jmsktm/Revenue Modeler

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
   

4. Cohort-Based Modeling

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

5. 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

6. 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
   • Portfolio-level scenarios
   • New product launch impact
   • Sunset product impact

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

6. Pricing Structure Options

   • Per-seat vs. per-company
   • Usage-based vs. flat
   • Tiered pricing design
   • Freemium conversion
   • Annual discount strategy

7. Implementation Plan

   • Grandfathering strategy
   • Rollout timeline
   • Customer communication
   • Monitoring metrics

Deliverable: Pricing analysis with optimization recommendations

Quick Reference

| Action | Command/Trigger | |--------|-----------------| | SaaS model | "Build MRR/ARR revenue model" | | Marketplace | "Model marketplace GMV and revenue" | | Usage-based | "Create consumption-based revenue model" | | Multi-product | "Model revenue across products" | | Pricing | "Analyze pricing optimization" | | Scenarios | "Model revenue scenarios" |

SaaS Metrics Reference

Core Metrics

| Metric | Formula | Healthy Benchmark | |--------|---------|-------------------| | MRR | Sum of monthly recurring revenue | Growing | | ARR | MRR × 12 | Growing | | ARPU | MRR / Customers | Stable or growing | | Net Revenue Retention | (Start MRR + Expansion - Contraction - Churn) / Start MRR | > 100% | | Gross Revenue Retention | (Start MRR - Contraction - Churn) / Start MRR | > 85% | | LTV | ARPU × Gross Margin / Churn Rate | > 3× CAC | | CAC Payback | CAC / (ARPU × Gross Margin) | < 12 months |

MRR Movement Types

| Type | Definition | |------|------------| | New MRR | Revenue from new customers this month | | Expansion MRR | Revenue increase from existing customers (upsells) | | Contraction MRR | Revenue decrease from existing customers (downgrades) | | Churned MRR | Revenue from customers who cancelled | | Reactivation MRR | Revenue from customers who returned |

SaaS Benchmarks

| Metric | Good | Great | Best-in-Class | |--------|------|-------|---------------| | MRR Growth (MoM) | 5-7% | 10-15% | 20%+ | | Net Revenue Retention | 100-110% | 110-130% | 130%+ | | Gross Churn (monthly) | 3-5% | 1-3% | < 1% | | LTV/CAC | 3:1 | 5:1 | 10:1 | | CAC Payback | 12-18 mo | 6-12 mo | < 6 mo |

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 with budget-planner: Link revenue to expense budget
• Use with cash-flow-forecaster: Convert revenue to cash
• Use with unit-economics-calculator: Validate profitability
• Use with financial-analyst: Historical performance analysis
• Use with investment-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