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+---
+name: financial-unit-economics
+description: Use when evaluating business model viability, analyzing profitability per customer/product/transaction, validating startup metrics (CAC, LTV, payback period), making pricing decisions, assessing scalability, comparing business models, or when user mentions unit economics, CAC/LTV ratio, contribution margin, customer profitability, break-even analysis, or needs to determine if a business can be profitable at scale.
+---
+# Financial Unit Economics
+
+## Table of Contents
+- [Purpose](#purpose)
+- [When to Use](#when-to-use)
+- [What Is It?](#what-is-it)
+- [Workflow](#workflow)
+- [Common Patterns](#common-patterns)
+- [Guardrails](#guardrails)
+- [Quick Reference](#quick-reference)
+
+## Purpose
+
+Financial Unit Economics analyzes the profitability of individual units (customers, products, transactions) to determine if a business model is viable and scalable. This skill guides you through calculating key metrics (CAC, LTV, contribution margin), interpreting ratios, conducting cohort analysis, and making data-driven decisions about pricing, marketing spend, and growth strategy.
+
+## When to Use
+
+Use this skill when:
+
+- **Business model validation**: Determine if startup/new product can be profitable at scale
+- **Pricing decisions**: Set prices based on target margins and customer economics
+- **Marketing spend**: Assess ROI of acquisition channels, optimize CAC
+- **Growth strategy**: Decide when to scale (raise funding, increase spend) based on unit economics
+- **Product roadmap**: Prioritize features that improve retention or reduce churn (increase LTV)
+- **Investor pitch**: Demonstrate business model viability with CAC, LTV, payback metrics
+- **Channel optimization**: Compare profitability across customer segments or acquisition channels
+- **Subscription models**: Analyze recurring revenue, churn, cohort retention curves
+- **Marketplace economics**: Model take rate, supply/demand side economics, liquidity
+- **Financial planning**: Forecast cash flow, runway, burn rate based on unit economics
+
+Trigger phrases: "unit economics", "CAC/LTV", "customer acquisition cost", "lifetime value", "contribution margin", "payback period", "customer profitability", "break-even", "cohort analysis", "is this business viable?"
+
+## What Is It?
+
+**Financial Unit Economics** is the practice of measuring profitability at the most granular level (per customer, product, or transaction) to understand if revenue from a single unit exceeds the cost to acquire and serve it.
+
+**Core components**:
+- **CAC (Customer Acquisition Cost)**: Total sales/marketing spend ÷ new customers acquired
+- **LTV (Lifetime Value)**: Revenue from customer over their lifetime minus variable costs
+- **Contribution Margin**: (Revenue - Variable Costs) ÷ Revenue (as %)
+- **LTV/CAC Ratio**: Measures return on acquisition investment (target: 3:1 or higher)
+- **Payback Period**: Months to recover CAC from customer revenue
+- **Cohort Analysis**: Track metrics over time for customer groups (by acquisition month/channel)
+
+**Quick example:**
+
+**Scenario**: SaaS startup, subscription model ($100/month), analyzing unit economics.
+
+**Metrics**:
+- **CAC**: $20k marketing spend, 100 new customers → CAC = $200
+- **Monthly revenue per customer**: $100
+- **Variable costs**: $20/customer/month (hosting, support)
+- **Gross margin**: ($100 - $20) / $100 = 80%
+- **Monthly churn**: 5% → Average lifetime = 1 / 0.05 = 20 months
+- **LTV**: $100 revenue × 20 months × 80% margin = $1,600
+- **LTV/CAC**: $1,600 / $200 = 8:1 ✓ (healthy, >3:1)
+- **Payback period**: $200 CAC ÷ ($100 × 80% margin) = 2.5 months ✓ (good, <12 months)
+
+**Interpretation**: Strong unit economics. Each customer generates 8× their acquisition cost. Can profitably scale marketing spend. Payback in 2.5 months means fast capital recovery.
+
+**Core benefits**:
+- **Early warning system**: Detect unsustainable business models before scaling losses
+- **Data-driven growth**: Know when unit economics justify increasing spend
+- **Channel optimization**: Identify which acquisition channels are profitable
+- **Pricing power**: Quantify impact of price changes on profitability
+- **Investor confidence**: Demonstrate path to profitability with clear metrics
+
+## Workflow
+
+Copy this checklist and track your progress:
+
+```
+Unit Economics Analysis Progress:
+- [ ] Step 1: Define the unit
+- [ ] Step 2: Calculate CAC
+- [ ] Step 3: Calculate LTV
+- [ ] Step 4: Assess contribution margin
+- [ ] Step 5: Analyze cohorts
+- [ ] Step 6: Interpret and recommend
+```
+
+**Step 1: Define the unit**
+
+What is your unit of analysis? (Customer, product SKU, transaction, subscription). See [resources/template.md](resources/template.md#unit-definition-template).
+
+**Step 2: Calculate CAC**
+
+Total acquisition costs (sales + marketing) ÷ new units acquired. Break down by channel if applicable. See [resources/template.md](resources/template.md#cac-calculation-template) and [resources/methodology.md](resources/methodology.md#1-customer-acquisition-cost-cac).
+
+**Step 3: Calculate LTV**
+
+Revenue over unit lifetime minus variable costs. Use cohort data for retention/churn. See [resources/template.md](resources/template.md#ltv-calculation-template) and [resources/methodology.md](resources/methodology.md#2-lifetime-value-ltv).
+
+**Step 4: Assess contribution margin**
+
+(Revenue - Variable Costs) ÷ Revenue. Identify levers to improve margin. See [resources/template.md](resources/template.md#contribution-margin-template) and [resources/methodology.md](resources/methodology.md#3-contribution-margin-analysis).
+
+**Step 5: Analyze cohorts**
+
+Track retention, LTV, payback by customer cohort (acquisition month/channel/segment). See [resources/template.md](resources/template.md#cohort-analysis-template) and [resources/methodology.md](resources/methodology.md#4-cohort-analysis).
+
+**Step 6: Interpret and recommend**
+
+Assess LTV/CAC ratio, payback period, cash efficiency. Make recommendations (pricing, channels, growth). See [resources/template.md](resources/template.md#interpretation-template) and [resources/methodology.md](resources/methodology.md#5-interpreting-unit-economics).
+
+Validate using [resources/evaluators/rubric_financial_unit_economics.json](resources/evaluators/rubric_financial_unit_economics.json). **Minimum standard**: Average score ≥ 3.5.
+
+## Common Patterns
+
+**Pattern 1: SaaS Subscription Model**
+- **Key metrics**: MRR, ARR, churn rate, LTV/CAC, payback period, CAC payback
+- **Calculation**: LTV = ARPU × Gross Margin % ÷ Churn Rate
+- **Benchmarks**: LTV/CAC ≥3:1, Payback <12 months, Churn <5% monthly (B2C) or <2% (B2B)
+- **Levers**: Reduce churn (increase LTV), upsell/cross-sell (increase ARPU), optimize channels (reduce CAC)
+- **When**: Subscription business, recurring revenue, retention critical
+
+**Pattern 2: E-commerce / Transactional**
+- **Key metrics**: AOV (Average Order Value), repeat purchase rate, contribution margin per order, CAC
+- **Calculation**: LTV = AOV × Purchase Frequency × Gross Margin % × Customer Lifetime (years)
+- **Benchmarks**: Contribution margin ≥40%, Repeat purchase rate ≥25%, LTV/CAC ≥2:1
+- **Levers**: Increase AOV (bundling, upsells), drive repeat purchases (loyalty programs), reduce variable costs
+- **When**: Transactional business, e-commerce, retail
+
+**Pattern 3: Marketplace / Platform**
+- **Key metrics**: Take rate, GMV (Gross Merchandise Value), supply/demand CAC, liquidity
+- **Calculation**: LTV = GMV per user × Take Rate × Gross Margin % ÷ Churn Rate
+- **Benchmarks**: Take rate 10-30%, LTV/CAC ≥3:1 for both sides, network effects kicking in
+- **Levers**: Increase take rate (value-added services), improve matching (increase GMV), balance supply/demand
+- **When**: Two-sided marketplace, platform business
+
+**Pattern 4: Freemium / PLG (Product-Led Growth)**
+- **Key metrics**: Free-to-paid conversion rate, time to convert, paid user LTV, blended CAC
+- **Calculation**: Blended LTV = (Free users × Conversion % × Paid LTV) - (Free user costs)
+- **Benchmarks**: Conversion ≥2%, Time to convert <90 days, Paid LTV/CAC ≥4:1
+- **Levers**: Increase conversion rate (improve product, optimize paywall), reduce time to value, lower CAC via virality
+- **When**: Product-led growth, freemium model, viral product
+
+**Pattern 5: Enterprise / High-Touch Sales**
+- **Key metrics**: CAC (including sales team costs), sales cycle length, NRR (Net Revenue Retention), LTV
+- **Calculation**: LTV = ACV (Annual Contract Value) × Gross Margin % × Average Customer Lifetime (years)
+- **Benchmarks**: LTV/CAC ≥3:1, Sales efficiency (ARR added ÷ S&M spend) ≥1.0, NRR ≥110%
+- **Levers**: Shorten sales cycle, increase ACV (upsell, premium tiers), improve retention (NRR)
+- **When**: Enterprise sales, high ACV, long sales cycles
+
+## Guardrails
+
+**Critical requirements:**
+
+1. **Fully-loaded CAC**: Include all acquisition costs (sales salaries, marketing spend, tools, overhead allocation). Underestimating CAC makes unit economics look better than reality. Common miss: excluding sales team salaries.
+
+2. **True variable costs**: Only include costs that scale with each unit (COGS, hosting per user, transaction fees). Don't include fixed costs (rent, core engineering). LTV calculation requires accurate margin.
+
+3. **Cohort-based LTV**: Don't average across all customers. Early cohorts ≠ recent cohorts. Track retention curves by cohort (acquisition month/channel). LTV should be based on observed retention, not assumptions.
+
+4. **Time horizon matters**: LTV is a prediction. Use conservative assumptions. For new products, LTV estimates are unreliable (insufficient data). Weight recent cohorts more heavily.
+
+5. **Payback period vs. LTV/CAC**: Both matter. High LTV/CAC but long payback (>18 months) strains cash. Fast payback (<6 months) allows rapid reinvestment. Optimize for both.
+
+6. **Channel-level analysis**: Blended metrics hide truth. CAC and LTV vary by channel (paid search vs. referral vs. content). Analyze separately to optimize spend.
+
+7. **Retention is king**: Small changes in churn have exponential impact on LTV. Improving monthly churn from 5% to 4% increases LTV by 25%. Retention improvements > acquisition improvements.
+
+8. **Gross margin floor**: Need ≥60% gross margin for SaaS, ≥40% for e-commerce to be viable. Low margin means high LTV/CAC ratio still yields poor cash flow.
+
+**Common pitfalls:**
+
+- ❌ **Ignoring churn**: Assuming customers stay forever. Reality: churn compounds. Use cohort retention curves.
+- ❌ **Vanity LTV**: Using unrealistic retention (e.g., 5 year LTV with 1 month of data). Stick to observed behavior.
+- ❌ **Blended CAC**: Mixing profitable and unprofitable channels. Break down by channel, segment, cohort.
+- ❌ **Not updating**: Unit economics change as product, market, competition evolve. Re-calculate quarterly.
+- ❌ **Missing costs**: Forgetting support costs, payment processing fees, fraud losses, refunds. Track everything.
+- ❌ **Premature scaling**: Growing before unit economics work (LTV/CAC <2:1). "We'll make it up in volume" rarely works.
+
+## Quick Reference
+
+**Key formulas:**
+
+```
+CAC = (Sales + Marketing Costs) ÷ New Customers Acquired
+
+LTV (subscription) = ARPU × Gross Margin % ÷ Monthly Churn Rate
+
+LTV (transactional) = AOV × Purchase Frequency × Gross Margin % × Lifetime (years)
+
+Contribution Margin % = (Revenue - Variable Costs) ÷ Revenue
+
+LTV/CAC Ratio = Lifetime Value ÷ Customer Acquisition Cost
+
+Payback Period (months) = CAC ÷ (Monthly Revenue × Gross Margin %)
+
+CAC Payback (months) = S&M Spend ÷ (New ARR × Gross Margin %)
+
+Gross Margin % = (Revenue - COGS) ÷ Revenue
+
+Customer Lifetime (months) = 1 ÷ Monthly Churn Rate
+
+MRR (Monthly Recurring Revenue) = Sum of all monthly subscriptions
+
+ARR (Annual Recurring Revenue) = MRR × 12
+
+ARPU (Average Revenue Per User) = Total Revenue ÷ Total Users
+
+NRR (Net Revenue Retention) = (Starting ARR + Expansion - Contraction - Churn) ÷ Starting ARR
+```
+
+**Benchmarks (varies by stage and industry):**
+
+| Metric | Good | Acceptable | Poor |
+|--------|------|------------|------|
+| **LTV/CAC Ratio** | ≥5:1 | 3:1 - 5:1 | <3:1 |
+| **Payback Period** | <6 months | 6-12 months | >18 months |
+| **Gross Margin (SaaS)** | ≥80% | 60-80% | <60% |
+| **Gross Margin (E-commerce)** | ≥50% | 40-50% | <40% |
+| **Monthly Churn (B2C SaaS)** | <3% | 3-7% | >7% |
+| **Monthly Churn (B2B SaaS)** | <1% | 1-3% | >3% |
+| **CAC Payback (SaaS)** | <12 months | 12-18 months | >18 months |
+| **NRR (SaaS)** | ≥120% | 100-120% | <100% |
+
+**Decision framework:**
+
+| LTV/CAC | Payback | Recommendation |
+|---------|---------|----------------|
+| <1:1 | Any | **Stop**: Losing money on every customer. Fix model or pivot. |
+| 1:1 - 2:1 | >12 months | **Caution**: Marginal economics. Don't scale yet. Improve retention or reduce CAC. |
+| 2:1 - 3:1 | 6-12 months | **Optimize**: Unit economics acceptable. Focus on improving before scaling. |
+| 3:1 - 5:1 | <12 months | **Scale**: Good economics. Can profitably invest in growth. |
+| >5:1 | <6 months | **Aggressive scale**: Excellent economics. Raise capital, increase spend rapidly. |
+
+**Inputs required:**
+- **Revenue data**: Pricing, ARPU, AOV, transaction frequency
+- **Cost data**: Sales/marketing spend, COGS, variable costs per customer
+- **Retention data**: Churn rate, cohort retention curves, repeat purchase behavior
+- **Channel data**: CAC by acquisition channel, LTV by segment
+- **Time period**: Cohort definition (monthly, quarterly), historical data range
+
+**Outputs produced:**
+- `unit-economics-analysis.md`: Full analysis with CAC, LTV, ratios, cohort breakdowns
+- `cohort-retention-table.csv`: Retention curves by cohort
+- `channel-profitability.csv`: CAC and LTV by acquisition channel
+- `recommendations.md`: Pricing, channel, growth recommendations based on metrics
--- a/skills/financial-unit-economics/resources/evaluators/rubric_financial_unit_economics.json
+++ b/skills/financial-unit-economics/resources/evaluators/rubric_financial_unit_economics.json
@@ -0,0 +1,211 @@
+{
+  "criteria": [
+    {
+      "name": "CAC Calculation Completeness",
+      "description": "Fully-loaded CAC includes all S&M costs (salaries, tools, overhead). Channel-level breakdown provided.",
+      "scale": {
+        "1": "Missing major costs (e.g., sales salaries). No channel breakdown. CAC severely underestimated.",
+        "3": "Most costs included. Channel breakdown present but incomplete. CAC reasonably accurate.",
+        "5": "Fully-loaded CAC with all costs itemized. Detailed channel-level breakdown. Time-period matching correct."
+      }
+    },
+    {
+      "name": "LTV Methodology Rigor",
+      "description": "LTV calculated using cohort data, not averages. Conservative assumptions, observed retention used.",
+      "scale": {
+        "1": "LTV based on average retention or unrealistic assumptions (e.g., 5-year LTV with 1 month data). No cohort analysis.",
+        "3": "LTV uses cohort data for some periods. Assumptions stated but may be optimistic. Retention curves shown.",
+        "5": "LTV from observed cohort retention. Conservative extrapolation. Multiple cohorts compared. Time horizon justified."
+      }
+    },
+    {
+      "name": "Contribution Margin Accuracy",
+      "description": "Contribution margin includes only variable costs (COGS, hosting, fees, support per unit). Fixed costs excluded.",
+      "scale": {
+        "1": "Margin calculation includes fixed costs or omits major variable costs. Inaccurate gross margin.",
+        "3": "Most variable costs included. Some minor costs may be missing. Margin calculation mostly correct.",
+        "5": "All variable costs identified and quantified. Fixed costs correctly excluded. Margin breakdown clear and accurate."
+      }
+    },
+    {
+      "name": "Cohort Analysis Depth",
+      "description": "Retention tracked by cohort (month/channel/segment). Trends analyzed. Cohorts compared.",
+      "scale": {
+        "1": "No cohort analysis. Metrics blended across all customers. No retention curves shown.",
+        "3": "Basic cohort table present. Some cohorts tracked. Trends mentioned but not deeply analyzed.",
+        "5": "Detailed cohort tables by month and channel. Retention trends analyzed. Cohort comparisons with insights drawn."
+      }
+    },
+    {
+      "name": "Ratio Interpretation",
+      "description": "LTV/CAC ratio interpreted with context (benchmarks, stage, industry). Payback period considered alongside ratio.",
+      "scale": {
+        "1": "Ratio calculated but not interpreted. No benchmarks provided. Payback period ignored.",
+        "3": "Ratio interpreted with general benchmarks. Payback mentioned. Some context (stage, industry) considered.",
+        "5": "Ratio interpreted with stage-appropriate benchmarks. Payback period analyzed. Combined assessment (ratio + payback) informs recommendations."
+      }
+    },
+    {
+      "name": "Channel-Level Analysis",
+      "description": "CAC, LTV, and ratios broken down by acquisition channel. Best/worst channels identified.",
+      "scale": {
+        "1": "Blended metrics only. No channel breakdown. Cannot identify which channels are profitable.",
+        "3": "Some channel breakdown provided. Major channels identified. Analysis present but incomplete.",
+        "5": "Comprehensive channel-level CAC and LTV. All channels compared. Clear identification of best/worst performers with actionable insights."
+      }
+    },
+    {
+      "name": "Sensitivity Analysis",
+      "description": "Key assumptions tested (churn, ARPU, CAC, margin). Impact on ratios quantified.",
+      "scale": {
+        "1": "No sensitivity analysis. Assumptions not tested. Single-point estimates only.",
+        "3": "Basic sensitivity on 1-2 variables. Impact direction noted but not quantified. Limited scenarios.",
+        "5": "Comprehensive sensitivity on key variables (churn, ARPU, CAC, margin). Multiple scenarios. Impact quantified. Breakeven thresholds identified."
+      }
+    },
+    {
+      "name": "Retention vs. Acquisition Balance",
+      "description": "Analysis recognizes retention impact on LTV. Recommendations balance reducing CAC with improving retention.",
+      "scale": {
+        "1": "Focus solely on CAC reduction. Retention improvements not considered. No churn analysis.",
+        "3": "Both CAC and retention mentioned. Some churn analysis. Balance between acquisition and retention somewhat considered.",
+        "5": "Clear recognition that retention drives LTV. Churn impact quantified. Recommendations prioritize retention improvements over pure CAC reduction where appropriate."
+      }
+    },
+    {
+      "name": "Business Model Appropriateness",
+      "description": "Analysis matches business model (subscription, transactional, marketplace, freemium, enterprise). Metrics and benchmarks appropriate.",
+      "scale": {
+        "1": "Generic analysis not tailored to business model. Wrong metrics or formulas used. Inappropriate benchmarks.",
+        "3": "Analysis somewhat tailored to business model. Mostly correct metrics. Benchmarks generally appropriate.",
+        "5": "Analysis fully customized to business model. Correct metrics (MRR/ARR for SaaS, AOV for ecommerce, GMV/take rate for marketplace). Stage and industry-appropriate benchmarks."
+      }
+    },
+    {
+      "name": "Actionability of Recommendations",
+      "description": "Clear, specific recommendations on pricing, channels, retention, and growth based on unit economics. Action items with owners/timelines.",
+      "scale": {
+        "1": "Vague or generic recommendations. No specific actions. Cannot implement recommendations.",
+        "3": "Recommendations provided but somewhat generic. Some specific actions. Implementation path unclear.",
+        "5": "Specific, actionable recommendations (e.g., 'increase price by $10', 'pause paid social', 'implement onboarding checklist'). Clear priorities. Implementation steps outlined."
+      }
+    }
+  ],
+  "guidance_by_type": {
+    "SaaS Subscription": {
+      "target_score": 4.2,
+      "key_criteria": ["LTV Methodology Rigor", "Cohort Analysis Depth", "Retention vs. Acquisition Balance"],
+      "common_pitfalls": ["Ignoring churn impact", "Not tracking NRR", "Vanity LTV with insufficient data"],
+      "specific_guidance": "Focus on monthly churn, cohort retention curves, ARPU trends, and NRR. Payback <12 months critical for SaaS."
+    },
+    "E-commerce / Transactional": {
+      "target_score": 3.9,
+      "key_criteria": ["Contribution Margin Accuracy", "Channel-Level Analysis", "CAC Calculation Completeness"],
+      "common_pitfalls": ["Missing shipping/fulfillment costs", "Not tracking repeat purchase rate", "Blending one-time and repeat customers"],
+      "specific_guidance": "Track AOV, purchase frequency, repeat rate separately from first purchase. Include all COGS, shipping, and payment fees in margin calculation."
+    },
+    "Marketplace / Platform": {
+      "target_score": 4.0,
+      "key_criteria": ["Business Model Appropriateness", "Channel-Level Analysis", "LTV Methodology Rigor"],
+      "common_pitfalls": ["Not analyzing supply and demand sides separately", "Ignoring take rate compression risk", "Missing network effects in projections"],
+      "specific_guidance": "Analyze both sides of marketplace. Track GMV per user, take rate, liquidity. Consider network effects on retention and growth."
+    },
+    "Freemium / PLG": {
+      "target_score": 4.1,
+      "key_criteria": ["LTV Methodology Rigor", "Cohort Analysis Depth", "Channel-Level Analysis"],
+      "common_pitfalls": ["Not separating free and paid user economics", "Ignoring free user costs", "Overstating viral coefficient"],
+      "specific_guidance": "Calculate blended LTV accounting for free user costs and conversion rates. Track free-to-paid conversion by cohort. Measure viral coefficient and payback for organic vs. paid channels."
+    },
+    "Enterprise / High-Touch Sales": {
+      "target_score": 4.3,
+      "key_criteria": ["CAC Calculation Completeness", "LTV Methodology Rigor", "Ratio Interpretation"],
+      "common_pitfalls": ["Excluding sales team costs from CAC", "Not accounting for long sales cycles in lag", "Ignoring expansion revenue (NRR)"],
+      "specific_guidance": "Include full sales team costs (salaries, tools, overhead). Account for 3-12 month sales cycle lag. Track NRR >110% as key metric. ACV and contract length critical for LTV."
+    }
+  },
+  "guidance_by_complexity": {
+    "Simple (Single Product, Early Stage)": {
+      "target_score": 3.5,
+      "focus_areas": ["CAC Calculation Completeness", "LTV Methodology Rigor", "Ratio Interpretation"],
+      "acceptable_shortcuts": ["Limited cohort data (3-6 months)", "Simplified channel analysis", "Basic sensitivity (1-2 variables)"],
+      "specific_guidance": "Focus on getting CAC and LTV directionally correct. Use simple LTV formula with conservative churn estimate. Aim for LTV/CAC >2:1 minimum."
+    },
+    "Standard (Multi-Channel, Growth Stage)": {
+      "target_score": 4.0,
+      "focus_areas": ["Channel-Level Analysis", "Cohort Analysis Depth", "Sensitivity Analysis"],
+      "acceptable_shortcuts": ["Quarterly vs. monthly cohorts acceptable", "Limited multi-product analysis"],
+      "specific_guidance": "Full channel breakdown required. 6-12 months cohort data expected. Sensitivity on churn, CAC, ARPU. Target LTV/CAC >3:1, payback <12 months."
+    },
+    "Complex (Multi-Product, Mature)": {
+      "target_score": 4.5,
+      "focus_areas": ["All criteria", "Advanced metrics (NRR, CAC efficiency, cohort trends)"],
+      "acceptable_shortcuts": ["None - comprehensive analysis expected"],
+      "specific_guidance": "Full cohort analysis by product, channel, segment. NRR >110% expected. CAC efficiency (Magic Number >1.0). Multi-year LTV projections with sensitivity. Target LTV/CAC >4:1, payback <6 months."
+    }
+  },
+  "common_failure_modes": [
+    {
+      "name": "Underestimated CAC",
+      "symptom": "CAC excludes sales salaries, tools, or overhead. Only includes ad spend.",
+      "detection": "Check if CAC = ad spend ÷ customers. Ask: 'Are sales team costs included?'",
+      "fix": "Add all S&M costs. Include salaries, benefits, tools, overhead allocation. Recalculate fully-loaded CAC."
+    },
+    {
+      "name": "Vanity LTV",
+      "symptom": "LTV projects 3-5 years out with only 1-3 months of retention data. Unrealistic assumptions.",
+      "detection": "Check cohort data availability. If LTV >> observed revenue, likely vanity LTV.",
+      "fix": "Use only observed retention data. For new products, cap LTV projection at 12-18 months max. Be conservative."
+    },
+    {
+      "name": "Blended Metrics",
+      "symptom": "Single blended CAC and LTV. No breakdown by channel or cohort. Hides poor-performing channels.",
+      "detection": "Ask: 'What is CAC and LTV by channel?' If not available, blended metrics likely.",
+      "fix": "Break down CAC and LTV by acquisition channel. Identify best and worst performers. Optimize spend accordingly."
+    },
+    {
+      "name": "Ignoring Churn",
+      "symptom": "LTV assumes customers stay forever or uses unrealistically low churn. No cohort retention curves.",
+      "detection": "Check if churn rate stated. If LTV formula doesn't include churn or lifetime, likely ignored.",
+      "fix": "Calculate churn from cohort data. Use retention curves to project LTV. Test sensitivity to churn changes."
+    },
+    {
+      "name": "Fixed Costs in Margin",
+      "symptom": "Contribution margin includes fixed costs (engineering, rent, admin). Margin too low.",
+      "detection": "Review margin calculation. If <40% for software or <20% for ecommerce, may include fixed costs.",
+      "fix": "Exclude fixed costs. Include only variable costs that scale with each unit (COGS, hosting per user, support per customer, payment fees)."
+    },
+    {
+      "name": "No Cohort Analysis",
+      "symptom": "Metrics averaged across all customers. No retention curves. Cannot see if newer cohorts perform better/worse.",
+      "detection": "Ask: 'How does retention differ by cohort?' If not tracked, no cohort analysis.",
+      "fix": "Build cohort retention table. Track M0, M1, M3, M6, M12 retention by acquisition month. Identify trends."
+    },
+    {
+      "name": "Payback Period Ignored",
+      "symptom": "High LTV/CAC ratio celebrated, but payback >18 months. Cash burn high, growth unsustainable.",
+      "detection": "Check payback calculation. If not mentioned, likely ignored.",
+      "fix": "Calculate payback = CAC ÷ (monthly revenue × margin). If >12-18 months, growth will strain cash. Consider raising capital or improving payback."
+    },
+    {
+      "name": "No Sensitivity Analysis",
+      "symptom": "Single-point estimates. No testing of assumptions. Fragile economics if assumptions wrong.",
+      "detection": "Ask: 'What if churn increases 2%?' If impact unknown, no sensitivity analysis.",
+      "fix": "Test churn +/- 1-2%, ARPU +/- 10-20%, CAC +/- 10-20%. Quantify impact on LTV/CAC ratio. Identify breakeven thresholds."
+    },
+    {
+      "name": "Wrong Business Model Metrics",
+      "symptom": "Using SaaS metrics (MRR, churn) for transactional business. Or marketplace metrics for subscription business. Confusion.",
+      "detection": "Check if metrics match business model. SaaS should have MRR/ARR/churn. Ecommerce should have AOV/frequency. Marketplace should have GMV/take rate.",
+      "fix": "Use business-model-appropriate metrics. SaaS: ARPU, churn, NRR. Transactional: AOV, purchase frequency. Marketplace: GMV, take rate, liquidity."
+    },
+    {
+      "name": "No Actionable Recommendations",
+      "symptom": "Analysis ends with metrics. No recommendations on pricing, channels, retention, or growth strategy.",
+      "detection": "Check if recommendations section exists. If analysis only reports metrics without actions, non-actionable.",
+      "fix": "Provide specific recommendations: pricing changes, channel allocation, retention improvements, growth pace. Tie recommendations directly to unit economics findings."
+    }
+  ],
+  "minimum_standard": 3.5,
+  "target_score": 4.0,
+  "excellence_threshold": 4.5
+}
--- a/skills/financial-unit-economics/resources/methodology.md
+++ b/skills/financial-unit-economics/resources/methodology.md
@@ -0,0 +1,500 @@
+# Financial Unit Economics Methodology
+
+Advanced techniques for calculating, analyzing, and optimizing unit economics.
+
+## Table of Contents
+1. [Customer Acquisition Cost (CAC)](#1-customer-acquisition-cost-cac)
+2. [Lifetime Value (LTV)](#2-lifetime-value-ltv)
+3. [Contribution Margin Analysis](#3-contribution-margin-analysis)
+4. [Cohort Analysis](#4-cohort-analysis)
+5. [Interpreting Unit Economics](#5-interpreting-unit-economics)
+6. [Advanced Topics](#6-advanced-topics)
+
+---
+
+## 1. Customer Acquisition Cost (CAC)
+
+### Fully-Loaded CAC Components
+
+**Formula**: CAC = (Total S&M Costs) ÷ New Customers Acquired
+
+**Sales & Marketing (S&M) Costs to include**:
+- **Marketing spend**: Paid ads (Google, Facebook, LinkedIn), content marketing, SEO tools, events, sponsorships
+- **Sales team compensation**: Base salaries, commissions, bonuses, benefits, taxes
+- **Marketing team compensation**: Marketers, designers, writers, contractors
+- **Sales tools**: CRM (Salesforce, HubSpot), sales engagement (Outreach, SalesLoft), analytics
+- **Marketing tools**: Marketing automation (Marketo, Pardot), analytics (Google Analytics, Mixpanel), advertising platforms
+- **Overhead allocation**: Portion of office space, admin support, IT costs attributable to S&M teams
+- **Agency/consultant fees**: External agencies, freelancers, consultants for marketing or sales
+
+**What NOT to include** (not acquisition costs):
+- Engineering/product development (build the product, not acquire customers)
+- Customer success/support (retain customers, not acquire)
+- General & administrative (not directly related to acquisition)
+
+### Time Period for CAC
+
+**Match costs to revenue period**: If calculating monthly CAC, use monthly S&M costs and monthly new customers.
+
+**Lag effect**: CAC spent today may yield customers next month. Adjust if significant lag (e.g., long sales cycles). Use 1-3 month lag for enterprise sales.
+
+**Example**:
+- Month 1: $50k S&M spend, 100 customers acquired → CAC = $500
+- But if customers from Month 1 spend came from ads run in Month 0, adjust accordingly.
+
+### CAC by Channel
+
+Breaking down CAC by channel reveals which channels are efficient vs. inefficient.
+
+**Method**: Track spend and new customers per channel.
+
+**Example**:
+
+| Channel | S&M Spend | New Customers | CAC | LTV | LTV/CAC |
+|---------|-----------|---------------|-----|-----|---------|
+| Paid Search | $30k | 100 | $300 | $900 | 3.0 |
+| Organic | $10k | 100 | $100 | $1,200 | 12.0 |
+| Referral | $5k | 50 | $100 | $1,500 | 15.0 |
+| Paid Social | $20k | 50 | $400 | $700 | 1.75 |
+
+**Insight**: Organic and Referral have best economics (low CAC, high LTV). Paid Social is unprofitable (LTV/CAC <2:1). Action: Increase organic/referral investment, pause paid social.
+
+### CAC Trends Over Time
+
+**Monitor CAC trends**: Is CAC increasing or decreasing over time?
+
+**Causes of rising CAC**:
+- Market saturation (exhausted easy channels)
+- Increased competition (competitors bidding up ad costs)
+- Product-market fit weakening (harder to acquire customers)
+- Inefficient spend (poor targeting, low conversion rates)
+
+**Causes of falling CAC**:
+- Improved conversion rates (better landing pages, messaging)
+- Brand awareness (more direct/organic traffic)
+- Product-led growth (virality, word-of-mouth)
+- Channel optimization (focusing on best-performing channels)
+
+---
+
+## 2. Lifetime Value (LTV)
+
+### LTV Calculation Methods
+
+**Method 1: Simple LTV (Subscription)**
+
+```
+LTV = ARPU × Gross Margin % ÷ Monthly Churn Rate
+```
+
+**When to use**: Early-stage SaaS, limited data, need quick estimate.
+
+**Example**:
+- ARPU = $50/month
+- Gross Margin = 80%
+- Monthly Churn = 5%
+- LTV = $50 × 80% ÷ 0.05 = $50 × 80% × 20 months = $800
+
+**Method 2: Cohort-Based LTV (More Accurate)**
+
+Track actual retention by cohort, sum revenue over observed periods.
+
+```
+LTV = ARPU × Gross Margin × Σ(Retention at month i)
+```
+
+**Example Cohort** (acquired Jan 2024):
+
+| Month | Retention % | Revenue (ARPU × Retention) | Cumulative |
+|-------|-------------|----------------------------|------------|
+| 0 | 100% | $50 × 1.0 = $50 | $50 |
+| 1 | 95% | $50 × 0.95 = $47.50 | $97.50 |
+| 2 | 88% | $50 × 0.88 = $44 | $141.50 |
+| 3 | 80% | $50 × 0.80 = $40 | $181.50 |
+| 6 | 60% | $50 × 0.60 = $30 | ~$280 |
+| 12 | 40% | $50 × 0.40 = $20 | ~$450 |
+
+LTV = $450 × 80% gross margin = **$360**
+
+Note: This is more conservative than simple LTV ($800) because early churn is higher than average.
+
+**Method 3: Predictive LTV (Machine Learning)**
+
+Use historical data to predict future retention and spending patterns. Advanced approach for companies with large datasets.
+
+**Inputs**: Customer attributes (demographics, behavior, acquisition channel), historical purchase/churn data.
+
+**Model**: Regression, survival analysis, or ML model predicts LTV for each customer segment.
+
+### LTV for Different Business Models
+
+**Transactional (E-commerce)**:
+
+```
+LTV = AOV × Purchase Frequency × Gross Margin % × Customer Lifetime (years)
+```
+
+**Example**:
+- AOV = $100
+- Purchases/year = 3
+- Gross Margin = 50%
+- Lifetime = 2 years
+- LTV = $100 × 3 × 50% × 2 = $300
+
+**Marketplace**:
+
+```
+LTV = GMV per user × Take Rate × Gross Margin % ÷ Churn Rate
+```
+
+**Example** (ride-sharing):
+- Monthly GMV per rider = $200 (total rides)
+- Take Rate = 25%
+- Gross Margin = 80% (after payment processing)
+- Monthly Churn = 10%
+- Lifetime = 1 ÷ 0.10 = 10 months
+- Monthly Revenue = $200 × 25% = $50
+- LTV = $50 × 10 months × 80% = $400
+
+**Freemium**:
+
+```
+Blended LTV = (Free-to-Paid Conversion % × Paid User LTV) - (Free User Costs × Avg Free User Lifetime)
+```
+
+**Example**:
+- 100 free users, 5% convert to paid
+- Paid LTV = $1,000
+- Free user cost = $2/month (hosting), avg lifetime 6 months
+- Blended LTV = (0.05 × $1,000) - ($2 × 6) = $50 - $12 = $38 per free user
+
+### Improving LTV
+
+**Levers to increase LTV**:
+
+1. **Reduce churn**: Improve onboarding, product engagement, customer success. 1% churn reduction → 10-25% LTV increase.
+2. **Increase ARPU**: Upsells, cross-sells, premium tiers, usage-based pricing.
+3. **Improve gross margin**: Reduce COGS, optimize infrastructure, negotiate better rates.
+4. **Extend lifetime**: Long-term contracts, annual billing (locks in customers).
+
+**Example impact** (SaaS):
+- Current: ARPU $50, Churn 5%, Margin 80% → LTV = $800
+- Reduce churn to 4%: LTV = $50 × 80% ÷ 0.04 = $1,000 (+25%)
+- Increase ARPU to $60: LTV = $60 × 80% ÷ 0.05 = $960 (+20%)
+- Both: LTV = $60 × 80% ÷ 0.04 = $1,200 (+50%)
+
+---
+
+## 3. Contribution Margin Analysis
+
+### Contribution Margin Formula
+
+```
+Contribution Margin = Revenue - Variable Costs
+Contribution Margin % = (Revenue - Variable Costs) ÷ Revenue
+```
+
+**Variable costs** (scale with each unit):
+- COGS (cost of goods sold)
+- Hosting/infrastructure per user
+- Payment processing fees (2-3% of revenue)
+- Customer support (per-customer time)
+- Shipping/fulfillment
+- Transaction-specific costs
+
+**Fixed costs** (do NOT include):
+- Engineering salaries (build product once)
+- Rent, utilities
+- Admin, HR, finance teams
+
+### Contribution Margin by Business Model
+
+**SaaS**:
+- Revenue: $100/month subscription
+- Variable costs: $15 hosting + $3 payment fees = $18
+- Contribution Margin: $100 - $18 = $82
+- Margin %: 82%
+
+**E-commerce**:
+- Revenue: $80 product sale
+- Variable costs: $30 COGS + $5 shipping + $2.40 payment fees = $37.40
+- Contribution Margin: $80 - $37.40 = $42.60
+- Margin %: 53%
+
+**Marketplace**:
+- GMV: $200 transaction
+- Take Rate: 20% → Revenue = $40
+- Variable costs: $2 payment fees + $3 support = $5
+- Contribution Margin: $40 - $5 = $35
+- Margin %: 87.5% (of platform revenue)
+
+### Improving Contribution Margin
+
+**Levers**:
+1. **Increase prices**: Directly increases revenue per unit.
+2. **Reduce COGS**: Negotiate supplier costs, economies of scale, vertical integration.
+3. **Optimize infrastructure**: Right-size hosting, use cheaper providers, optimize usage.
+4. **Automate support**: Self-service, chatbots, knowledge base reduce manual support time.
+5. **Negotiate fees**: Lower payment processing rates (volume discounts), reduce transaction costs.
+
+**Example** (E-commerce):
+- Current: Revenue $80, COGS $30, Margin 53%
+- Negotiate COGS to $25: Margin = ($80 - $32.40) / $80 = 59.5% (+6.5pp)
+- Increase price to $90: Margin = ($90 - $37.65) / $90 = 58% (+5pp)
+- Both: Margin = ($90 - $32.65) / $90 = 63.7% (+10.7pp)
+
+---
+
+## 4. Cohort Analysis
+
+### Why Cohort Analysis Matters
+
+**Problem with averages**: Blending all customers hides important trends. Early customers may have different behavior than recent customers.
+
+**Cohort analysis**: Track customers grouped by acquisition period (month, quarter) to see how metrics evolve.
+
+**Benefits**:
+- Detect improving/worsening trends
+- Compare channels/segments
+- Forecast future LTV based on observed behavior
+
+### Building a Retention Cohort Table
+
+**Structure**: Rows = cohorts (acquisition month), Columns = months since acquisition.
+
+**Example**:
+
+| Cohort | M0 | M1 | M2 | M3 | M6 | M12 |
+|--------|----|----|----|----|----|----|
+| Jan 2024 | 100% | 92% | 84% | 78% | 62% | 42% |
+| Feb 2024 | 100% | 90% | 81% | 75% | 60% | - |
+| Mar 2024 | 100% | 93% | 86% | 80% | 65% | - |
+| Apr 2024 | 100% | 91% | 83% | 77% | - | - |
+
+**Insights**:
+- **Improving retention**: Mar cohort (93% M1 retention) > Jan cohort (92%). Product improvements working.
+- **Stable long-term retention**: ~60% at M6 across cohorts. Predictable LTV.
+
+### Calculating LTV from Cohorts
+
+**Method**: Sum revenue at each time period, weighted by retention.
+
+**Example** (Jan 2024 cohort, ARPU $50, Margin 80%):
+
+LTV = $50 × 80% × (1.0 + 0.92 + 0.84 + 0.78 + ... + 0.42 at M12)
+
+Approximate sum of retention % = ~9.5 months equivalent
+
+LTV = $50 × 80% × 9.5 = **$380**
+
+**More accurate**: Sum all observed months, extrapolate tail based on churn rate stabilization.
+
+### Cohort Analysis by Channel
+
+Compare retention and LTV across acquisition channels.
+
+**Example**:
+
+| Channel | M0 | M1 | M3 | M6 | M12 | LTV |
+|---------|----|----|----|----|-----|-----|
+| Organic | 100% | 95% | 85% | 70% | 55% | $450 |
+| Paid Search | 100% | 88% | 75% | 55% | 35% | $300 |
+| Referral | 100% | 97% | 90% | 75% | 60% | $500 |
+
+**Insight**: Referral has best retention and LTV. Paid Search has worst retention (high early churn). Focus on referral growth.
+
+### Trends to Monitor
+
+1. **Retention curve shape**: Does churn stabilize (flatten) after a few months, or continue accelerating?
+2. **Cohort improvement**: Are newer cohorts retaining better than older cohorts? (Product improvements working)
+3. **Channel differences**: Which channels yield stickiest customers?
+4. **Time to payback**: How long until cumulative revenue (× margin) > CAC?
+
+---
+
+## 5. Interpreting Unit Economics
+
+### LTV/CAC Ratio Benchmarks
+
+| Ratio | Assessment | Recommendation |
+|-------|------------|----------------|
+| <1:1 | **Unsustainable** | Losing money on every customer. Fix or pivot. |
+| 1-2:1 | **Marginal** | Barely profitable. Don't scale yet. |
+| 2-3:1 | **Acceptable** | Unit economics work. Optimize before scaling. |
+| 3-5:1 | **Good** | Can profitably grow. Scale marketing spend. |
+| >5:1 | **Excellent** | Strong economics. Aggressive growth, raise capital. |
+
+**Why 3:1 is the target**:
+- 1× covers CAC
+- 1× covers operating expenses (R&D, G&A, customer success)
+- 1× profit
+
+**Context matters**:
+- **Payback period**: 10:1 LTV/CAC with 24-month payback is worse than 4:1 with 6-month payback (cash strain).
+- **Market size**: Low LTV/CAC acceptable if huge market (can still build large business).
+- **Stage**: Early-stage startups may accept 2-3:1 while finding product-market fit. Growth-stage should target >3:1.
+
+### Payback Period Benchmarks
+
+| Payback | Assessment | Cash Impact |
+|---------|------------|-------------|
+| <6 months | **Excellent** | Can reinvest quickly, fuel rapid growth. |
+| 6-12 months | **Good** | Manageable, standard for SaaS. |
+| 12-18 months | **Acceptable** | Need patient capital, slower growth. |
+| >18 months | **Challenging** | High cash burn, risky. Hard to scale. |
+
+**Why payback matters**: Short payback = fast capital recovery = can reinvest in growth without needing external funding.
+
+**Example**:
+- Company A: LTV/CAC 8:1, Payback 18 months → High cash burn, slow reinvestment despite good ratio.
+- Company B: LTV/CAC 4:1, Payback 6 months → Faster reinvestment, can scale more aggressively.
+
+### Cash Efficiency Metrics
+
+**CAC Payback (SaaS-specific)**:
+
+```
+CAC Payback (months) = S&M Spend ÷ (New ARR × Gross Margin %)
+```
+
+**Example**:
+- Q1 S&M spend: $100k
+- New ARR added: $120k
+- Gross Margin: 80%
+- CAC Payback = $100k ÷ ($120k × 80%) = 1.04 quarters = ~3.1 months
+
+**Sales Efficiency (Magic Number)**:
+
+```
+Sales Efficiency = (New ARR in Quarter) ÷ (S&M Spend in Prior Quarter)
+```
+
+**Benchmarks**:
+- <0.75: Inefficient, unprofitable growth
+- 0.75-1.0: Acceptable
+- >1.0: Efficient, profitable growth
+- >1.5: Highly efficient
+
+**Example**:
+- Q1 S&M spend: $200k
+- Q2 new ARR: $180k
+- Sales Efficiency = $180k / $200k = 0.9 (acceptable)
+
+---
+
+## 6. Advanced Topics
+
+### Net Revenue Retention (NRR)
+
+**Formula**:
+
+```
+NRR = (Starting ARR + Expansion - Contraction - Churn) ÷ Starting ARR
+```
+
+**Components**:
+- **Starting ARR**: Revenue from cohort at start of period
+- **Expansion**: Upsells, cross-sells, usage growth
+- **Contraction**: Downgrades, reduced usage
+- **Churn**: Customers leaving
+
+**Example**:
+- Starting ARR (Jan 2024 cohort): $100k
+- Expansion (upsells): +$25k
+- Contraction (downgrades): -$5k
+- Churn (lost customers): -$10k
+- Ending ARR: $100k + $25k - $5k - $10k = $110k
+- NRR = $110k / $100k = **110%**
+
+**Benchmarks**:
+- <100%: Shrinking revenue from existing customers (bad)
+- 100-110%: Stable, small growth from expansion
+- 110-120%: Good, strong expansion
+- >120%: Excellent, revenue grows even without new customers
+
+**Why NRR matters**: >100% NRR means you can grow revenue without adding new customers. Powerful compounding effect.
+
+### Unit Economics for Different Stages
+
+**Early-stage (finding product-market fit)**:
+- Target: LTV/CAC >2:1
+- Focus: Find repeatable, scalable channels
+- Acceptable: Higher CAC, longer payback while iterating
+
+**Growth-stage (scaling)**:
+- Target: LTV/CAC >3:1, Payback <12 months
+- Focus: Optimize channels, improve retention
+- Need: Efficient growth to justify increasing spend
+
+**Late-stage (mature)**:
+- Target: LTV/CAC >4:1, Payback <6 months, NRR >110%
+- Focus: Profitability, margin expansion
+- Optimize: Every channel, reduce CAC, maximize LTV
+
+### Multi-Product Unit Economics
+
+**Challenge**: Customers may buy multiple products. How to attribute value?
+
+**Approaches**:
+
+1. **Customer-level LTV**: Sum revenue across all products purchased by customer.
+   - LTV = Total revenue from customer × Margin
+
+2. **Product-level LTV**: Track LTV separately per product.
+   - Useful if products have different margins, retention patterns.
+
+3. **Blended LTV**: Weight by product mix.
+   - Blended LTV = (% Product A × LTV_A) + (% Product B × LTV_B) + ...
+
+**Example** (SaaS with two tiers):
+- 70% subscribe to Basic ($50/month, LTV $800)
+- 30% subscribe to Pro ($150/month, LTV $2,400)
+- Blended LTV = (0.7 × $800) + (0.3 × $2,400) = $560 + $720 = $1,280
+
+### Sensitivity Analysis
+
+Test how changes to assumptions impact unit economics.
+
+**Variables to test**:
+- Churn rate (+/- 1-2%)
+- ARPU (+/- 10-20%)
+- CAC (+/- 10-20%)
+- Gross margin (+/- 5-10%)
+
+**Example**:
+- Base case: LTV $1,000, CAC $250, Ratio 4:1
+- Churn increases 5% → 4%: LTV drops to $800, Ratio 3.2:1 (still acceptable)
+- Churn increases 5% → 6%: LTV drops to $667, Ratio 2.7:1 (marginal)
+- CAC increases 20% to $300: Ratio drops to 3.3:1 (still good)
+
+**Insight**: Unit economics are sensitive to churn. Small churn increases significantly hurt LTV.
+
+### Competitive Dynamics
+
+**CAC increases over time** due to:
+- Market saturation (easier customers already acquired)
+- Competition (bidding wars on ads, higher sales/marketing costs)
+- Channel exhaustion (diminishing returns on channels)
+
+**Strategies**:
+1. **Build moats**: Brand, network effects, switching costs reduce reliance on paid acquisition.
+2. **Product-led growth**: Virality, word-of-mouth, organic growth reduce CAC.
+3. **Expand TAM**: Enter new markets, segments to access untapped customers.
+4. **Improve conversion**: Better product, messaging, sales process → more customers from same spend.
+
+**Example** (competitive landscape):
+- Year 1: CAC $200, LTV $1,000, Ratio 5:1
+- Year 3: CAC $350 (competition), LTV $1,200 (retention improvements), Ratio 3.4:1
+- Year 5: CAC $500, LTV $1,500, Ratio 3:1
+
+**Insight**: Even with rising CAC, improving LTV (retention, upsells) maintains healthy ratio.
+
+## Key Takeaways
+
+1. **CAC must be fully-loaded**: Include all S&M costs (salaries, tools, overhead). Break down by channel.
+2. **LTV requires cohort data**: Track retention by cohort, extrapolate conservatively. Don't rely on averages.
+3. **Contribution margin sets ceiling**: Need high margin (>60% SaaS, >40% ecommerce) for viable economics.
+4. **Both ratio and payback matter**: 5:1 ratio with 24-month payback < 3:1 with 6-month payback (cash efficiency).
+5. **Retention > Acquisition**: Small churn improvements have exponential LTV impact. Prioritize retention.
+6. **Channel-level analysis**: Blended metrics hide truth. Analyze CAC/LTV per channel, optimize spend accordingly.
+7. **Update quarterly**: Unit economics evolve with scale, market changes, competition. Re-calculate regularly.
--- a/skills/financial-unit-economics/resources/template.md
+++ b/skills/financial-unit-economics/resources/template.md
@@ -0,0 +1,309 @@
+# Financial Unit Economics Templates
+
+Quick-start templates for calculating CAC, LTV, contribution margin, and cohort analysis.
+
+## Unit Definition Template
+
+**Business model**: [Subscription / Transactional / Marketplace / Freemium / Enterprise]
+
+**Unit of analysis**: [What are you measuring?]
+- Customer (entire relationship)
+- Subscription (per subscription period)
+- Transaction (per purchase)
+- Product SKU (per product sold)
+- User (active user)
+
+**Time period**: [Monthly / Quarterly / Annual cohorts]
+
+**Segments** (if analyzing by segment):
+- [ ] Acquisition channel (paid search, organic, referral, etc.)
+- [ ] Customer type (B2B vs B2C, SMB vs Enterprise)
+- [ ] Geography (US, EU, APAC)
+- [ ] Product tier (Free, Pro, Enterprise)
+
+---
+
+## CAC Calculation Template
+
+**Customer Acquisition Cost (CAC)** = Total acquisition costs ÷ New customers acquired
+
+### Fully-Loaded CAC
+
+**Sales & Marketing Costs** (period: [Month/Quarter/Year])
+
+| Cost Category | Amount | Notes |
+|---------------|--------|-------|
+| **Marketing spend** | $[X] | Paid ads, content marketing, events, tools |
+| **Sales team salaries** | $[X] | Base + commission + benefits |
+| **Sales tools & software** | $[X] | CRM, sales engagement, analytics |
+| **Marketing team salaries** | $[X] | Marketers, designers, contractors |
+| **Overhead allocation** | $[X] | % of office, admin costs attributable to S&M |
+| **Other** | $[X] | [Specify] |
+| **Total S&M Cost** | **$[X]** | Sum of above |
+
+**New customers acquired** (same period): [N]
+
+**CAC = $[Total Cost] ÷ [N customers] = $[CAC per customer]**
+
+### CAC by Channel
+
+Break down CAC by acquisition channel to identify most/least efficient channels.
+
+| Channel | S&M Spend | New Customers | CAC | Notes |
+|---------|-----------|---------------|-----|-------|
+| Paid Search | $[X] | [N] | $[X/N] | [Google Ads, Bing] |
+| Paid Social | $[X] | [N] | $[X/N] | [Facebook, LinkedIn, etc.] |
+| Content/SEO | $[X] | [N] | $[X/N] | [Organic, blog, SEO tools] |
+| Referral | $[X] | [N] | $[X/N] | [Referral program costs] |
+| Direct | $[X] | [N] | $[X/N] | [Type-in, brand awareness] |
+| Other | $[X] | [N] | $[X/N] | [Specify] |
+| **Total** | **$[X]** | **[N]** | **$[Blended CAC]** | Fully-loaded blended CAC |
+
+**Insight**: [Which channels are most/least efficient? Where to increase/decrease spend?]
+
+---
+
+## LTV Calculation Template
+
+**Lifetime Value (LTV)** = Revenue over customer lifetime × Gross margin %
+
+Choose calculation method based on business model:
+
+### LTV (Subscription Model)
+
+```
+LTV = ARPU × Gross Margin % ÷ Monthly Churn Rate
+```
+
+**Inputs**:
+- **ARPU** (Average Revenue Per User): $[X]/month
+- **Gross Margin %**: [X]% (Revenue - COGS) ÷ Revenue
+- **Monthly Churn Rate**: [X]% (customers lost ÷ starting customers)
+
+**Calculation**:
+- **Customer Lifetime** = 1 ÷ Churn Rate = 1 ÷ [X]% = [Y] months
+- **LTV** = $[ARPU] × [Y months] × [Gross Margin %] = **$[LTV]**
+
+### LTV (Transactional Model)
+
+```
+LTV = AOV × Purchase Frequency × Gross Margin % × Customer Lifetime (years)
+```
+
+**Inputs**:
+- **AOV** (Average Order Value): $[X] per transaction
+- **Purchase Frequency**: [Y] purchases/year
+- **Gross Margin %**: [Z]%
+- **Customer Lifetime**: [N] years
+
+**Calculation**:
+- **Annual Revenue per Customer** = $[AOV] × [Frequency] = $[X]/year
+- **LTV** = $[Annual Revenue] × [Lifetime years] × [Gross Margin %] = **$[LTV]**
+
+### LTV (Marketplace / Platform)
+
+```
+LTV = GMV per user × Take Rate × Gross Margin % ÷ Churn Rate
+```
+
+**Inputs**:
+- **GMV per user** (monthly): $[X]
+- **Take Rate**: [Y]% (platform's % of GMV)
+- **Gross Margin %**: [Z]% (after variable costs)
+- **Monthly Churn Rate**: [C]%
+
+**Calculation**:
+- **Monthly Revenue per User** = $[GMV] × [Take Rate] = $[X]/month
+- **Customer Lifetime** = 1 ÷ [Churn] = [Y] months
+- **LTV** = $[Monthly Rev] × [Lifetime] × [Gross Margin %] = **$[LTV]**
+
+### LTV by Cohort (Observed Retention)
+
+More accurate: Use actual retention data from cohorts.
+
+**Example Cohort Retention Table** (% of customers remaining):
+
+| Month | Cohort Jan | Cohort Feb | Cohort Mar | Average |
+|-------|------------|------------|------------|---------|
+| 0 | 100% | 100% | 100% | 100% |
+| 1 | 95% | 94% | 96% | 95% |
+| 2 | 88% | 86% | 89% | 88% |
+| 3 | 80% | 78% | 82% | 80% |
+| 6 | 65% | 62% | - | 64% |
+| 12 | 45% | - | - | 45% |
+
+**LTV Calculation**:
+- Sum: Month 0 revenue + (Month 1 retention × revenue) + (Month 2 retention × revenue) + ...
+- **LTV** = ARPU × Gross Margin × Σ(retention %) = **$[X]**
+
+---
+
+## Contribution Margin Template
+
+**Contribution Margin %** = (Revenue - Variable Costs) ÷ Revenue
+
+### Revenue & Variable Costs
+
+| Item | Per Unit | Notes |
+|------|----------|-------|
+| **Revenue** | $[X] | Subscription fee / Sale price / Transaction value |
+| **Variable Costs:** | | (costs that scale with each unit) |
+| - COGS | $[X] | Product cost, manufacturing |
+| - Hosting / Infrastructure | $[X] | Per-user server costs |
+| - Payment processing | $[X] | Stripe/PayPal fees (~2-3%) |
+| - Support | $[X] | Per-customer support time |
+| - Shipping | $[X] | Fulfillment, delivery |
+| - Other variable | $[X] | [Specify] |
+| **Total Variable Costs** | **$[Y]** | Sum |
+| **Contribution Margin** | **$[X - Y]** | Revenue - Variable Costs |
+| **Contribution Margin %** | **[(X-Y)/X]%** | Margin as % |
+
+**Interpretation**:
+- **High margin (>60%)**: Strong unit economics, room for high CAC
+- **Medium margin (40-60%)**: Acceptable, need disciplined CAC management
+- **Low margin (<40%)**: Challenging, requires very efficient acquisition or high LTV
+
+**Levers to improve margin**:
+- [ ] Increase pricing (improve revenue per unit)
+- [ ] Reduce COGS (negotiate supplier costs, economies of scale)
+- [ ] Optimize infrastructure (reduce hosting costs per user)
+- [ ] Automate support (reduce manual support time)
+- [ ] Negotiate payment fees (lower processing costs)
+
+---
+
+## Cohort Analysis Template
+
+Track retention, LTV, and payback by customer acquisition cohort (month, channel, segment).
+
+### Retention Cohort Table
+
+| Cohort (Month Acquired) | M0 | M1 | M2 | M3 | M6 | M12 | LTV | CAC | LTV/CAC | Payback (months) |
+|-------------------------|----|----|----|----|----|----|-----|-----|---------|------------------|
+| Jan 2024 | 100% | 92% | 84% | 78% | 62% | 42% | $1,200 | $300 | 4.0 | 4.5 |
+| Feb 2024 | 100% | 90% | 81% | 75% | 60% | - | $1,150 | $320 | 3.6 | 5.0 |
+| Mar 2024 | 100% | 93% | 86% | 80% | 65% | - | $1,300 | $280 | 4.6 | 4.0 |
+| Apr 2024 | 100% | 91% | 83% | 77% | - | - | $1,100 | $350 | 3.1 | 5.5 |
+| **Average** | **100%** | **91.5%** | **83.5%** | **77.5%** | **62.3%** | **42%** | **$1,188** | **$313** | **3.8** | **4.8** |
+
+**Insights**:
+- [Are newer cohorts performing better or worse than older cohorts?]
+- [Which cohorts have best/worst retention?]
+- [Is LTV improving over time?]
+- [Is CAC increasing or decreasing?]
+
+### Cohort by Channel
+
+| Channel | # Customers | Avg LTV | Avg CAC | LTV/CAC | 12M Retention | Payback (months) |
+|---------|-------------|---------|---------|---------|---------------|------------------|
+| Paid Search | 500 | $800 | $250 | 3.2 | 35% | 6.0 |
+| Organic | 300 | $1,500 | $150 | 10.0 | 55% | 3.0 |
+| Referral | 200 | $1,800 | $100 | 18.0 | 60% | 2.5 |
+| Paid Social | 400 | $700 | $300 | 2.3 | 30% | 7.0 |
+| **Total** | **1,400** | **$1,050** | **$225** | **4.7** | **42%** | **5.0** |
+
+**Insights**:
+- [Best channels: Referral (high LTV, low CAC, fast payback, high retention)]
+- [Worst channels: Paid Social (low LTV, high CAC, slow payback, low retention)]
+- [Action: Increase referral investment, reduce or pause paid social]
+
+---
+
+## Interpretation Template
+
+### LTV/CAC Ratio Analysis
+
+**Your LTV/CAC**: [X:1]
+
+| Range | Assessment | Action |
+|-------|------------|--------|
+| <1:1 | **Unsustainable**: Losing money on every customer | Stop growth, fix model or pivot |
+| 1:1 - 2:1 | **Marginal**: Barely profitable | Don't scale yet, improve retention or reduce CAC |
+| 2:1 - 3:1 | **Acceptable**: Unit economics work | Optimize before scaling |
+| 3:1 - 5:1 | **Good**: Can profitably grow | Scale marketing spend |
+| >5:1 | **Excellent**: Strong economics | Aggressive scale, raise capital |
+
+**Your assessment**: [Based on ratio above]
+
+### Payback Period Analysis
+
+**Your Payback Period**: [X] months
+
+| Range | Assessment | Cash Impact |
+|-------|------------|-------------|
+| <6 months | **Excellent**: Fast capital recovery | Can reinvest quickly, fuel growth |
+| 6-12 months | **Good**: Reasonable payback | Manageable cash needs |
+| 12-18 months | **Acceptable**: Slower recovery | Need patient capital |
+| >18 months | **Challenging**: Long payback | High cash burn, risky |
+
+**Your assessment**: [Based on payback above]
+
+### Combined Decision Framework
+
+| Your Metrics | Recommendation |
+|--------------|----------------|
+| LTV/CAC: [X:1] | [Assessment from table above] |
+| Payback: [Y] months | [Assessment from table above] |
+| Gross Margin: [Z]% | [Good ≥60% (SaaS) / ≥40% (ecommerce), or needs improvement] |
+| **Overall** | **[Stop / Optimize / Scale / Aggressive Scale]** |
+
+### Recommendations
+
+**Pricing**:
+- [ ] [Increase price to improve margin and LTV]
+- [ ] [Add premium tier for upsell]
+- [ ] [Reduce price to increase conversion]
+- [ ] [No change needed]
+
+**Channels**:
+- [ ] [Increase spend on: [channels with best LTV/CAC]]
+- [ ] [Reduce or pause spend on: [channels with poor LTV/CAC]]
+- [ ] [Test new channels: [suggestions]]
+
+**Retention**:
+- [ ] [Improve onboarding to reduce early churn]
+- [ ] [Add features to increase engagement]
+- [ ] [Customer success program for high-value customers]
+- [ ] [Loyalty/referral program to increase repeat]
+
+**Growth**:
+- [ ] [Scale aggressively: Economics support growth]
+- [ ] [Optimize first: Improve metrics before scaling]
+- [ ] [Pause growth: Fix unit economics]
+
+**Cash & Fundraising**:
+- [ ] [Raise funding to fuel growth (if LTV/CAC >3:1 and payback <12 months)]
+- [ ] [Focus on profitability (if LTV/CAC 2-3:1 and payback 12-18 months)]
+- [ ] [Reduce burn (if LTV/CAC <2:1)]
+
+---
+
+## Quick Example: SaaS Startup
+
+**Unit**: Customer (subscription)
+
+**CAC**: $20k marketing, 100 customers → **$200 CAC**
+
+**LTV**:
+- ARPU: $100/month
+- Gross Margin: 80%
+- Monthly Churn: 5% → Lifetime = 1/0.05 = 20 months
+- **LTV** = $100 × 20 × 80% = **$1,600**
+
+**Metrics**:
+- **LTV/CAC**: $1,600 / $200 = **8:1** ✓ Excellent
+- **Payback**: $200 ÷ ($100 × 80%) = **2.5 months** ✓ Excellent
+- **Gross Margin**: **80%** ✓ Strong
+
+**Recommendation**: **Aggressive scale**. Economics are excellent (8:1 LTV/CAC, 2.5 month payback). Raise capital, increase marketing spend 2-3×, hire sales team, expand to new channels.
+
+---
+
+## Common Mistakes to Avoid
+
+1. **Not using cohort data**: Don't average retention across all time periods. Recent cohorts may behave differently.
+2. **Excluding costs**: Don't forget sales salaries, support, payment fees, refunds.
+3. **Vanity LTV**: Don't project 5-year LTV with 1 month of data. Use observed retention only.
+4. **Ignoring channels**: Don't blend CAC across all channels. Analyze each separately.
+5. **Fixed vs variable costs**: Don't include fixed costs (engineering, rent) in contribution margin. Only variable costs that scale with units.
+6. **Not updating**: Re-calculate quarterly. Unit economics change as you scale, market shifts, competition intensifies.