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:

Current State Analysis

• Current MRR/ARR

• Customer count by segment

• ARPU by segment

• Growth trends (MoM, YoY)

• Cohort retention data

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

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

Cohort-Based Modeling

• Track each cohort separately

• Apply cohort-specific retention curves

• Model degradation over time

• Account for seasonality

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

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:

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

GMV Driver Model

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

OR

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

Take Rate Analysis

• Current take rate

• Take rate by category

• Take rate optimization potential

• Competitive benchmarking

• Additional revenue streams (ads, premium, fulfillment)

Liquidity Modeling

• Match rate projections

• Supply/demand balance

• Geographic coverage

• Category depth

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:

Usage Metrics Identification

• Primary usage unit (API calls, storage, compute hours)

• Average usage per customer

• Usage distribution (heavy vs. light users)

• Seasonal patterns

Pricing Structure

• Per-unit pricing tiers

• Volume discounts

• Minimum commitments

• Overage pricing

• Platform fees

Customer Segmentation

• Segment by usage level

• Different growth rates by segment

• Segment-specific retention

• Enterprise vs. SMB patterns

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

Predictability Enhancement

• Committed vs. overage revenue

• Minimum revenue guarantees

• Prepaid usage credits

• Annual contract values

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:

Product Portfolio Mapping

• Product 1: Type, pricing, target market

• Product 2: Type, pricing, target market

• Product 3: Type, pricing, target market

• Cross-sell relationships

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

Cross-Sell Modeling

• Attach rate assumptions

• Timing of cross-sell

• Bundle discount impact

• Cannibalization effects

Revenue Mix Analysis

• Current revenue mix

• Target revenue mix

• Mix shift assumptions

• Profitability by product

Consolidation

• Sum of product revenues

• Eliminate double-counting

• Bundle revenue allocation

• Total company revenue

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:

Current Pricing Analysis

• Current price points

• Discount frequency and depth

• ARPU analysis

• Price sensitivity observed

Competitive Benchmarking

• Competitor pricing

• Feature comparison

• Value-based positioning

• Market standard pricing

Value-Based Pricing Analysis

• Customer value delivered

• ROI for customer

• Willingness to pay research

• Price anchoring opportunities

Price Elasticity Modeling

• Historical price change impact

• Segment-specific elasticity

• Volume vs. price trade-off

• Revenue optimization point

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

Pricing Structure Options

• Per-seat vs. per-company

• Usage-based vs. flat

• Tiered pricing design

• Freemium conversion

• Annual discount strategy

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

Pricing Assumptions

Revenue Projections

Monthly MRR Waterfall

Annual Summary

Scenario Comparison

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