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

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.