revenue-modeler

name	Revenue Modeler
slug	revenue-modeler
description	Build revenue projection models with driver-based forecasting, scenario analysis, and pricing optimization
category	finance
complexity	complex
version	1.0.0
author	ID8Labs
triggers	["revenue model","revenue projection","sales forecast","pricing model","revenue growth","MRR forecast"]
tags	["revenue-modeling","forecasting","pricing","saas-metrics","growth-planning"]

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