Revenue Forecasting Engine

Revenue Forecasting Engine

Build accurate, data-driven revenue forecasts your board and investors actually trust.

What This Does

Generates a complete revenue forecasting model covering:

1. Pipeline-Weighted Forecast — Apply stage-specific close rates to your current pipeline
2. Cohort Analysis — Track revenue by customer cohort with expansion/contraction/churn
3. Scenario Modeling — Bear/base/bull projections with probability weighting
4. Seasonality Adjustments — Monthly coefficients based on your historical patterns
5. Leading Indicators — Track signals that predict revenue 60-90 days out

Instructions

When the user asks for a revenue forecast, follow this framework:

Step 1: Gather Inputs

Ask for (or use available data):

• Current MRR/ARR
• Pipeline by stage with deal values
• Historical close rates by stage
• Average sales cycle length
• Net revenue retention rate
• Expansion revenue %

Step 2: Build the Pipeline Forecast

Stage-Weighted Model:

Adjustment factors:

• Deal age penalty: -5% per month past avg cycle
• Champion risk: -20% if no identified champion
• Budget confirmed: +10% if budget is allocated
• Competitive deal: -15% if competitor identified

Step 3: Cohort Revenue Model

Track each monthly cohort:

Month 0: New MRR from cohort
Month 1: Retained MRR × (1 - monthly churn rate)
Month 3: Add expansion revenue (avg 2-5% monthly for healthy SaaS)
Month 6: Steady-state retention rate applies
Month 12: Mature cohort — use net revenue retention

Benchmarks by company stage:

Step 4: Scenario Analysis

Bear Case (20% probability):

• Pipeline closes at 60% of weighted value
• Churn increases 50%
• No expansion revenue
• 1 key deal slips each quarter

Base Case (60% probability):

• Pipeline closes at weighted value
• Current retention rates hold
• Historical expansion rate
• Normal seasonality

Bull Case (20% probability):

• Pipeline closes at 120% of weighted value
• Retention improves 10%
• Expansion accelerates 25%
• 1 surprise large deal per quarter

Expected Value = (Bear × 0.2) + (Base × 0.6) + (Bull × 0.2)

Step 5: Seasonality Coefficients

Apply monthly adjustment factors:

Step 6: Leading Indicators Dashboard

Track these weekly — they predict revenue 60-90 days out:

Step 7: Output Format

Present the forecast as:

REVENUE FORECAST — [Period]
================================
Current ARR: $X
Pipeline (Weighted): $X
Expected New ARR: $X

12-Month Projection:
  Bear:  $X (20%)
  Base:  $X (60%)
  Bull:  $X (20%)
  Expected: $X

Key Risks:
  1. [Risk] — [Mitigation]
  2. [Risk] — [Mitigation]

Leading Indicators:
  🟢 [Healthy metric]
  🟡 [Watch metric]
  🔴 [Concerning metric]

Next Month Actions:
  1. [Specific action]
  2. [Specific action]

Red Flags to Call Out

• Pipeline coverage < 3x target = high risk

• 40% of forecast from 1-2 deals = concentration risk

• Average deal age exceeding 1.5x normal cycle = stalling
• Declining demo-to-close rate = product-market fit erosion
• Rising CAC payback period = unit economics degrading

Revenue Recognition Notes

• SaaS: Recognize ratably over contract term
• Services: Recognize on delivery/milestones
• Usage-based: Recognize on consumption
• Annual prepay: Deferred revenue, recognize monthly

Built by AfrexAI — AI context packs for business operators who ship.

Get the full toolkit:

• AI Revenue Leak Calculator — Find where you're losing money
• Context Packs — Industry-specific AI agent configs ($47/pack)
• Agent Setup Wizard — Deploy your first AI agent in 15 minutes

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