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

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

How to Use

code

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

code

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

code

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:

code

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:

code

## 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:

code

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

Team target: $500K

Output:

code

## 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 the highest impact on our forecast?"
•"Show me the gap between forecast and target by month."

Refinement Cycle

•Generate initial forecast → Review with reps
•Update deal probabilities based on rep input
•Re-run forecast with adjusted assumptions
•Lock commit number, track weekly variance

Checklists & Templates

Weekly Forecast Review Checklist

• All deals have current close dates
• Stage progression updated this week
• Commit deals have next steps scheduled
• Risks flagged and mitigation assigned
• Coverage ratio calculated

Forecast Template

markdown

## [Period] Revenue Forecast

**Generated:** [Date]
**Pipeline Cutoff:** [Date]

### Summary
- Target: $X
- Commit: $X (X% of target)
- Best Case: $X
- Coverage: Xx

### By Segment
[Table]

### Risks & Mitigations
[List]

### Actions This Week
[List]

References

•Clari Revenue Operations Playbook
•Forrester B2B Revenue Waterfall Model
•MEDDICC Deal Qualification Framework
•Gartner Sales Forecasting Best Practices

Related Skills

•deal-risk-scoring - Assess individual deal health
•lead-scoring - Qualify top-of-funnel
•account-health - Customer retention signals

Skill Metadata

•Domain: RevOps
•Complexity: Intermediate
•Mode: centaur
•Time to Value: 15-30 minutes per forecast
•Prerequisites: Pipeline data export, historical win rates