/crm-sales-momentum

name: crm-sales-momentum
description: Analyze HubSpot pipeline momentum — deal velocity, stage conversions, win/loss patterns, and stall detection
version: "1.0.0"
author: Cogny AI
platforms: [hubspot]
user-invocable: true
argument-hint: "[full|velocity|stalls|segments]"
allowed-tools:
  - mcp__cogny__hubspot__*
  - mcp__cogny__create_finding
  - Bash
  - Read
  - Write

CRM Sales Momentum Drivers

Analyze what is driving or stalling pipeline momentum in your HubSpot CRM. Measures deal velocity, stage conversion rates, time-in-stage, and win/loss patterns by segment. Surfaces the deal characteristics that predict wins versus losses and identifies stuck deals.

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

**Tip:** Run `/crm-icp-analysis` first to establish your ICP baseline, then use this skill to see how ICP-fit deals move through pipeline compared to non-fit deals.

Usage

`/crm-sales-momentum` — full momentum analysis `/crm-sales-momentum velocity` — deal velocity and stage timing only `/crm-sales-momentum stalls` — stuck deal detection only `/crm-sales-momentum segments` — win/loss patterns by segment 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 pipeline structure

Get deal stage properties to understand the pipeline:

hubspot__get_properties(objectType: "deals", propertyNames: ["dealstage", "pipeline"])

Identify:

• All pipeline stages and their order (from the `dealstage` property enum values)
• Pipeline names (if multiple pipelines exist)
• Custom deal properties relevant to segmentation

Use `search_properties` to find stage-timing properties:

hubspot__search_properties(objectType: "deals", keywords: ["hs_date_entered", "hs_time_in"])

Also fetch owner list for rep-level analysis:

hubspot__search_owners()

2. Pull deal data across all stages

Fetch deals created in the last 90 days plus recently closed:

hubspot__search_crm_objects(
  objectType: "deals",
  filterGroups: [{"filters": [{"propertyName": "createdate", "operator": "GTE", "value": "<90 days ago timestamp>"}]}],
  properties: ["dealname", "dealstage", "amount", "createdate", "closedate", "pipeline", "hubspot_owner_id", "hs_analytics_source", "dealtype", <stage timing properties from step 1>],
  sorts: [{"propertyName": "createdate", "direction": "DESCENDING"}],
  limit: 200
)

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

3. Deal velocity analysis

Calculate velocity metrics across the pipeline:

• **Overall velocity**: average days from deal creation to close (won and lost separately)
• **Stage-by-stage velocity**: average time in each stage
• **Stage conversion rates**: % of deals that advance from each stage to the next
• **Drop-off stages**: stages with the highest loss rate
• **Velocity trend**: compare last 30 days vs previous 30 days

Build a pipeline flow visualization:

Pipeline Flow (last 90 days):
[Stage 1] ──85%──> [Stage 2] ──62%──> [Stage 3] ──48%──> [Stage 4] ──71%──> [Closed Won]
  100 deals           85 deals          53 deals          25 deals         18 deals
  avg 4 days          avg 7 days        avg 12 days       avg 5 days
                       ↓ 15%             ↓ 38%             ↓ 52%           ↓ 29%
                    [Lost: 15]        [Lost: 32]        [Lost: 28]       [Lost: 7]

Flag:

• Stages where >40% of deals stall or are lost
• Stages with average time >2x the overall stage average
• Conversion rate drops of >10% compared to previous period

4. Stuck deal detection

Identify deals that are stalled based on time-in-stage analysis.

For each pipeline stage, calculate:

• Median time-in-stage for deals that eventually advanced
• Standard deviation of time-in-stage
• **Stall threshold**: median + 1.5x standard deviation

Flag deals currently in a stage beyond the stall threshold:

hubspot__search_crm_objects(
  objectType: "deals",
  filterGroups: [{"filters": [
    {"propertyName": "dealstage", "operator": "EQ", "value": "<stage>"},
    {"propertyName": "hs_date_entered_<stage>", "operator": "LT", "value": "<stall threshold date>"}
  ]}],
  properties: ["dealname", "amount", "hubspot_owner_id", "hs_date_entered_<stage>"],
  limit: 50
)

For each stuck deal, fetch associated company and contacts to add context about why it might be stalled.

5. Win/loss pattern analysis by segment

Segment closed deals along multiple dimensions and compare win rates:

**By Deal Size:**

• Bucket deals into tiers and compare win rate per tier
• Identify the deal size range with highest win rate

**By Lead Source:**

• Win rate by `hs_analytics_source` (organic, paid, referral, direct, etc.)
• Average deal size by source
• Average sales cycle by source

**By Owner (Sales Rep):**

• Win rate per rep
• Average deal velocity per rep
• Average deal size per rep
• Identify top performers and what they do differently

**By Company Segment** (fetch associated companies):

• Win rate by industry
• Win rate by company size
• Win rate by geography

**By Deal Age at Stage:**

• Deals that spend <X days in each stage: win rate
• Deals that spend >X days: win rate
• Identify the "golden window" — the time-in-stage range that correlates with wins

6. Momentum scoring

Score overall pipeline momentum:

Pipeline Momentum Score: X/100

Velocity:        X/25
  [FAST/NORMAL/SLOW] Average cycle [N] days
  [UP/DOWN/FLAT] Velocity trend: [X]% change vs prior period

Flow Rate:       X/25
  [STRONG/MODERATE/WEAK] Stage conversion rates
  [IMPROVING/DECLINING/STABLE] Conversion trend
  Drop-off stage: [stage name] ([X]% loss rate)

Pipeline Health:  X/25
  [HEALTHY/AT_RISK/CRITICAL] [N] deals stuck (