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Revenue Modeler

Expert revenue forecasting agent that builds driver-based revenue models, projects growth scenarios, optimizes pricing strategies, and forecasts subscription metrics. Specializes in SaaS revenue modeling, marketplace economics, and multi-stream revenue forecasting.

This skill applies rigorous revenue modeling methodologies to create defensible projections, stress-test assumptions, and support strategic planning. Perfect for fundraising projections, board reporting, budgeting, and pricing decisions.

Core Workflows

Workflow 1: SaaS Revenue Model

Objective: Build comprehensive SaaS/subscription revenue model

Steps:

1. Current State Analysis

   • Current MRR/ARR
   • Customer count by segment
   • ARPU by segment
   • Growth trends (MoM, YoY)
   • Cohort retention data

2. Revenue Driver Identification

   • Customer Acquisition:

     • New customer growth rate
     • Lead generation capacity
     • Conversion rates by channel
     • Sales capacity and productivity
     • CAC and payback period

   • Customer Retention:

     • Gross churn rate (customer count)
     • Net revenue retention (NRR)
     • Churn by segment/cohort
     • Contraction rate

   • Expansion:

     • Upsell rate
     • Cross-sell rate
     • Seat expansion
     • Tier upgrades

3. Model Architecture

   Beginning MRR + New MRR (new customers × ARPU) + Expansion MRR (existing customer upgrades) - Contraction MRR (downgrades) - Churned MRR (lost customers) = Ending MRR

ARR = MRR × 12

1. Cohort-Based Modeling

   • Track each cohort separately
   • Apply cohort-specific retention curves
   • Model degradation over time
   • Account for seasonality

2. Scenario Development

   • Base Case:

     • Current trend continuation
     • Realistic growth assumptions

   • Upside Case:

     • Improved conversion
     • Lower churn
     • Higher expansion

   • Downside Case:

     • Slower acquisition
     • Higher churn
     • Economic headwinds

3. Key Metrics Output

   • MRR/ARR projections by month
   • Customer count projections
   • Net Revenue Retention
   • LTV/CAC ratio evolution
   • Payback period
   • Gross margin projections

Deliverable: Monthly MRR model with 12-36 month projections

Workflow 2: Marketplace Revenue Model

Objective: Build revenue model for marketplace businesses

Steps:

1. Marketplace Metrics Setup

   • Supply Side:

     • Active sellers/providers
     • Listings per seller
     • Average order value
     • Supply growth rate

   • Demand Side:

     • Active buyers
     • Transactions per buyer
     • Buyer frequency
     • Demand growth rate

   • Marketplace Metrics:

     • Gross Merchandise Value (GMV)
     • Take rate percentage
     • Net revenue = GMV × Take rate

2. GMV Driver Model

   GMV = Active Buyers × Transactions/Buyer × Average Order Value

OR

GMV = Active Sellers × Listings/Seller × Sell-Through Rate × Price

3. Take Rate Analysis

 * Current take rate
 * Take rate by category
 * Take rate optimization potential
 * Competitive benchmarking
 * Additional revenue streams (ads, premium, fulfillment)

4. Liquidity Modeling

 * Match rate projections
 * Supply/demand balance
 * Geographic coverage
 * Category depth

5. Revenue Streams

 * Transaction fees (primary)
 * Subscription fees (seller SaaS)
 * Advertising revenue
 * Fulfillment/logistics fees
 * Premium placement fees
 * Data/analytics fees

Deliverable: Marketplace revenue model with GMV and take rate projections

Workflow 3: Usage-Based Revenue Model

Objective: Model revenue for consumption-based pricing

Steps:

1. Usage Metrics Identification

   • Primary usage unit (API calls, storage, compute hours)
   • Average usage per customer
   • Usage distribution (heavy vs. light users)
   • Seasonal patterns

2. Pricing Structure

   • Per-unit pricing tiers
   • Volume discounts
   • Minimum commitments
   • Overage pricing
   • Platform fees

3. Customer Segmentation

   • Segment by usage level
   • Different growth rates by segment
   • Segment-specific retention
   • Enterprise vs. SMB patterns

4. Model Components

   Revenue = Σ (Customers per segment × Usage per customer × Price per unit)

Account for:

- Customer growth
- Usage growth per customer
- Price changes
- Volume discount impact

5. Predictability Enhancement

 * Committed vs. overage revenue
 * Minimum revenue guarantees
 * Prepaid usage credits
 * Annual contract values

6. Scenario Modeling

 * Usage growth scenarios
 * Customer mix changes
 * Pricing optimization
 * Enterprise contract impact

Deliverable: Usage-based revenue model with consumption projections

Workflow 4: Multi-Product Revenue Model

Objective: Model revenue across multiple products and revenue streams

Steps:

1. Product Portfolio Mapping

   • Product 1: Type, pricing, target market
   • Product 2: Type, pricing, target market
   • Product 3: Type, pricing, target market
   • Cross-sell relationships

2. Individual Product Models

   • Build sub-model for each product
   • Apply appropriate methodology:
     • Subscription → SaaS model
     • Transaction → Marketplace model
     • Usage → Consumption model
     • One-time → Pipeline model

3. Cross-Sell Modeling

   • Attach rate assumptions
   • Timing of cross-sell
   • Bundle discount impact
   • Cannibalization effects

4. Revenue Mix Analysis

   • Current revenue mix
   • Target revenue mix
   • Mix shift assumptions
   • Profitability by product

5. Consolidation

   • Sum of product revenues
   • Eliminate double-counting
   • Bundle revenue allocation
   • Total company revenue

6. Scenario Development

   • Product-specific scenarios

Deliverable: Consolidated multi-product revenue model

Workflow 5: Pricing Optimization Model

Objective: Analyze and optimize pricing strategy

Steps:

1. Current Pricing Analysis

   • Current price points
   • Discount frequency and depth
   • ARPU analysis
   • Price sensitivity observed

2. Competitive Benchmarking

   • Competitor pricing
   • Feature comparison
   • Value-based positioning
   • Market standard pricing

3. Value-Based Pricing Analysis

   • Customer value delivered
   • ROI for customer
   • Willingness to pay research
   • Price anchoring opportunities

4. Price Elasticity Modeling

   • Historical price change impact
   • Segment-specific elasticity
   • Volume vs. price trade-off
   • Revenue optimization point

5. Pricing Scenarios

   • Price increase impact:

     • Revenue gain from price
     • Volume loss from churn
     • Net revenue impact

   • Price decrease impact:

     • Revenue loss from price
     • Volume gain from conversion
     • Net revenue impact

Deliverable: Pricing analysis with optimization recommendations

Quick Reference

SaaS Metrics Reference

Core Metrics

MRR Movement Types

SaaS Benchmarks

Revenue Model Template

# Revenue Model: [Company Name]

**Model Period:** [Start] - [End]
**Last Updated:** [Date]

## Model Inputs

### Customer Assumptions
| Metric | Current | Growth Rate |
|--------|---------|-------------|
| Starting Customers | | |
| New Customers/Month | | |
| Churn Rate (Monthly) | | |
| Net Revenue Retention | | |

### Pricing Assumptions
| Segment | ARPU | % of New |
|---------|------|----------|
| Starter | | |
| Professional | | |
| Enterprise | | |
| Weighted Avg | | |

## Revenue Projections

### Monthly MRR Waterfall
| Month | Start MRR | New | Expansion | Contraction | Churn | End MRR |
|-------|-----------|-----|-----------|-------------|-------|---------|
| M1 | | | | | | |
| M2 | | | | | | |
| ... | | | | | | |
| M12 | | | | | | |

### Annual Summary
| Metric | Year 1 | Year 2 | Year 3 |
|--------|--------|--------|--------|
| ARR | | | |
| YoY Growth | | | |
| Customers | | | |
| ARPU | | | |
| NRR | | | |

## Scenario Comparison
| Scenario | Year 1 ARR | Year 2 ARR | Year 3 ARR |
|----------|------------|------------|------------|
| Base | | | |
| Upside | | | |
| Downside | | | |

## Key Assumptions & Risks
1. [Assumption 1] - [Risk if wrong]
2. [Assumption 2] - [Risk if wrong]

Best Practices

Model Building

• Start with driver-based approach
• Document all assumptions
• Make assumptions adjustable
• Build scenario capability
• Test edge cases

Assumption Setting

• Ground in historical data
• Benchmark to industry
• Be realistic, not optimistic
• Explain reasoning
• Sensitivity test key drivers

Presentation

• Executive summary first
• Visualize key trends
• Show assumption sensitivity
• Include scenario comparison
• Highlight risks

Integration with Other Skills

• Use withbudget-planner: Link revenue to expense budget
• Use withcash-flow-forecaster: Convert revenue to cash
• Use withunit-economics-calculator: Validate profitability
• Use withfinancial-analyst: Historical performance analysis
• Use withinvestment-analyzer: Support fundraising projections

Common Pitfalls to Avoid

• Hockey stick projections: Ground in reality
• Ignoring churn: Even small churn compounds
• Overestimating new customers: Harder than it looks
• Ignoring seasonality: Build in monthly patterns
• Linear assumptions: Growth often S-curve
• Ignoring capacity constraints: Sales, product, support
• Static pricing: Build in price evolution
• No segmentation: Different customers behave differently

Weekly Installs

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Repository

jmsktm/claude-settings

GitHub Stars

2

First Seen

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Security Audits

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