revenue-modeler（收入建模者）

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

1. 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

1. 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

1. 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

1. 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

1. GMV Driver Model

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

   OR

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

1. Take Rate Analysis

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

1. Liquidity Modeling

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

1. 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

1. Pricing Structure

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

1. Customer Segmentation

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

1. 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

1. Predictability Enhancement

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

1. 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

1. 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

1. Cross-Sell Modeling

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

1. Revenue Mix Analysis

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

1. Consolidation

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

1. 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

1. Competitive Benchmarking

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

1. Value-Based Pricing Analysis

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

1. Price Elasticity Modeling

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

1. 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

1. Pricing Structure Options

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

1. Implementation Plan

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

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 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