ideal-customer-profile

name: ideal-customer-profiledescription: >  Define a sharp Ideal Customer Profile (ICP) — firmographics, behavioral  signals, JTBD, and buyer persona. Use when defining a new ICP, refining  one from actual closed customers, or auditing why pipeline or conversion  is poor.license: MIT + Commons Clausemetadata:   version: 1.0.0  author: borghei  category: project-management  domain: go-to-market  updated: 2026-05-27  python-tools: icp_scorer.py  tech-stack: icp, ideal-customer-profile, firmographics, jtbd  tags:     - icp    - buyer-persona    - firmographics    - jobs-to-be-done    - segmentation

Ideal Customer Profile (ICP)

The sharp definition of who you serve — used by marketing for targeting, sales for qualification, and product for prioritization.

When to use this skill

• Defining ICP for a new product / segment
• Refining ICP from observed closed customers
• Auditing when pipeline quality / conversion is poor
• Pre-fundraise to articulate market position
• Sales qualification rubric build
• Marketing targeting for outbound + paid

The 8 ICP dimensions

1. Firmographics — industry, vertical, size, geography, age
2. Tech-stack signals — what they use today (and what's missing)
3. Buyer persona — role, level, tenure, team size
4. Jobs-to-be-done (JTBD) — what they're trying to accomplish
5. Existing alternatives — how they solve the JTBD today
6. Trigger events — why now (new funding, new exec, regulation, etc.)
7. Budget authority — who controls spend
8. Reachability — can we find + contact them via channels we have

ICP vs Persona vs Segment

• ICP = the company/account (firmographics + tech + situation)
• Persona = the human buyer (role + motivations + objections)
• Segment = a cluster of similar ICPs (often by size or vertical)

Use ICP for "which companies." Use persona for "which humans within ICP."

Clarify First

Before defining the ICP, settle the inputs below. When one is missing or vague and would change the result, ask about it; when it wouldn't, use a sensible default and say which one you used.

• Existing customers vs greenfield: refine from closed-won/best-customer patterns vs design-partner proxy (the real ICP comes from data, not a guess).
• The JTBD the product solves: what customers are trying to accomplish (anchors firmographics, alternatives, and trigger events).
• Reachable channels you have: paid / outbound / events / content / partners (an ICP you can't reach is academic; drives the Reachability dimension).

Ask about the two or three inputs that matter most for this request, not the whole list. If the user wants a result straight away, produce it and state your assumptions at the top.

Workflow

Step 1 — Start from your best customers

If you have customers:

• List top 20 by health, revenue, expansion
• Find common patterns across them
• That's your real ICP (not what you guessed)

If you don't have customers:

• Use design partners / pilot customers as proxy
• Be ready to revise after 10-20 real customers

Step 2 — Disqualify common myth-ICPs

Common "ICP" patterns that are wishful thinking:

• "Companies with 100-10,000 employees" — too broad
• "Innovative companies" — meaningless
• "Companies that want to grow" — everyone
• "Enterprise" — undefined; map to size

A real ICP excludes most companies.

Step 3 — Document the 8 dimensions

Per dimension, be specific.

Example for HR analytics SaaS:

Step 4 — Build the qualification rubric

For sales qualification, distill ICP to a scorable checklist:

Qualification (BANT-style):
- Industry fit: SaaS/services? (yes/no)
- Size fit: 200-2000 EE? (yes/no)
- Tech fit: Workday/BambooHR + ADP/Gusto? (yes/no)
- Authority: VP People+ in deal? (yes/no)
- Pain: actively trying to solve HR analytics? (yes/no)
- Budget: confirmed > $50K? (yes/no)
- Timeline: decision in next 6 months? (yes/no)

5+ yes = strong lead. <3 yes = disqualify.

Step 5 — Run icp_scorer.py

Audit ICP definition for specificity, score account lists against ICP.

python3 project-management/gtm/ideal-customer-profile/scripts/icp_scorer.py \
  --input icp_spec.json --format markdown

Step 6 — Refresh per quarter

ICP shifts:

• New customers reveal new patterns
• Product evolution opens new segments
• Market shifts (recession, regulation, competition)

Don't refresh weekly. Quarterly is healthy.

Decision frameworks

Firmographic specificity test

For each dimension, ask:

• Could I send a list of companies matching this to my BDR tomorrow?
• If not, sharpen.

Vague: "growing SaaS companies" Sharp: "US-headquartered B2B SaaS companies, $10M-$100M ARR, post-Series B, that have hired their first VP of Sales in last 12 months"

Tech-stack signals

Tech stack reveals readiness:

• Have a CRM = likely structured sales process
• Have an HRIS = HR team beyond founder/HR-of-1
• Use a specific vendor = open to category
• Built in-house = NIH (Not Invented Here) bias risk

Tools like BuiltWith, G2, Crunchbase + scraping can reveal stack.

Trigger event types

Strongest triggers (cause buying activity):

• New executive in role (60-day window)
• Funding round closed (have budget; want to spend strategically)
• M&A activity (integration needs)
• Regulatory deadline (compliance pressure)
• Major incident (urgent need)
• Vendor consolidation push
• Annual planning cycle (Q4)
• Reorg / department restructure

Weaker triggers:

• "They might want it"
• "They have the budget"

Reachability test

For each ICP, can you:

• Find them via paid acquisition? (size of audience; CPC)
• Find them via outbound? (LinkedIn search, ZoomInfo, Apollo)
• Find them at events? (SHRM, RSA, AWS reInvent, etc.)
• Find them via content? (SEO keywords they search)
• Find them via partners? (which adjacent vendors)

If unreachable, ICP is academic.

Common engagements

"Define ICP for our new product"

1. Pull design partner / pilot data (or interview 5-10 ideal targets).
2. Document the 8 dimensions.
3. Build qualification rubric.
4. Validate with sales: can they recognize these in inbound?
5. Validate with marketing: can they target this list?

"Refine ICP from closed customers"

1. List top 20 paying customers by health/expansion.
2. Find patterns: size, vertical, role, trigger, tech stack.
3. Document refined ICP.
4. Compare to original ICP: what's different?
5. Update qualification rubric + targeting.
6. Sunset segments not in refined ICP.

"Pipeline conversion is bad — is it ICP?"

1. Audit closed-won vs closed-lost ICP fit scores.
2. Common failure: pipeline is full of "near-ICP" that don't convert.
3. Tighten qualification; reject more aggressively at intake.

Anti-patterns to avoid

• ICP = "everyone." Diluted GTM.
• ICP without trigger event. Targets without urgency.
• ICP unreachable via your channels. Academic exercise.
• ICP that doesn't disqualify anyone. Not really an ICP.
• Same ICP for 3 years post-launch. Customers reveal real ICP; refresh.
• ICP defined by product, not customer. "Companies that need X" = solution-thinking.
• ICP without qualification rubric. Sales can't apply it.

Scope & Limitations

In Scope:

• An ICP across eight dimensions: firmographics, tech-stack signals, buyer persona, JTBD, existing alternatives, trigger events, budget authority and reachability
• Refining the ICP from top customers by health, revenue and expansion, or from design partners when there are no customers yet
• A yes/no qualification rubric for sales
• Two tool modes: --mode validate audits an ICP definition for completeness and disqualification criteria, and --mode score scores accounts with weighted criteria (icp_scorer.py)

Out of Scope:

• Finding, enriching or scraping account data; the tool scores only accounts the user lists
• The wider go-to-market plan built on the ICP (see project-management/gtm/gtm-strategy/)
• Detailed buyer personas and user research (see product-team/ux-researcher-designer/)
• CRM lead scoring and pipeline operations (see business-growth/revenue-operations/)
• Competitive analysis of who else serves the ICP (see business-growth/competitive-teardown/)

Important Caveats:

• Every per-account fit score and every defined or specific flag is entered by hand. The tool checks and weights them but does not verify them.
• Scoring weights and the "5+ yes is a strong lead" cut-off are starting defaults to tune against closed-won and closed-lost data.
• A greenfield ICP is a hypothesis until 10-20 real customers confirm it.
• ICPs drift with the product and the market. The skill expects a quarterly refresh.

References

• references/icp-dimensions-deep.md — 8 dimensions in depth + signals
• references/icp-refinement-from-data.md — using closed customers to refine

Related skills

• project-management/gtm/gtm-strategy — uses ICP as input
• project-management/strategy-frameworks/business-model-canvas — segments block
• project-management/discovery/customer-interview-script — interview-based ICP discovery
• business-growth/competitive-teardown — competitive context
• c-level-advisor/cro-advisor — sales context
• c-level-advisor/cmo-advisor — marketing context