crm-icp-analysis · v1.0.0 · 2026-04-14 · sha256 3ee9c4cf45680b50
crm-icp-analysis v1.0.0A
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---
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]
═══════════════════════════════════════════════════
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`:
```json
{
"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