Revenue Forecasting Engine

Generates detailed revenue forecasts using pipeline weighting, cohort analysis, scenario modeling, seasonality, and leading indicators to inform business dec...

1kalin@1kalin

Install

openclaw skills install @1kalin/afrexai-revenue-forecasting

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:

text

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:

text

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

Bundles: Playbook $27 | Pick 3 for $97 | All 10 for $197 | Everything Bundle $247