/crm-icp-analysis

name: crm-icp-analysis
description: Analyze HubSpot CRM data to build a data-driven Ideal Customer Profile from closed-won deals, contacts, and companies
version: "1.0.0"
author: Cogny AI
platforms: [hubspot]
user-invocable: true
argument-hint: "[full|companies|contacts|deals]"
allowed-tools:
  - mcp__cogny__hubspot__*
  - mcp__cogny__create_finding
  - Bash
  - Read
  - Write

CRM ICP Analysis

Build a data-driven Ideal Customer Profile by analyzing closed-won deals, associated contacts, and companies in your HubSpot CRM. Identifies patterns in industries, company sizes, job titles, deal sizes, sales cycles, and lead sources that predict revenue.

**Requires:** Cogny Agent subscription ($9/mo) — [Sign up](https://cogny.com/agent)

Usage

`/crm-icp-analysis` — full ICP analysis across all dimensions `/crm-icp-analysis companies` — company firmographic analysis only `/crm-icp-analysis contacts` — buyer persona analysis only `/crm-icp-analysis deals` — deal pattern analysis only

Prerequisites Check

Call `mcp__cogny__hubspot__get_user_details` to verify CRM access. Confirm read access to contacts, companies, and deals. If access is missing:

This skill requires HubSpot CRM access via Cogny's MCP server.
Sign up at https://cogny.com/agent and connect your HubSpot account.

Steps

1. Discover available properties

Before querying data, understand what fields exist:

hubspot__get_properties(objectType: "deals")
hubspot__get_properties(objectType: "companies")
hubspot__get_properties(objectType: "contacts")

Identify key properties for analysis:

• **Deals:** dealstage, amount, closedate, createdate, pipeline, dealtype, hs_analytics_source
• **Companies:** industry, numberofemployees, annualrevenue, city, state, country, type
• **Contacts:** jobtitle, hs_persona, lifecyclestage, hs_analytics_source

Note any custom properties that look ICP-relevant (e.g., custom industry fields, company tier, segment tags).

2. Analyze closed-won deals

Search for closed-won deals to establish the revenue baseline:

hubspot__search_crm_objects(
  objectType: "deals",
  filterGroups: [{"filters": [{"propertyName": "dealstage", "operator": "EQ", "value": "closedwon"}]}],
  properties: ["dealname", "amount", "closedate", "createdate", "pipeline", "dealtype", "hs_analytics_source"],
  sorts: [{"propertyName": "closedate", "direction": "DESCENDING"}],
  limit: 200
)

Check the `total` count — paginate if needed to capture full dataset.

Calculate:

• **Total closed-won deals** and **total revenue**
• **Average deal size** (mean and median)
• **Deal size distribution**: bucket into tiers (e.g., <$5K, $5-25K, $25-100K, $100K+)
• **Average sales cycle length**: days from createdate to closedate
• **Sales cycle by deal size tier**
• **Win rate by pipeline** (if multiple pipelines exist)
• **Lead source breakdown**: which sources produce closed-won deals

Also search closed-lost for comparison:

hubspot__search_crm_objects(
  objectType: "deals",
  filterGroups: [{"filters": [{"propertyName": "dealstage", "operator": "EQ", "value": "closedlost"}]}],
  properties: ["dealname", "amount", "closedate", "createdate", "pipeline", "hs_analytics_source"],
  limit: 200
)

Compare closed-won vs closed-lost to identify discriminating patterns.

3. Analyze winning companies

Fetch companies associated with closed-won deals. Use `get_crm_objects` with deal IDs to get associations, then batch-fetch the associated companies:

hubspot__get_crm_objects(
  objectType: "companies",
  objectIds: [<associated company IDs>],
  properties: ["name", "industry", "numberofemployees", "annualrevenue", "city", "state", "country", "type", "domain"]
)

Build firmographic profile:

• **Industry breakdown**: rank industries by deal count and total revenue
• **Company size distribution**: by employee count bands (1-50, 51-200, 201-1000, 1000+)
• **Revenue range**: annual revenue bands of winning companies
• **Geography**: country, state/region concentration
• **Company type**: customer, partner, prospect categorization

Flag:

• Industries that appear in >20% of closed-won deals (core ICP)
• Company size sweet spots (highest win rate bands)
• Geographic clusters

4. Analyze buyer personas

Fetch contacts associated with closed-won deals, then batch-fetch:

hubspot__get_crm_objects(
  objectType: "contacts",
  objectIds: [<associated contact IDs>],
  properties: ["jobtitle", "hs_persona", "lifecyclestage", "hs_analytics_source", "email", "firstname", "lastname"]
)

Build buyer persona profile:

• **Job title clustering**: group similar titles (e.g., "VP Marketing", "Head of Marketing", "Marketing Director" = Marketing Leadership)
• **Seniority distribution**: C-level, VP, Director, Manager, Individual Contributor
• **Functional area**: Marketing, Sales, Product, Engineering, Finance, Operations
• **Number of contacts per deal**: single-threaded vs multi-threaded deals
• **Lead source by persona**: how different personas find you

Flag:

• Dominant buyer persona (>30% of closed-won contacts)
• Multi-threaded deals that win at higher rates
• Personas that correlate with larger deal sizes

5. Cross-dimensional analysis

Combine insights across deals, companies, and contacts:

• **Best segment**: Industry + Company Size + Persona that produces highest win rate
• **Highest-value segment**: combination that produces largest average deal size
• **Fastest-closing segment**: combination with shortest sales cycle
• **Lead source efficiency**: which sources produce best-fit leads (not just most leads)
• **Anti-ICP patterns**: segments with low win rates or high loss rates

6. Output ICP definition

CRM ICP Analysis
Data basis: [N] closed-won deals, [N] companies, [N] contacts
Period: [earliest close date] to [latest close date]
Total revenue analyzed: $[X]

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IDEAL CUSTOMER PROFILE
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