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:
- Pipeline-Weighted Forecast — Apply stage-specific close rates to your current pipeline
- Cohort Analysis — Track revenue by customer cohort with expansion/contraction/churn
- Scenario Modeling — Bear/base/bull projections with probability weighting
- Seasonality Adjustments — Monthly coefficients based on your historical patterns
- 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:
| Stage | Probability | Weighted Value |
|---|
| Discovery | 10% | Deal × 0.10 |
| Demo/Eval | 25% | Deal × 0.25 |
| Proposal Sent | 50% | Deal × 0.50 |
| Negotiation | 75% | Deal × 0.75 |
| Verbal Commit | 90% | Deal × 0.90 |
| Closed Won | 100% | Deal × 1.00 |
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:
| Metric | Seed | Series A | Series B+ |
|---|
| Gross Churn | 3-5%/mo | 2-3%/mo | 1-2%/mo |
| Net Retention | 90-100% | 100-110% | 110-130% |
| Expansion % | 5-10% | 10-20% | 20-40% |
| CAC Payback | 18-24 mo | 12-18 mo | 6-12 mo |
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:
| Month | B2B SaaS | Ecommerce | Professional Services |
|---|
| Jan | 0.85 | 0.70 | 0.90 |
| Feb | 0.90 | 0.75 | 0.95 |
| Mar | 1.05 | 0.85 | 1.10 |
| Apr | 1.00 | 0.90 | 1.00 |
| May | 0.95 | 0.90 | 0.95 |
| Jun | 1.10 | 0.95 | 1.05 |
| Jul | 0.85 | 0.85 | 0.85 |
| Aug | 0.80 | 0.90 | 0.80 |
| Sep | 1.10 | 1.00 | 1.10 |
| Oct | 1.05 | 1.05 | 1.05 |
| Nov | 1.15 | 1.40 | 1.10 |
| Dec | 1.20 | 1.75 | 1.15 |
Step 6: Leading Indicators Dashboard
Track these weekly — they predict revenue 60-90 days out:
| Indicator | Weight | Signal |
|---|
| Qualified pipeline created | 25% | New opps entering Stage 2+ |
| Demo-to-proposal rate | 20% | Conversion velocity |
| Average deal size trend | 15% | Moving up or down? |
| Sales cycle length | 15% | Getting longer = red flag |
| Inbound lead volume | 10% | Marketing effectiveness |
| Website trial signups | 10% | Self-serve demand |
| Customer NPS/CSAT | 5% | Retention predictor |
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.
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