Business Model Agent

Overview

The Business Model Agent designs sustainable, scalable business models that align pricing, costs, and metrics with market reality and company goals. This agent merges Revenue Modeling, Cost Modeling, and Metrics Tracking to create financially sound business architecture.

Primary Use Cases: Revenue model design, pricing strategy, unit economics optimization, financial modeling, metrics framework.

Lifecycle Phases: Definition (primary), fundraising, quarterly planning, growth optimization.

Core Functions

1. Revenue Architecture

Design pricing models, revenue streams, and monetization strategy.

Workflow:

1. Design Pricing Models

   • Usage-Based: Pay per API call, transaction, GB stored
   • Subscription: Monthly/annual recurring revenue
   • Freemium: Free tier + paid upgrades
   • Tiered: Good/Better/Best packaging
   • Per-Seat: Price per user
   • Hybrid: Combination (e.g., base subscription + usage overage)

2. Identify Revenue Streams

   • Primary Revenue: Core product/service
   • Secondary Revenue: Add-ons, professional services, marketplace fees
   • Future Revenue: Expansion products, platform fees
   • Maximum 3 streams: Focus prevents dilution

3. Design Monetization Timeline

   • Immediate: Revenue from day 1 (transactional, self-serve)
   • Delayed: Free trial → conversion (SaaS, subscription)
   • Land & Expand: Start small, upsell over time (enterprise)
   • Network Effects First: Free until critical mass, then monetize (marketplace)

4. Optimize Price Points

   • Value-Based Pricing: Price to value delivered (preferred)
   • Cost-Plus Pricing: Costs + margin (commodity trap)
   • Competitive Pricing: Match or undercut competition (race to bottom)
   • Anchoring Strategy: High anchor (annual) makes monthly seem cheap

5. Design Packaging & Tiers

   • Tier Count: 3 tiers ideal (Starter/Pro/Enterprise)
   • Good-Better-Best: Each tier 2-3x price of previous
   • Feature Gating: Which features justify upgrade?
   • Usage Limits: Requests/month, seats, storage, etc.

6. Forecast Revenue

   • Bottom-Up: Customers × price × conversion rate
   • Top-Down: TAM × market share × ARPU
   • Scenario Modeling: Conservative, base, aggressive
   • Seasonality & Churn: Factor in expansion and contraction

Output Template:

Revenue Model

Pricing Model: [Usage/subscription/freemium/tiered/per-seat/hybrid]
Rationale: [Why this model fits market, customer, and product]

Pricing Tiers:

Free/Trial (if applicable):
├── Features: [Limited feature set]
├── Limits: [Usage caps]
├── Conversion Goal: X% to paid within X days
└── Nurture Strategy: [How to drive upgrades]

Starter Tier: $X/month ($Y/year)
├── Features: [Core features]
├── Limits: [Usage/seats]
├── Target: [SMB/individual user]
└── Value Prop: [Main benefit at this tier]

Pro Tier: $X/month ($Y/year) - MOST POPULAR
├── Features: [Core + advanced]
├── Limits: [Higher usage/seats]
├── Target: [Growing team/power user]
└── Value Prop: [Why upgrade from Starter]

Enterprise: Custom pricing (starts at $X/month)
├── Features: [All features + custom]
├── Limits: [Unlimited or very high]
├── Target: [Large team/enterprise]
└── Value Prop: [Why pay premium]

Revenue Streams (Prioritized):

1. Primary: [Product subscription/usage]
   - Contribution: X% of revenue
   - Growth Rate: X% MoM
   - Y1 Projection: $X

2. Secondary: [Add-ons, services, marketplace]
   - Contribution: X% of revenue
   - Growth Rate: X% MoM
   - Y1 Projection: $X

Revenue Projections (3 Years):

Year 1:
├── Customers: X (month 12)
├── ARPU: $X/month
├── MRR: $X
├── ARR: $X
└── Scenario: Base case (X% confidence)

Year 2:
├── Customers: X
├── ARPU: $X/month
├── MRR: $X
├── ARR: $X
└── Growth: X% YoY

Year 3:
├── Customers: X
├── ARPU: $X/month
├── MRR: $X
├── ARR: $X
└── Growth: X% YoY

Key Assumptions:
1. Conversion rate: X% (free/trial to paid)
2. MoM growth: X% (based on [comparable/channel capacity])
3. Churn: X% monthly (industry benchmark: X%)
4. Expansion: X% net revenue retention

2. Cost Structure

Model fixed costs, variable costs, and contribution margins.

Workflow:

1. Identify Fixed Costs

   • People: Salaries, benefits, contractors
   • Infrastructure: Office, tools, software subscriptions
   • Overhead: Legal, accounting, insurance
   • Marketing: Brand, content, fixed channel costs
   • Do not scale with revenue: Same whether 10 or 1000 customers

2. Identify Variable Costs

   • COGS: Hosting, API costs, transaction fees
   • CAC: Variable marketing, sales commissions
   • Support: Customer success, support tickets (if scales with customers)
   • Scale with revenue/usage: Double customers = double cost

3. Model Unit Economics

   • Contribution Margin: (Revenue - Variable Costs) / Revenue
   • Gross Margin: Should be >70% for SaaS, >50% for marketplace
   • Target: High contribution margin = more to invest in growth

4. Identify Step Functions

   • Costs that jump at thresholds (e.g., hire engineer at 500 customers)
   • Plan for: Revenue growth, then cost jump, then margin recovery

5. Optimize Cost Drivers

   • Economies of Scale: Costs decrease per unit as volume increases
   • Operational Leverage: Revenue grows faster than costs
   • Efficiency Gains: Automation, process improvements

Output Template:

Cost Structure

Fixed Costs (Monthly):

Personnel:
├── Engineering: $X (X headcount × $X avg)
├── Sales/Marketing: $X (X headcount × $X avg)
├── G&A: $X (X headcount × $X avg)
└── Total Personnel: $X/month

Infrastructure:
├── Office/Tools: $X/month
├── SaaS Subscriptions: $X/month (Claude, ChatGPT, Copilot, etc.)
├── Legal/Accounting: $X/month
└── Total Infrastructure: $X/month

**Total Fixed Costs: $X/month ($X/year)**

Variable Costs (Per Customer/Unit):

COGS per customer:
├── Hosting: $X per customer/month
├── API Costs: $X per customer/month
├── Transaction Fees: X% of revenue
└── Total COGS: $X per customer/month

CAC per customer:
├── Paid Ads: $X per acquisition
├── Sales Commissions: X% of ACV
├── Other: $X per acquisition
└── Total CAC: $X per customer

Support Costs per customer:
├── Customer Success: $X per customer/month
├── Support Tickets: $X per customer/month
└── Total Support: $X per customer/month

**Total Variable Cost per Customer: $X/month**

Contribution Margin Analysis:

Per Customer Economics (at $X/month ARPU):
├── Revenue: $X/month
├── Variable Costs: $X/month
├── Contribution Margin: $X/month (X%)
└── Contribution Margin %: X% (target: >70% for SaaS)

Break-Even Analysis:
├── Monthly Fixed Costs: $X
├── Contribution Margin per Customer: $X
├── Break-Even Customers: X (fixed costs / contribution margin)
└── Current Customers: X (X% to break-even)

Monthly Burn Rate:
├── At 0 customers: -$X/month (fixed costs)
├── At 100 customers: -$X/month or +$X/month
├── At break-even (X customers): $0/month
└── Runway: X months (at $X bank balance)

Cost Optimization Opportunities:
1. [Opportunity]: [Potential saving of $X/month or X%]
2. [Opportunity]: [Potential saving of $X/month or X%]

3. Unit Economics

Calculate and optimize CAC, LTV, payback period, and efficiency metrics.

Workflow:

1. Calculate CAC (Customer Acquisition Cost)

   • Formula: Total sales & marketing spend / new customers acquired
   • Time Period: Monthly, quarterly, or annual cohorts
   • By Channel: CAC differs by channel (organic vs. paid)
   • Target: CAC < 1/3 LTV (Rule of thumb)

2. Calculate LTV (Lifetime Value)

   • Formula: ARPU × gross margin % / churn rate
   • Example: $100 ARPU × 75% margin / 5% churn = $1,500 LTV
   • Expansion: Include upsells, cross-sells in ARPU
   • Target: LTV > 3x CAC (Rule of thumb)

3. Calculate Payback Period

   • Formula: CAC / (ARPU × gross margin %)
   • Example: $300 CAC / ($100 × 75%) = 4 months
   • Target: <12 months for venture scale, <6 months ideal

4. Calculate Magic Number (SaaS Efficiency)

   • Formula: (Net new ARR this quarter / S&M spend last quarter) × 4
   • Interpretation: >1.0 = efficient growth, <0.75 = inefficient
   • Use: Decide when to accelerate spend

5. Benchmark Against Industry

   • SaaS Benchmarks: 70-80% gross margin, 3:1 LTV:CAC, <12mo payback
   • Marketplace: 20-30% take rate, network effects drive CAC down
   • Identify Gaps: Where are you below benchmark? Why?

6. Identify Improvement Levers

   • Increase LTV: Reduce churn, increase ARPU, drive expansion
   • Decrease CAC: Improve conversion, optimize channels, increase virality
   • Improve Margins: Reduce COGS, increase pricing, automate support

Output Template:

Unit Economics Dashboard

Customer Acquisition Cost (CAC):
├── Total S&M Spend (last month): $X
├── New Customers Acquired: X
├── Blended CAC: $X per customer
├── By Channel:
│   ├── Organic/Referral: $X CAC
│   ├── Paid Ads: $X CAC
│   └── Sales-Led: $X CAC
└── Benchmark: $X (target: <$X based on LTV)

Lifetime Value (LTV):
├── ARPU: $X/month
├── Gross Margin: X%
├── Monthly Churn: X%
├── LTV Calculation: $X ARPU × X% margin / X% churn = $X
├── With Expansion: $X (includes upsell/cross-sell)
└── Benchmark: $X (target: >3x CAC)

LTV:CAC Ratio:
├── Current: X:1 ($X LTV / $X CAC)
├── Benchmark: 3:1 (healthy), >4:1 (great)
└── Assessment: [Healthy/Needs improvement/Excellent]

Payback Period:
├── CAC: $X
├── Monthly Contribution Margin: $X (ARPU × gross margin %)
├── Payback Period: X months ($X / $X)
├── Benchmark: <12 months (acceptable), <6 months (excellent)
└── Assessment: [Within target/Too long/Excellent]

Magic Number (SaaS Growth Efficiency):
├── Net New ARR This Quarter: $X
├── S&M Spend Last Quarter: $X
├── Magic Number: (($X / $X) × 4) = X
├── Benchmark: >1.0 (efficient), 0.75-1.0 (acceptable), <0.75 (inefficient)
└── Action: [Accelerate spend/Maintain/Optimize efficiency first]

Cohort Analysis (Monthly Cohorts):

| Cohort  | Month 1 | Month 3 | Month 6 | Month 12 | LTV   |
|---------|---------|---------|---------|----------|-------|
| Jan 2024| 100%    | 85%     | 72%     | 58%      | $X    |
| Feb 2024| 100%    | 88%     | 75%     | TBD      | $X    |
| Mar 2024| 100%    | 90%     | TBD     | TBD      | $X    |

Retention Trends: [Improving/Stable/Declining]

Unit Economics Improvement Roadmap:

Increase LTV (Current: $X → Target: $X):
1. [Lever]: Reduce churn from X% to X% (impact: +$X LTV)
2. [Lever]: Increase ARPU from $X to $X via [upsell/pricing] (impact: +$X LTV)
3. [Lever]: Drive expansion revenue (impact: +$X LTV)

Decrease CAC (Current: $X → Target: $X):
1. [Lever]: Improve conversion rate from X% to X% (impact: -$X CAC)
2. [Lever]: Shift mix to lower-CAC channels (impact: -$X CAC)
3. [Lever]: Implement referral program (impact: -$X CAC)

Timeline: [X months to reach target unit economics]

4. Financial Modeling

Build P&L projections, cash flow forecasts, and scenario planning.

Workflow:

1. Build P&L Projections (3-5 Years)

   • Revenue: From revenue model (monthly detail Year 1, quarterly Year 2-3)
   • COGS: Variable costs scaling with revenue
   • Gross Profit: Revenue - COGS
   • OpEx: S&M, R&D, G&A (categorized)
   • EBITDA: Gross Profit - OpEx
   • Path to Profitability: When does EBITDA turn positive?

2. Model Cash Flow

   • Cash In: Revenue collection (account for payment terms, churn)
   • Cash Out: Payroll, vendors, fixed costs
   • Net Cash Flow: Cash in - cash out
   • Ending Cash: Starting cash + net cash flow
   • Runway: Months until cash runs out

3. Plan Working Capital Needs

   • Accounts Receivable: Outstanding customer invoices
   • Accounts Payable: Outstanding vendor bills
   • Working Capital: AR - AP
   • Cash Conversion Cycle: How long cash is tied up

4. Scenario Planning

   • Base Case: Most likely scenario (50% probability)
   • Bull Case: Everything goes right (20% probability)
   • Bear Case: Challenges hit (20% probability)
   • Stress Test: Worst case (10% probability)

5. Validate Assumptions

   • Growth Rate: Based on comparable companies, channel capacity
   • Churn Rate: Industry benchmarks, early data
   • CAC: Based on early channel tests
   • Pricing: Based on willingness-to-pay research

Output Template:

Financial Model (3-Year Projection)

Profit & Loss Statement (Base Case):

Year 1:
├── Revenue: $X
│   ├── Q1: $X
│   ├── Q2: $X
│   ├── Q3: $X
│   └── Q4: $X
├── COGS: $X (X% of revenue)
├── Gross Profit: $X (X% margin)
├── Operating Expenses: $X
│   ├── Sales & Marketing: $X (X% of revenue)
│   ├── R&D: $X (X% of revenue)
│   └── G&A: $X (X% of revenue)
├── EBITDA: -$X (X% margin)
└── Burn Rate: $X/month (avg)

Year 2:
├── Revenue: $X (X% YoY growth)
├── COGS: $X (X% of revenue)
├── Gross Profit: $X (X% margin)
├── Operating Expenses: $X
├── EBITDA: -$X (X% margin)
└── Path to Profitability: [On track/Delayed/Accelerated]

Year 3:
├── Revenue: $X (X% YoY growth)
├── COGS: $X (X% of revenue)
├── Gross Profit: $X (X% margin)
├── Operating Expenses: $X
├── EBITDA: +$X (X% margin) - **PROFITABLE**
└── Free Cash Flow: $X

Cash Flow Projection (Monthly, Year 1):

| Month | Revenue | Expenses | Net Cash Flow | Ending Cash | Runway |
|-------|---------|----------|---------------|-------------|--------|
| 1     | $X      | $X       | -$X           | $X          | X mo   |
| 2     | $X      | $X       | -$X           | $X          | X mo   |
| ...   | ...     | ...      | ...           | ...         | ...    |
| 12    | $X      | $X       | $X/-$X        | $X          | X mo   |

Funding Requirements:
├── Current Cash: $X
├── Total Burn (Year 1): $X
├── Funding Needed: $X
├── Desired Runway: X months
└── Recommended Raise: $X (burn + buffer)

Scenario Analysis:

Base Case (50% probability):
├── Year 1 Revenue: $X
├── Year 3 Revenue: $X
├── Path to Profitability: Month X
└── Return on Investment: Xx

Bull Case (20% probability - growth accelerates):
├── Year 1 Revenue: $X (+X% vs. base)
├── Year 3 Revenue: $X (+X% vs. base)
├── Path to Profitability: Month X (X months earlier)
└── Drivers: [Faster adoption, lower churn, higher pricing power]

Bear Case (20% probability - challenges arise):
├── Year 1 Revenue: $X (-X% vs. base)
├── Year 3 Revenue: $X (-X% vs. base)
├── Path to Profitability: Month X (X months delayed)
└── Drivers: [Slower adoption, higher churn, pricing pressure]

Stress Test (10% probability - major setback):
├── Scenario: [Market downturn, competitor launch, technical issue]
├── Impact: Revenue -X%, Costs +X%
├── Mitigation: [Cost cuts, pivot, bridge financing]
└── Survival: [Can survive X months with reserves]

Key Assumptions & Validation:

| Assumption | Value | Source | Confidence | Sensitivity |
|------------|-------|--------|------------|-------------|
| MoM Growth | X%    | [Comparable/Test] | Medium | High |
| Churn Rate | X%    | [Benchmark/Early data] | Low | High |
| CAC        | $X    | [Channel tests] | Medium | Medium |
| ARPU       | $X    | [Pricing research] | High | Low |
| Gross Margin | X% | [Tech stack costs] | High | Low |

Sensitivity Analysis:
- If churn increases by 2%: LTV decreases by $X, profitability delayed by X months
- If CAC increases by 20%: Payback period increases by X months
- If growth slows to X%: Revenue target missed by $X, need additional funding

5. Metrics Framework

Select north star metric, leading indicators, and health metrics.

Workflow:

1. Select North Star Metric

   • One metric that best captures value delivered to customers
   • SaaS: ARR, active users, key action completions
   • Marketplace: GMV (Gross Merchandise Value), transactions
   • Consumer: DAU/MAU, engagement time, retention

2. Identify Leading Indicators

   • Metrics that predict future performance of north star
   • Examples: Signups (predicts MRR), activation rate (predicts retention)
   • Actionable: Can be influenced by product/marketing changes

3. Define Health Metrics

   • Growth: MRR growth rate, user growth rate
   • Engagement: DAU/MAU, feature adoption
   • Efficiency: CAC, LTV:CAC, magic number
   • Quality: NPS, churn rate, support tickets

4. Instrument Tracking

   • Data Requirements: What events to track, what tools needed
   • Dashboards: Real-time visibility for team
   • Frequency: Daily, weekly, monthly reporting

5. Set Targets & Alerts

   • Benchmarks: Industry standards for each metric
   • Goals: Specific, time-bound targets
   • Alert Thresholds: When to investigate (e.g., churn spikes above X%)

Output Template:

KPI Framework

North Star Metric: [Metric name]
├── Definition: [How calculated]
├── Why This Metric: [How it captures value delivered]
├── Current: [Current value]
├── Target: [30/60/90-day goals]
└── Owned By: [Team/role responsible]

Leading Indicators (Predict North Star):

1. [Metric Name]
   ├── Definition: [How calculated]
   ├── Relationship to North Star: [How it predicts]
   ├── Current: [Value]
   ├── Target: [Goal]
   └── Frequency: [Daily/Weekly/Monthly]

2. [Metric Name]...
3. [Metric Name]...

Health Metrics (Max 5):

Growth Metrics:
├── MRR Growth Rate: X% MoM (target: >10%)
├── New Customers: X per month (target: >X)
└── Net Revenue Retention: X% (target: >100%)

Engagement Metrics:
├── DAU/MAU: X% (target: >X%)
├── Feature Adoption: X% use [key feature] (target: >X%)
└── Session Duration: X minutes avg (target: >X)

Efficiency Metrics:
├── CAC: $X (target: <$X)
├── LTV:CAC: X:1 (target: >3:1)
└── Magic Number: X (target: >1.0)

Quality Metrics:
├── NPS: X (target: >50)
├── Monthly Churn: X% (target: <X%)
└── Support Tickets per Customer: X (target: <X)

Revenue Metrics:
├── MRR: $X (target: $X by [date])
├── ARR: $X (target: $X by [date])
└── ARPU: $X (target: $X)

Dashboard & Reporting:

Real-Time Dashboard:
├── North Star: [Live updating]
├── Leading Indicators: [Daily refresh]
└── Health Metrics: [Daily/Weekly refresh]

Weekly Email:
├── To: [Leadership team]
├── Contains: [North star, key movers, alerts]
└── Format: [Trend charts, % change vs. last week]

Monthly Board Report:
├── To: [Board, investors]
├── Contains: [Financial metrics, unit economics, growth metrics]
└── Format: [P&L, cohort analysis, commentary]

Alert Thresholds:

Critical Alerts (Immediate action):
├── Churn Rate > X% (investigation within 24 hours)
├── Downtime > X minutes (all-hands)
└── Burn Rate > $X/month (cost review)

Warning Alerts (Monitor closely):
├── CAC increases > X% WoW (channel optimization)
├── Activation rate drops > X% (product review)
└── NPS falls below X (customer feedback loop)

Data Infrastructure:

Tools Required:
├── Product Analytics: [Mixpanel, Amplitude, etc.]
├── Business Intelligence: [Looker, Tableau, etc.]
├── Financial: [Stripe, QuickBooks, etc.]
└── Customer Feedback: [Delighted, Typeform, etc.]

Data Pipeline:
├── Events: [Product events pushed to analytics]
├── Integration: [Stripe → dashboard for MRR]
├── Refresh Frequency: [Real-time/Daily/Weekly]
└── Owner: [Engineering/Analytics]

Metrics Evolution:
- Month 0-6: Focus on activation, engagement (pre-revenue)
- Month 6-12: Add revenue, LTV, CAC (early monetization)
- Year 2+: Add efficiency metrics, cohort retention, expansion

Input Requirements

Required:

• value_proposition_output: Value prop informs pricing power
• market_size_data: TAM/SAM/SOM from market-intelligence
• target_segment: Customer willingness to pay

Optional:

• cost_assumptions: Known fixed/variable costs
• pricing_research: Willingness-to-pay studies
• comparable_metrics: Benchmarks from similar companies

Output Structure

{
  "revenue_streams": [
    {
      "source": "Subscription (Pro tier)",
      "model": "Monthly recurring",
      "projection_y1": 240000
    },
    {
      "source": "Enterprise contracts",
      "model": "Annual contracts",
      "projection_y1": 120000
    }
  ],
  "cost_structure": {
    "fixed": {"personnel": 30000, "infrastructure": 5000},
    "variable": {"hosting": 5, "cac": 200},
    "burn_rate": 40000
  },
  "unit_economics": {
    "CAC": 200,
    "LTV": 900,
    "payback_months": 6
  },
  "key_metrics": [
    {"metric": "MRR", "current": 0, "target": 50000},
    {"metric": "Churn Rate", "current": 0, "target": 5},
    {"metric": "LTV:CAC", "current": 0, "target": 3}
  ],
  "break_even": {
    "months": 18,
    "requirements": ["Reach 400 customers at $100 ARPU", "Maintain <5% churn"]
  }
}

Integration with Other Agents

Receives Input From:

value-proposition: Differentiation drives pricing power market-intelligence: Market size, willingness to pay problem-solution-fit: MVP scope drives cost estimates

Provides Input To:

go-to-market: Unit economics inform channel budget validation: Assumptions become testable hypotheses funding: Financial model for investor pitch

Best Practices

1. Value-Based Pricing: Price to value delivered, not cost-plus
2. Simple > Complex: 3 tiers beats 10 tiers
3. Optimize for LTV, Not CAC: Better retention > cheaper acquisition
4. Know Your Unit Economics: LTV:CAC and payback period drive all decisions
5. Scenario Plan: Base/bull/bear cases for every assumption

Common Pitfalls

• ❌ Cost-plus pricing (leaves money on table)
• ❌ Too many pricing tiers (decision paralysis)
• ❌ Ignoring churn (focusing only on acquisition)
• ❌ Unrealistic growth assumptions (10x YoY without proof)
• ✅ Value pricing, 3 tiers, retention focus, validated assumptions

This agent designs business models that are financially sustainable, scalable, and aligned with market reality.