost a major customer (panic on retention)
- Compliance deadline (forced timeline)
- Outgrew a tool (Excel breaking, current vendor missing a feature)
If no triggers are identifiable, the product is probably a "vitamin not painkiller" — flag this.
3. Jobs-to-be-Done (why they hire the product)
Use Christensen's JTBD form:
When [situation], I want to [motivation], so I can [expected outcome].
Pull exact phrases from win-call recordings if possible. Customer's words > marketing language.
4. Disqualifiers (who this is NOT for)
The most under-done part of most ICPs. Name segments that look adjacent but aren't:
- Companies below/above a size threshold
- Industries with regulatory friction the product can't handle
- Buying processes too complex for current sales motion
- Use cases the product handles poorly
A good ICP doc has at least 3 explicit disqualifiers.
5. Buying signals (how to find them)
Externally-visible signals that correlate with fit — these power outbound:
- Job postings (e.g., "hiring SDRs" → ready for an outbound tool)
- Funding announcements
- Tech stack via BuiltWith / Wappalyzer
- LinkedIn activity / podcast appearances
- Public product changes
6. Buyer / champion / user (who matters in the deal)
- Economic buyer: who signs the check
- Champion: who advocates internally
- End user: who actually uses it
These are often three different people. Messaging must address each.
Process
- Pull the data. Get the customer list with metrics. Read CSV/sheet if supplied.
- Rank accounts by composite health. Default formula:
(MRR × tenure_months × NPS_score) / (support_tickets + 1). Tune with user.
- Compare top quartile vs bottom quartile across each firmographic dimension. Note where they diverge — those are your ICP signals.
- Run JTBD interviews if the user has time — 5 wins, 5 losses, 5 churns. If not, extract from existing call notes / support tickets.
- Draft the ICP in the output format below.
- Stress-test: pick 3 of the user's "best gut accounts" and 3 "worst gut accounts" — does the ICP correctly predict them? If not, dimensions are wrong.
- Score new prospects: provide a simple 0-10 scoring rubric for inbound/outbound lists.
Output format
ICP: [Product] — [Audience version if multi]
Date: [YYYY-MM-DD]
Confidence: [Strong / Moderate / Provisional]
1. ONE-LINER
[Company type] in [stage/situation] who [JTBD] when [trigger event].
2. FIRMOGRAPHICS
- Industry: [...]
- Size: [...]
- Stage: [...]
- Tech stack signal: [...]
3. TRIGGER EVENTS
- [Event] → why it creates urgency
4. JOBS-TO-BE-DONE
When [situation], I want to [motivation], so I can [outcome].
5. BUYING SIGNALS (for outbound)
- [Signal + where to find it]
6. DEAL ROLES
- Economic buyer: [title]
- Champion: [title]
- End user: [title]
7. DISQUALIFIERS — DO NOT TARGET
- [Segment + why]
8. ICP SCORECARD (apply to any prospect)
[10-question rubric, each 0-1, score >=7 = ICP]
9. EVIDENCE
[Citations: which customers / data points support each claim]
Quality checks
- Predictive test: pick 5 accounts not used in the analysis. Does the scorecard rank them correctly by actual revenue/retention?
- Disqualifier test: at least 3 explicit disqualifiers? If not, the ICP is too broad.
- JTBD test: are the JTBDs in the customer's words, or marketing-speak?
- Falsifiability test: could this ICP be wrong? If it reads as universal truths ("companies that want to grow"), it has no information content.
Common failure modes
- Aspirational ICP — the customer the team wishes they had, not the one they actually serve well. Anchor on data.
- Demographic personas — "Marketing Mary, 32" tells you nothing about why she buys. Replace with situation + JTBD.
- No disqualifiers — a list of who's a fit without who isn't is a wishlist, not an ICP.
- Ignoring multi-audience reality — internal vs external buyers, free vs paid, SMB vs enterprise often need separate ICPs. Don't average them.
- Static doc — ICPs decay. Mark a review date (default: 6 months out).
Handoffs
- Once ICP is locked, hand to
positioning-canvas if positioning isn't yet defined (positioning step 4 needs ICP)
- Hand to outbound/copy work for messaging that uses the JTBD language
- Hand to sales ops for the scorecard to filter inbound leads
