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

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]

═══════════════════════════════════════════════════
IDEAL CUSTOMER PROFILE
═══════════════════════════════════════════════════

Company Firmographics:
  Industry:        [Top 1-3 industries] ([X]% of wins)
  Employee Count:  [Range] (sweet spot: [X-Y])
  Annual Revenue:  $[Range]
  Geography:       [Top regions]

Buyer Persona:
  Primary Buyer:   [Title cluster] ([X]% of deals)
  Secondary Buyer: [Title cluster] ([X]% of deals)
  Seniority:       [Level] and above
  Function:        [Department]

Deal Characteristics:
  Average Deal Size:    $[X] (median: $[Y])
  Sweet Spot Range:     $[X] - $[Y]
  Average Sales Cycle:  [N] days
  Best Lead Sources:    [Source 1], [Source 2]

Win Rate Analysis:
  Overall Win Rate:     [X]%
  Best Segment Rate:    [X]% ([segment description])
  Worst Segment Rate:   [X]% ([segment description])

═══════════════════════════════════════════════════
ICP FIT SCORING MODEL
═══════════════════════════════════════════════════

Dimension          | Weight | Criteria
───────────────────|────────|─────────────────────────
Industry           |  25%   | [Top industries] = match
Company Size       |  20%   | [Range] employees = match
Seniority          |  20%   | [Level]+ = match
Job Function       |  15%   | [Functions] = match
Deal Size Potential|  10%   | $[X]+ = match
Lead Source        |  10%   | [Sources] = match

Scoring: 80-100 = Strong fit | 60-79 = Moderate fit | <60 = Weak fit

═══════════════════════════════════════════════════
ANTI-ICP (AVOID)
═══════════════════════════════════════════════════

- [Pattern 1 that predicts losses]
- [Pattern 2 that predicts losses]
- [Pattern 3 that predicts losses]

Top 3 Actions:
1. [Highest-impact recommendation]
2. [Second recommendation]
3. [Third recommendation]

7. Record findings

For EVERY actionable ICP insight, call mcp__cogny__create_finding:

{
  "title": "ICP sweet spot: SaaS companies 200-1000 employees close at 2.3x average rate",
  "body": "Analysis of 87 closed-won deals shows SaaS companies with 200-1000 employees have a 42% win rate vs 18% overall. Average deal size $45K, 34-day sales cycle. These companies represent only 23% of pipeline but 51% of revenue. Recommend: prioritize this segment in outbound and ad targeting.",
  "action_type": "icp_refinement",
  "expected_outcome": "Increase win rate by focusing pipeline on high-fit segments",
  "estimated_impact_usd": 5000,
  "priority": "high"
}

Action types for ICP analysis:

• icp_refinement — ICP definition updates, scoring model changes
• targeting_refinement — ad/outbound targeting changes based on ICP
• pipeline_optimization — sales process changes for specific segments
• lead_source_optimization — invest more in high-ICP-fit lead sources
• disqualification_rule — anti-ICP patterns to filter out early