---

name: yepai-sales-pipeline description: >- Pipeline health diagnostics, velocity analysis, deal scoring, coverage ratios, forecast modeling, and data-driven intervention recommendations. user-invocable: false disable-model-invocation: false metadata: {"openclaw":{"emoji":"📊"}} tags: [sales] license: MIT-0

Pipeline Analytics Criteria

When analyzing pipeline health, building forecasts, or scoring deals, apply these frameworks.

Pipeline Velocity

Velocity = (Qualified Opps × Avg Deal Size × Win Rate) / Sales Cycle Length

Each variable is a diagnostic lever:

• Qualified Opportunities: Track by source, segment, rep. Declining top-of-funnel shows in revenue 2-3 quarters later.
• Average Deal Size: Segment ruthlessly — blended averages hide problems.
• Win Rate: Track by stage, rep, segment, deal size. Stage-level rates reveal where deals die.
• Sales Cycle Length: Lengthening cycles = competitive pressure, committee expansion, or qualification gaps.

Coverage Ratios

Context	Target Coverage
Mature, predictable	3x
Growth-stage / new market	4-5x
New rep ramping	5x+

Quality-adjusted coverage discounts pipeline by deal health, stage age, and engagement signals. $5M of stale, unqualified pipeline < $2M of active, well-qualified opportunities.

Deal Health Scoring

Three signal categories:

• Qualification Depth: MEDDPICC completeness. Deals with <5/8 fields populated are underqualified. Underqualified at late stage = primary source of forecast misses.
• Engagement Intensity: Meeting recency (>14 days at late stage = red flag), stakeholder breadth, buyer-initiated activity.
• Progression Velocity: Stage movement vs. benchmark. Stalled >1.5x median stage duration needs intervention or removal.

Forecasting

Layer multiple signals — don't rely on stage-weighted probability alone:

• Historical Conversion: Base rate by stage/segment (almost always lower than CRM probability)
• Velocity Weighting: Faster deals = higher close probability
• Engagement Adjustment: Multi-threaded active deals close at 2-3x rate of single-threaded
• Seasonal/Cyclical Patterns: Quarter-end compression, budget cycles

Output: Commit (>90%), Best Case (>60%), Upside (<60%) with confidence intervals.

Analytical Rules

• Never present a single forecast number without a confidence range
• Always segment before drawing conclusions
• Distinguish leading indicators (activity, pipeline creation) from lagging (revenue, win rate)
• Flag data quality issues explicitly. Pipeline not updated in 30+ days = flagged regardless of stage.

Deliverable: Pipeline Health Dashboard

# Pipeline Health: [Period]

## Velocity Metrics
| Metric | Current | Prior Period | Trend | Benchmark |
|--------|---------|-------------|-------|-----------|
| Pipeline Velocity | $/day | | | |
| Qualified Opps | | | | |
| Avg Deal Size | $ | | | |
| Win Rate | % | | | |
| Cycle Length | days | | | |

## Coverage Analysis
| Segment | Quota Remaining | Weighted Pipeline | Coverage | Quality-Adjusted |
|---------|----------------|-------------------|----------|------------------|
| | $ | $ | x | x |

## Deals Requiring Intervention
| Deal | Stage | Days Stalled | MEDDPICC Score | Risk Signal | Action |
|------|-------|-------------|----------------|-------------|--------|
| | | | /8 | | |

Deliverable: Forecast Model

# Forecast: [Period]

| Category | Amount | Confidence | Key Assumptions |
|----------|--------|------------|-----------------|
| Commit | $ | >90% | |
| Best Case | $ | >60% | |
| Upside | $ | <60% | |

## Risk Factors
- [Risk with quantified impact: "$X at risk if [condition]"]

## Upside Opportunities
- [Opportunity with probability and potential amount]