cognyai 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
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
dealstageproperty 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 ([X]% of active pipeline)
[GROWING/SHRINKING/STABLE] Pipeline value: $[X] ([X]% change)
Coverage ratio: [X]x ([pipeline value] / [target])
Predictability: X/25
[HIGH/MEDIUM/LOW] Win rate consistency: [X]% +/- [Y]%
[CLEAR/MIXED/UNCLEAR] Segment patterns identified
[STRONG/WEAK] Leading indicators reliability
7. Output momentum report
CRM Sales Momentum Analysis
Period: Last 90 days
Deals analyzed: [N] ([N] won, [N] lost, [N] active)
Pipeline value: $[X]
═══════════════════════════════════════════════════
MOMENTUM SCORE: X/100
═══════════════════════════════════════════════════
Pipeline Flow:
[Stage-by-stage flow diagram from Step 3]
Key Velocity Metrics:
Average Sales Cycle (Won): [N] days
Average Sales Cycle (Lost): [N] days
Fastest Close: [N] days ([deal name])
Slowest Win: [N] days ([deal name])
Stage Health:
┌──────────────────┬──────────┬───────────┬──────────┬─────────────┐
│ Stage │ Deals In │ Avg Days │ Conv Rate│ Stuck Deals │
├──────────────────┼──────────┼───────────┼──────────┼─────────────┤
│ [Stage 1] │ [N] │ [N] │ [X]% │ [N] │
│ [Stage 2] │ [N] │ [N] │ [X]% │ [N] │
│ [Stage 3] │ [N] │ [N] │ [X]% │ [N] │
│ [Stage 4] │ [N] │ [N] │ [X]% │ [N] │
└──────────────────┴──────────┴───────────┴──────────┴─────────────┘
Win Predictors (what separates wins from losses):
1. [Predictor]: deals with [X] win at [Y]% vs [Z]% without
2. [Predictor]: deals with [X] win at [Y]% vs [Z]% without
3. [Predictor]: deals with [X] win at [Y]% vs [Z]% without
Stuck Deals Requiring Attention:
┌──────────────────┬──────────┬──────────┬──────────┬─────────────────┐
│ Deal │ Amount │ Stage │ Days In │ Owner │
├──────────────────┼──────────┼──────────┼──────────┼─────────────────┤
│ [Deal 1] │ $[X] │ [Stage] │ [N] │ [Rep] │
│ [Deal 2] │ $[X] │ [Stage] │ [N] │ [Rep] │
└──────────────────┴──────────┴──────────┴──────────┴─────────────────┘
Rep Performance:
┌──────────────────┬──────────┬───────────┬──────────┬─────────────┐
│ Rep │ Win Rate │ Avg Cycle │ Avg Deal │ Pipeline $ │
├──────────────────┼──────────┼───────────┼──────────┼─────────────┤
│ [Rep 1] │ [X]% │ [N] days │ $[X] │ $[X] │
│ [Rep 2] │ [X]% │ [N] days │ $[X] │ $[X] │
└──────────────────┴──────────┴───────────┴──────────┴─────────────┘
Top 3 Actions:
1. [Highest-impact momentum fix]
2. [Second highest]
3. [Third highest]
8. Record findings
For EVERY actionable momentum insight, call mcp__cogny__create_finding:
{
"title": "38% of deals stall at Proposal stage — avg 18 days vs 7-day benchmark",
"body": "32 of 85 deals entering the Proposal stage in the last 90 days have stalled or been lost. Median time-in-stage for deals that advance is 7 days, but stalled deals average 18 days. Common pattern: deals without a second contact (single-threaded) stall at 2.1x the rate. 12 deals currently stuck in Proposal worth $340K total. Recommend: implement multi-threading requirement before Proposal stage, add 10-day stall alert.",
"action_type": "pipeline_optimization",
"expected_outcome": "Reduce Proposal stage stall rate from 38% to <20%",
"estimated_impact_usd": 8000,
"priority": "high"
}
Action types for sales momentum:
pipeline_optimization— stage process changes, stall interventionsdeal_acceleration— specific actions to unstick individual dealsrep_coaching— rep-specific performance improvementsforecasting_improvement— pipeline coverage, predictability fixeslead_source_optimization— invest in sources that produce faster-closing dealsdisqualification_rule— patterns that predict losses, filter early