Financial Modeling

Financial Modeling

Core principle: Decompose the decision into revenue drivers, cost drivers, and timing — then stress-test the assumptions that swing the outcome. The model isn't the answer; the sensitivity analysis is.

When to Use This Skill

• Evaluating whether to invest in a project, product, or initiative
• A pricing decision needs analytical backing
• "Is this profitable?" or "what's the ROI?" without a structured model
• A decision-synthesis analysis needs financial criteria and scores
• A fermi-estimation output needs refining into a rigorous model
• Comparing options with different cost structures or revenue timelines

Core Methodology

Step 1: Frame the Financial Question

• What's the decision? (invest/don't, A vs. B, price X vs. Y)
• Time horizon? (match to decision's natural lifecycle)
• Who bears costs, who captures value?
• Discount rate? (cost of capital, opportunity cost, or stated hurdle rate)

If inputs are rough, accept them from fermi-estimation via Contract M and flag what needs validation.

Step 2: Map Unit Economics

Decompose to the smallest repeatable economic unit — per customer, transaction, unit sold, API call.

• Revenue per unit: price × volume, or recurring revenue × retention
• Cost per unit: direct (COGS, marginal) + allocated indirect
• Contribution margin: revenue − cost per unit
• Break-even volume: fixed costs / contribution margin

If clean unit economics don't exist (e.g., platform with network effects), identify the value driver (users, transactions, data volume) and model around it.

Step 3: Build the Cost-Benefit Table

Enumerate explicitly. Categorize each:

• Direct costs: engineering time, infrastructure, licensing, materials
• Indirect costs: opportunity cost, coordination overhead, technical debt
• Direct benefits: revenue, cost savings, efficiency gains
• Indirect benefits: option value, learning, strategic positioning

Discount future to present using Step 1's rate. Compute:

• NPV: sum of discounted net cash flows
• IRR: discount rate at which NPV = 0
• Payback period: when cumulative net cash flow turns positive

Step 4: Model Scenarios

Three cases by varying key assumptions:

• Base: most likely
• Upside: optimistic but plausible (not best-case fantasy)
• Downside: pessimistic but plausible (not worst-case catastrophe)

Recompute NPV, payback, contribution margin per scenario. Wide upside-downside spread = high uncertainty, lower confidence.

Step 5: Run Sensitivity Analysis

Identify which assumption, if wrong, changes the decision.

• Vary each key assumption ±25% and ±50%
• Record the largest NPV swing
• That's the key financial risk — deserves the most validation effort

If two assumptions interact (e.g., price and volume), model the interaction explicitly.

Step 6: Produce Financial Criteria for Decision-Synthesis

Feeding decision-synthesis via Contract G, translate findings into criteria:

• Convert thresholds to must-have or want-to-have ("NPV > $0" must-have; "payback < 18 months" want-to-have)
• Score each option 1-5 on financial performance
• Flag key financial risk and scenario spread as confidence modifiers
• Suggest weights based on how financially dominant the decision is

Output Format

🎯 Decision Under Evaluation

• Decision: [what's being decided]
• Time horizon: [N years/months]
• Discount rate: [X% — with justification]

📋 Key Financial Assumptions

📊 Unit Economics

• Revenue per unit: [value]
• Cost per unit: [value]
• Contribution margin: [value] ([%])
• Break-even volume: [value]

⚖️ Cost-Benefit Summary

• IRR: [value]%
• Payback period: [N months/years]

📊 Scenario Analysis

⚠️ Sensitivity Analysis

• Key financial risk: [the assumption that, if wrong, changes the answer]

🏆 Recommendation

• Financial verdict: [invest / don't / conditional on validating X]
• Confidence: H/M/L — informed by scenario spread and sensitivity
• Decision-synthesis criteria (Contract G):
  • ["NPV > $X over 3 years"] — type: must/want, weight: [N]
  • ["Payback < 18 months"] — type: must/want, weight: [N]
• Financial scores per option (if comparing):
  • Option A: NPV = $X, payback = Y months, score = [1-5]
  • Option B: NPV = $X, payback = Y months, score = [1-5]

Common Traps

False precision: Reporting NPV to the dollar when inputs are order-of-magnitude. Match output precision to input precision.

Ignoring opportunity cost: Comparing investment to zero instead of next-best use of the same resources. The baseline is "do the next-best thing," not "do nothing."

Hockey stick projections: Revenue grows exponentially, costs stay flat. Challenge any model where margins improve dramatically without a structural reason.

Sunk cost inclusion: Already-spent costs in forward-looking analysis. Only future cash flows matter.

Single-scenario thinking: Only the base case. Without upside/downside, stakeholders can't assess risk.

Trusting LLM arithmetic: An LLM applying this skill structures the model correctly but may compute NPV/IRR/sensitivity inaccurately. Verify in a spreadsheet. Use this skill for structure, assumptions, and logic — not as a real financial model replacement.

Thinking Triggers

• "What are the unit economics at the atomic level?"
• "Which single assumption, if wrong by 2x, flips this decision?"
• "What's the opportunity cost — what else could these resources do?"
• "Does the upside-downside spread change my confidence?"
• "Am I modeling what I hope, or what's most likely?"