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

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

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

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

[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