How to Use These Prompts
Each prompt includes:
When to use it: The situation or analysis need
Required inputs: Information you must provide
AI prompt: Copy, customise, and paste into your preferred AI tool
Global AI Safety Line
Paste the following instruction at the start of every revenue analysis prompt:
“If unsure about any claim, calculation, or financial assumption, write ‘VERIFY:’ next to it. Do not invent financial data, projections, or benchmarks. Flag any assumptions made. This analysis is for internal decision-making and is not financial, legal, or investment advice.”
Terminology note: Use “revenue” for GAAP or total bookings, and “MRR/ARR” for recurring subscription run rate. These prompts distinguish between the two where it matters.
Before you start:
Gather accurate, up-to-date financial data before running any analysis
Verify all AI-generated calculations independently
Consult qualified financial professionals for significant business decisions
Treat AI outputs as starting points for analysis, not final conclusions
Performance Analysis Prompts
Monthly and Quarterly Reviews
When to use: Understanding short-term performance against your goals. These highlight top products and underperforming channels.
AI Prompt:
Required inputs: [current revenue], [previous period data], [targets], [segment breakdowns]
Year-Over-Year Comparison
When to use: Identifying seasonal patterns and long-term growth trajectories. This helps you see if your business is accelerating or slowing down.
AI Prompt:
Required inputs: [monthly revenue for current year], [monthly revenue for prior year]
Revenue Concentration Analysis
When to use: Assessing customer revenue risk and diversification. High concentration in one or two customers is a risk factor that should be monitored.
AI Prompt:
Required inputs: [revenue by customer], [customer count], [historical concentration data]
Forecasting and Planning
Bottom-Up Revenue Forecast
When to use: Building projections based on real sales activities. This includes leads, conversion rates, and average deal sizes.
AI Prompt:
Required inputs: [leads], [conversion rates], [deal size], [sales cycle length]
Scenario-Based Forecast
When to use: Planning for multiple possible outcomes when uncertainty is high.
AI Prompt:
Required inputs: [baseline assumptions], [variables to test], [historical data], [forecast period]
Break-Even Analysis
When to use: Determining the exact point where your revenue covers all costs. Essential for pricing and resource planning.
AI Prompt:
Required inputs: [fixed costs], [variable costs], [price per unit or average revenue]
Customer and Subscription Metrics
Customer Lifetime Value (CLV)
When to use: CLV measures the total revenue a customer generates during their entire relationship with you. It helps you decide how much you can spend to acquire new customers (CAC).
AI Prompt:
Required inputs: [average revenue per user], [lifespan], [retention rate], [gross margin]
MRR/ARR Analysis
When to use: For subscription models, tracking Monthly Recurring Revenue (MRR) is critical. You must monitor new, expansion, and churned MRR. Note: MRR/ARR measures recurring subscription run rate, not total GAAP revenue.
AI Prompt:
Required inputs: [MRR/ARR current], [MRR components], [customer counts]
Churn and Retention Analysis
When to use: Understanding customer and revenue retention patterns.
AI Prompt:
Required inputs: [customer counts start/end], [churned customers], [revenue churn data]
Quick Reference: Revenue Metrics
Core Revenue Metrics
SaaS and Subscription Metrics
Note: These benchmarks are common rules of thumb, but they vary significantly by industry, ACV, company stage, and market. Always verify against relevant peer data and current benchmark reports.
Sales Metrics
Reporting and Communication
Board Revenue Report
When to use: Preparing clear updates for stakeholders. Focus on high-level trends and variances from budget.
AI Prompt:
Required inputs: [revenue data], [forecast vs. actual], [key metrics], [narrative context]
Revenue Narrative
When to use: Explaining the “why” behind the numbers. Use this to build trust with investors or your internal team.
AI Prompt:
Required inputs: [revenue results], [context and drivers], [audience], [key messages]
Best Practices for Revenue Analysis
Data Quality
Verify data accuracy before analysis, AI cannot check if your initial data is correct
Use verified financial statements as your source
Understand how metrics are calculated in your systems
Reconcile to financial statements where possible
Risk Identification
High revenue concentration in one or two customers is a risk factor
Track customer dependency and diversification
Monitor leading indicators, not just lagging results
Forecasting Discipline
Document all assumptions explicitly
Use ranges rather than single-point forecasts
Update forecasts regularly, they should be rolling
Update as soon as monthly actuals are available
Track forecast accuracy to improve over time
Using AI for Analysis
Provide accurate, complete data
Verify all calculations independently
Treat AI outputs as drafts requiring review
Add context AI cannot know
Do not rely on AI for financial decisions without expert validation
Limitations and Cautions
What AI Can Help With
Structuring analysis frameworks
Calculating standard metrics
Identifying patterns in data you provide
Generating report templates
Suggesting questions to investigate
What AI Cannot Do
Verify your data is accurate
Know your business context
Provide reliable industry benchmarks without sources
Make financial recommendations
Replace financial expertise
When to Consult Professionals
Major business decisions
Investor communications
Financial reporting and compliance
Tax and legal implications
Complex valuation questions
| Metric | Formula | Use Case |
|---|---|---|
| Revenue Growth Rate | (Current – Prior) / Prior × 100 | Tracks trajectory |
| MRR (Monthly Recurring Revenue) | Sum of all monthly subscriptions | Subscription run rate |
| ARR (Annual Recurring Revenue) | MRR × 12 | Annual run rate |
| ARPU (Average Revenue Per User) | Total Revenue / Active Users | Customer value |
| Gross Margin | (Revenue – COGS) / Revenue × 100 | Measures profitability |
| Metric | Formula | Benchmark Guidance (VERIFY) |
|---|---|---|
| Net Revenue Retention | (Starting MRR + Expansion – Contraction – Churn) / Starting MRR | >100% often cited for growth. VERIFY for your segment. |
| Gross Revenue Retention | (Starting MRR – Churn – Contraction) / Starting MRR | Often cited as healthy at 85%+. Many B2B datasets show medians near 90%+. VERIFY by ACV and market. |
| Customer Churn Rate | Churned Customers / Starting Customers | Median monthly churn varies strongly by ARPA and segment, often ranging from about 2% to 6% in SaaS datasets. VERIFY for your peer set. |
| MRR Churn Rate | Churned MRR / Starting MRR | Varies significantly by segment. VERIFY against peers. |
| Quick Ratio | (New MRR + Expansion MRR) / (Churned MRR + Contraction MRR) | >4 commonly cited as healthy. VERIFY for your stage. |
| LTV:CAC Ratio | Customer Lifetime Value / Customer Acquisition Cost | >3:1 widely cited rule of thumb. VERIFY for your model. |
| CAC Payback | CAC / (ARPU × Gross Margin) | Best-in-class can be <12 months. Many frameworks label 12-18 months as good. VERIFY for your segment. |
| Metric | Formula | Purpose |
|---|---|---|
| Pipeline Coverage | Pipeline Value / Revenue Target | Forecast sufficiency |
| Win Rate | Won Deals / Total Closed Deals | Sales effectiveness |
| Average Deal Size | Total Revenue / Number of Deals | Deal quality |
| Sales Cycle Length | Average days from opportunity to close | Process efficiency |
| Revenue per Employee | Total Revenue / Number of Employees | Productivity (varies heavily by model) |
Disclaimer
These prompts are provided for educational purposes and should be adapted to your specific business context. Always verify AI-generated analyses, calculations, and recommendations independently. Revenue analysis involves complex financial considerations that may require professional expertise. Consult qualified financial advisers, accountants, or business consultants for significant business decisions. AI outputs should be treated as analytical tools and starting points, not as definitive financial advice. AI Crash Course is not responsible for business decisions made using these prompts.
