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.

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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afrexai-revenue-forecasting

Installation