/pipeline-forecasting

name: pipeline-forecasting
description: Generate predictive pipeline forecasts with confidence intervals and scenario modeling for revenue planning
license: MIT
metadata:
  author: ClawFu
  version: 1.0.0
  mcp-server: "@clawfu/mcp-skills"

Pipeline Forecasting

> Build accurate, data-driven revenue forecasts using historical conversion rates, deal velocity, and confidence-weighted projections.

When to Use This Skill

• Weekly/monthly pipeline reviews with leadership
• Board meeting revenue projections
• Quota setting and territory planning
• Identifying gaps between forecast and target
• Scenario planning for best/worst/likely outcomes

Methodology Foundation

Based on **Clari's Revenue Operations methodology** and **Forrester's B2B Revenue Waterfall**, combining:

• Weighted pipeline (probability × value)
• Historical stage conversion rates
• Deal velocity analysis
• Commit vs. upside categorization

What Claude Does vs What You Decide

| Claude Does | You Decide | |-------------|------------| | Calculates weighted pipeline by stage | Which deals to include/exclude | | Applies historical conversion rates | Override factors for specific deals | | Generates confidence intervals | Final commit number to leadership | | Identifies forecast risks | Actions to close gaps | | Models best/worst/likely scenarios | Which scenario to plan against |

What This Skill Does

1. **Ingests pipeline data** - Current opportunities with stage, value, close date 2. **Applies conversion math** - Historical win rates by stage, segment, rep 3. **Calculates weighted forecast** - Probability-adjusted revenue projection 4. **Generates scenarios** - Best case, commit, worst case with confidence bands 5. **Identifies risks** - Deals pushing, pipeline gaps, coverage ratios

How to Use

I need a pipeline forecast for Q1. Here's our current pipeline:

[Paste pipeline data: Deal name, Stage, Value, Close Date, Rep]

Historical context:
- Average win rate: 25%
- Stage 3→Close rate: 45%
- Stage 4→Close rate: 70%
- Average sales cycle: 45 days

Target: $2.5M for Q1

Instructions

Step 1: Pipeline Categorization

Segment deals into:

• **Commit** - High confidence (Stage 4+, verbal commit)
• **Best Case** - Medium confidence (Stage 3, engaged)
• **Upside** - Low confidence (Stage 1-2, early)

Step 2: Weighted Calculation

Weighted Value = Deal Value × Stage Probability × Rep Factor

Stage Probabilities (adjust to your data):
- Stage 1 (Discovery): 10%
- Stage 2 (Qualification): 20%
- Stage 3 (Proposal): 40%
- Stage 4 (Negotiation): 70%
- Stage 5 (Verbal): 90%

Step 3: Coverage Analysis

Pipeline Coverage = Total Pipeline ÷ Target
- Healthy: 3-4x coverage
- At Risk: 2-3x coverage
- Critical: <2x coverage

Step 4: Scenario Modeling

• **Worst Case**: Commit only (90%+ probability deals)
• **Likely Case**: Commit + 50% of Best Case
• **Best Case**: Commit + Best Case + 25% of Upside

Step 5: Risk Identification

Flag deals with:

• Close date in past (needs update)
• No activity in 14+ days (stalled)
• Pushing from previous quarter (slip risk)
• Missing key fields (data quality)

Examples

Example 1: Quarterly Forecast

**Input:**

Pipeline for Q1:
- Acme Corp: Stage 4, $150K, Jan 30, Rep: Sarah
- Beta Inc: Stage 3, $80K, Feb 15, Rep: Mike
- Gamma Ltd: Stage 2, $200K, Mar 1, Rep: Sarah
- Delta Co: Stage 4, $120K, Feb 28, Rep: Lisa
- Echo Systems: Stage 1, $500K, Mar 15, Rep: Mike

Target: $400K

**Output:**

## Q1 Pipeline Forecast

### Weighted Pipeline
| Deal | Stage | Value | Probability | Weighted |
|------|-------|-------|-------------|----------|
| Acme Corp | 4 | $150K | 70% | $105K |
| Delta Co | 4 | $120K | 70% | $84K |
| Beta Inc | 3 | $80K | 40% | $32K |
| Gamma Ltd | 2 | $200K | 20% | $40K |
| Echo Systems | 1 | $500K | 10% | $50K |
| **Total** | | **$1.05M** | | **$311K** |

### Scenario Analysis
- **Worst Case (Commit)**: $189K (Acme + Delta at 70%)
- **Likely Case**: $245K (Commit + 50% Best Case)
- **Best Case**: $361K (All weighted)

### Coverage Ratio
- Pipeline: $1.05M
- Target: $400K
- Coverage: 2.6x ⚠️ (At Risk - need 3x+)

### Risks Identified
1. ⚠️ Echo Systems ($500K) is Stage 1 with Mar close - unlikely
2. ⚠️ Gap of $155K between Likely ($245K) and Target ($400K)

### Recommendations
1. Pressure test Acme and Delta for verbal commits
2. Accelerate Beta Inc to Stage 4 this week
3. Add $200K+ new pipeline to reach 3x coverage

Example 2: Monthly Roll-Up

**Input:**

January pipeline by rep:
Sarah: $450K weighted, $280K commit
Mike: $320K weighted, $150K commit
Lisa: $280K weighted, $200K commit

Team target: $500K

**Output:**

## January Team Forecast

| Rep | Weighted | Commit | % to Target |
|-----|----------|--------|-------------|
| Sarah | $450K | $280K | 56% |
| Mike | $320K | $150K | 30% |
| Lisa | $280K | $200K | 40% |
| **Team** | **$1.05M** | **$630K** | **126%** |

### Forecast Summary
- **Team Commit**: $630K (126% of $500K target) ✅
- **Confidence**: HIGH - commit exceeds target

### Risk Watch
- Mike at 30% commit coverage - needs deal acceleration
- Sarah carrying most weight - concentration risk

Skill Boundaries

What This Skill Does Well

• Mathematical pipeline calculations
• Scenario modeling with clear assumptions
• Identifying data quality issues
• Coverage ratio analysis

What This Skill Cannot Do

• Predict which specific deals will close (human judgment)
• Account for market changes or competitive moves
• Replace rep-level deal knowledge
• Guarantee forecast accuracy

When to Escalate to Human

• Deals with unusual circumstances (M&A, champion left)
• Market disruptions affecting close rates
• Strategic accounts requiring executive judgment
• Final commit numbers for board/investors

Iteration Guide

Follow-up Prompts

• "What if we lose the top 2 deals? Show me that scenario."
• "Apply a 20% haircut to all Stage 2 deals and recalculate."
• "Which deals have