| name | revenue-forecast |
| description | Projects revenue with multiple scenarios using historical data and market factors. Use when forecasting future revenue for planning or reporting. |
| allowed-tools | Read Write Glob |
| metadata | {"author":"matthewhitcham","version":"1.0"} |
Revenue Forecast
When to Use This Skill
Use this skill when you need to:
- Project revenue for the next 3, 6, or 12 months
- Create conservative, base, and optimistic revenue scenarios
- Forecast revenue by product line, channel, or customer segment
- Build a data-backed revenue plan for budgeting or investor communications
DO NOT use this skill for complete financial models (use financial-model), expense forecasting, or pricing decisions. This is focused specifically on revenue projection.
Core Principle
A REVENUE FORECAST IS A RANGE, NOT A NUMBER — PRESENT THREE SCENARIOS AND IDENTIFY WHICH ASSUMPTIONS SEPARATE THEM.
Phase 1: Historical Data
Required Inputs
| Input | What to Ask | Default |
|---|
| Monthly revenue (last 6-12 months) | "Share your monthly revenue for the last 6-12 months." | No default — must be provided |
| Revenue streams | "How do you generate revenue? (products, services, subscriptions, one-time)" | No default — must be provided |
| Seasonality | "Are there seasonal patterns in your revenue?" | No known seasonality |
| Growth drivers | "What is driving growth? (marketing, referrals, new products, expansion)" | Organic growth |
| Planned changes | "Any upcoming changes? (new product launch, price increase, new channel)" | None planned |
| Forecast period | "How far out? (3, 6, or 12 months)" | 12 months |
GATE: Do not proceed without at least 3 months of historical revenue data.
Phase 2: Trend Analysis
Historical Performance
## Revenue Analysis
### Monthly Revenue History
| Month | Revenue | MoM Change | YoY Change |
|-------|---------|-----------|-----------|
| [Month] | $[X] | +/-[X]% | +/-[X]% |
| ... | | | |
### Key Metrics
| Metric | Value |
|--------|-------|
| Average monthly revenue (last 6 months) | $[X] |
| Average monthly growth rate | [X]% |
| Revenue trend | Growing / Flat / Declining |
| Highest month | $[X] ([Month]) |
| Lowest month | $[X] ([Month]) |
| Revenue volatility (std deviation) | $[X] |
Revenue by Stream
### Revenue Breakdown by Stream
| Stream | Monthly Avg | % of Total | Growth Rate | Trend |
|--------|-----------|-----------|-------------|-------|
| [Stream 1] | $[X] | [X]% | [X]% | ↑↓→ |
| [Stream 2] | $[X] | [X]% | [X]% | ↑↓→ |
Phase 3: Forecast Model
Three Scenarios
## Revenue Forecast: [Period]
### Assumptions
| Factor | Conservative | Base | Optimistic |
|--------|-------------|------|------------|
| Monthly growth rate | [X]% | [X]% | [X]% |
| New stream revenue | $0 | $[X] | $[X] |
| Seasonal adjustment | Yes | Yes | Yes |
| Price change impact | None | None | +[X]% |
| Churn/loss factor | [X]% | [X]% | [X]% |
### Monthly Forecast
| Month | Conservative | Base | Optimistic |
|-------|-------------|------|------------|
| M1 | $[X] | $[X] | $[X] |
| M2 | $[X] | $[X] | $[X] |
| ... | | | |
| M12 | $[X] | $[X] | $[X] |
| **Total** | **$[X]** | **$[X]** | **$[X]** |
### Forecast by Revenue Stream (Base Case)
| Month | [Stream 1] | [Stream 2] | [Stream 3] | Total |
|-------|-----------|-----------|-----------|-------|
| M1 | $[X] | $[X] | $[X] | $[X] |
| ... | | | | |
Key Drivers and Risks
### What Separates the Scenarios
**Conservative → Base:** [Key assumption difference, e.g., "Assumes new
marketing channel launches on time and produces 20 leads/month by M3"]
**Base → Optimistic:** [Key assumption difference, e.g., "Assumes enterprise
deal closes in Q2 adding $5K/month recurring"]
### Downside Risks
1. [Risk] — Impact: -$[X]/month — Likelihood: [High/Med/Low]
2. [Risk] — Impact: -$[X]/month — Likelihood: [High/Med/Low]
### Upside Opportunities
1. [Opportunity] — Impact: +$[X]/month — Likelihood: [High/Med/Low]
Phase 4: Deliverable
## Forecast Summary
**Forecast period:** [X] months
**Conservative annual revenue:** $[X]
**Base case annual revenue:** $[X]
**Optimistic annual revenue:** $[X]
**Planning recommendation:** Budget against the conservative scenario.
Target the base case. Celebrate if you hit optimistic.
forecast/
└── revenue-forecast-[YYYY].md
Example: Consulting Business ($15K/month Average)
History: 6 months of data, $12K-$18K range, average $15K, 4% monthly growth driven by referrals.
Forecast (12 months): Conservative $14.5K avg (flat), Base $17.8K avg (4% growth), Optimistic $21K avg (7% growth from adding a new service). Annual totals: Conservative $174K, Base $214K, Optimistic $252K.
Key driver: Base case assumes maintaining current referral rate. Optimistic assumes launching a group coaching program in M4 adding $3K/month.
Anti-Patterns
- Straight-line projections — revenue rarely grows in a straight line. Account for seasonality, ramp-up periods, and plateaus.
- Single-number forecasts — always provide a range. A single number is either a lie or a guess.
- Ignoring churn and cancellations — if you have recurring revenue, model churn. Gross new revenue minus churn equals net revenue growth.
- Forecasting without identifying drivers — "revenue will grow 10%" is not a forecast. "10 new clients at $1,500/month from LinkedIn ads" is a forecast.
- Over-optimism bias — most founders forecast 2-3x what actually happens. Use historical growth rates as the base, not aspirational targets.
Recovery
- Less than 3 months of data: Use industry benchmarks and clearly label the forecast as preliminary. Update monthly as data accumulates.
- Highly variable revenue: Focus on trailing averages rather than month-to-month growth rates. Widen the gap between conservative and optimistic scenarios.
- New revenue stream with no data: Model it separately with a conservative ramp. Do not include it in the base case until you have 2-3 months of actual data.
- Revenue declining: Acknowledge the trend. Model scenarios for stabilization and recovery. Identify which actions change the trajectory.
