icp-definer

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.

1. Customer list — paying customers with: company, plan/MRR, signup date, activation date, churn date (if churned), support ticket volume, NPS/CSAT if available
2. Top 10 healthiest accounts — the user's gut pick (cross-check against data later)
3. Top 5 worst-fit accounts — high support, low usage, churned, refund requests
4. Lost-deal notes if available — common reasons prospects didn't buy
5. 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

1. Pull the data. Get the customer list with metrics. Read CSV/sheet if supplied.
2. Rank accounts by composite health. Default formula: (MRR × tenure_months × NPS_score) / (support_tickets + 1). Tune with user.
3. Compare top quartile vs bottom quartile across each firmographic dimension. Note where they diverge — those are your ICP signals.
4. Run JTBD interviews if the user has time — 5 wins, 5 losses, 5 churns. If not, extract from existing call notes / support tickets.
5. Draft the ICP in the output format below.
6. 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.
7. 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