Financial Analyst Skill

The Financial Analyst brings Wall Street-level financial rigor to Amazon advertising. It goes beyond simple ROAS to calculate true profitability, unit economics, customer lifetime value, and optimal budget allocation.

Core Capabilities

1. True Profitability Analysis

•Revenue minus ALL costs (not just ad spend)
•Product cost (COGS) integration
•Amazon fees calculation (FBA, referral, storage)
•Net profit per unit and per order
•Contribution margin analysis

2. Unit Economics Modeling

•Customer Acquisition Cost (CAC)
•Lifetime Value (LTV) estimation
•LTV:CAC ratio optimization
•Break-even analysis
•Payback period calculation

3. Budget Optimization

•Optimal budget allocation across campaigns
•Marginal ROAS analysis
•Diminishing returns detection
•Budget reallocation recommendations
•Scenario modeling for budget changes

4. ROI Attribution

•Multi-touch attribution
•New vs returning customer value
•Brand halo effect measurement
•Organic lift from paid ads
•Cross-product attribution

5. Financial Forecasting

•Revenue projections
•Cash flow impact
•Seasonal budget planning
•What-if scenario analysis

Financial Analysis Reports

Product Profitability Deep Dive

json

{
  "action": "analyze_product_profitability",
  "asin": "B0YOUR001",
  "period": "last_30_days"
}

Response:

json

{
  "profitability_analysis": {
    "asin": "B0YOUR001",
    "product_name": "Premium Wireless Headphones",
    "period": "Jan 6 - Feb 5, 2026",
    
    "revenue_breakdown": {
      "gross_revenue": 45000,
      "units_sold": 500,
      "average_selling_price": 90.00,
      "organic_revenue": 27000,
      "paid_revenue": 18000
    },
    
    "cost_structure": {
      "product_cost_per_unit": 22.00,
      "total_cogs": 11000,
      "amazon_referral_fee": 6750,
      "fba_fulfillment_fee": 3500,
      "fba_storage_fee": 180,
      "advertising_cost": 3600,
      "total_costs": 25030
    },
    
    "profitability_metrics": {
      "gross_profit": 19970,
      "gross_margin_pct": 44.4,
      "net_profit_per_unit": 39.94,
      "contribution_margin": 68.00,
      "contribution_margin_pct": 75.6,
      "tacos": 8.0,
      "true_acos": 20.0,
      "roas": 5.0
    },
    
    "advertising_efficiency": {
      "ad_spend": 3600,
      "ad_attributed_revenue": 18000,
      "ad_attributed_profit": 8994,
      "profit_per_ad_dollar": 2.50,
      "break_even_acos": 68.0,
      "current_vs_break_even": "52% headroom"
    },
    
    "insights": [
      "Product is highly profitable with 52% buffer to break-even ACoS",
      "Recommend increasing ad budget - marginal profit available",
      "Storage fees are minimal - inventory levels healthy"
    ]
  }
}

Unit Economics Dashboard

json

{
  "action": "calculate_unit_economics",
  "account_level": true
}

Response:

json

{
  "unit_economics": {
    "period": "Last 90 days",
    
    "customer_metrics": {
      "total_customers": 4500,
      "new_customers": 3200,
      "returning_customers": 1300,
      "repeat_rate": 28.9,
      "average_orders_per_customer": 1.42
    },
    
    "acquisition": {
      "total_ad_spend": 48000,
      "new_customers_from_ads": 2400,
      "cac_blended": 10.67,
      "cac_new_only": 20.00,
      "cac_trend": "improving (-8% MoM)"
    },
    
    "lifetime_value": {
      "average_order_value": 75.00,
      "orders_per_customer_12mo": 2.1,
      "gross_margin_pct": 45,
      "estimated_ltv": 70.88,
      "ltv_calculation": "75 × 2.1 × 0.45 = $70.88"
    },
    
    "ltv_cac_analysis": {
      "ltv_cac_ratio": 3.54,
      "benchmark": "3.0+ is excellent",
      "status": "healthy",
      "payback_period_days": 45,
      "recommendation": "Ratio supports increased acquisition spend"
    },
    
    "customer_segmentation": {
      "high_value": {
        "count": 450,
        "avg_ltv": 180.00,
        "acquisition_source": "Brand campaigns (65%)"
      },
      "medium_value": {
        "count": 2700,
        "avg_ltv": 65.00,
        "acquisition_source": "Product campaigns (55%)"
      },
      "low_value": {
        "count": 1350,
        "avg_ltv": 25.00,
        "acquisition_source": "Generic campaigns (70%)"
      }
    }
  }
}

Budget Optimization Analysis

json

{
  "action": "optimize_budget_allocation",
  "total_budget": 50000,
  "objective": "maximize_profit"
}

Response:

json

{
  "budget_optimization": {
    "total_budget": 50000,
    "objective": "maximize_profit",
    
    "current_allocation": {
      "brand_campaigns": {"budget": 10000, "roas": 8.5, "marginal_roas": 6.2},
      "product_campaigns": {"budget": 25000, "roas": 4.2, "marginal_roas": 3.1},
      "generic_campaigns": {"budget": 15000, "roas": 2.8, "marginal_roas": 1.5}
    },
    
    "recommended_allocation": {
      "brand_campaigns": {"budget": 15000, "change": "+50%", "reason": "High marginal ROAS, under-invested"},
      "product_campaigns": {"budget": 28000, "change": "+12%", "reason": "Solid returns, moderate increase"},
      "generic_campaigns": {"budget": 7000, "change": "-53%", "reason": "Below profit threshold, reallocate"}
    },
    
    "projected_impact": {
      "current_revenue": 185000,
      "projected_revenue": 215000,
      "revenue_change": "+16%",
      "current_profit": 42000,
      "projected_profit": 58000,
      "profit_change": "+38%"
    },
    
    "marginal_analysis": {
      "last_dollar_efficiency": [
        {"campaign": "Brand", "next_1000_roas": 5.8, "recommendation": "invest"},
        {"campaign": "Product", "next_1000_roas": 2.9, "recommendation": "invest"},
        {"campaign": "Generic", "next_1000_roas": 1.2, "recommendation": "cut"}
      ]
    },
    
    "scenario_comparison": [
      {"scenario": "Aggressive Growth", "budget": 70000, "proj_profit": 72000, "risk": "medium"},
      {"scenario": "Efficient Growth", "budget": 50000, "proj_profit": 58000, "risk": "low"},
      {"scenario": "Profit Maximization", "budget": 35000, "proj_profit": 48000, "risk": "very_low"}
    ]
  }
}

Advanced Financial Features

Break-Even Calculator

json

{
  "break_even": {
    "product_cost": 22.00,
    "selling_price": 89.99,
    "amazon_fees_pct": 35,
    "break_even_acos": 65,
    "formula": "(Price - COGS - Fees) / Price × 100",
    "interpretation": "Any ACoS below 65% is profitable"
  }
}

TACoS vs ACoS Analysis

json

{
  "tacos_acos_comparison": {
    "acos": 22.5,
    "tacos": 8.2,
    "organic_ratio": 63.5,
    "interpretation": "Strong organic presence - ads driving halo effect",
    "benchmark": "TACoS < 10% is excellent for established products",
    "trend": "TACoS improving as organic grows"
  }
}

Integration Points

Consumes from:

•Amazon Seller Central API (orders, fees)
•Inventory management systems (COGS)
•grok-admaster-operator: Campaign spend data
•simulation-lab: Forecast scenarios

Feeds to:

•executive-reporter: Financial sections
•campaign-strategist: Budget constraints
•evolution-engine: Profit-based fitness functions

Files

code

.agent/skills/financial-analyst/
├── SKILL.md
├── scripts/
│   ├── profitability_calculator.py
│   ├── unit_economics_engine.py
│   ├── budget_optimizer.py
│   └── ltv_modeler.py
└── resources/
    └── amazon_fee_structure.json

This skill transforms advertising from a cost center to a strategic profit lever with full financial transparency.