revenue-forecast

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.

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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.

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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.

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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.