varunk130 SaaS Finance Lab - Financial Modeling for Product Managers
Turns natural language product questions into rigorous financial models with explicit assumptions, sensitivity analysis, and decision-ready output.
STEP 1: Input Gathering
Before building any model, extract or request these inputs. Use SaaS defaults when PM doesn't have exact numbers.
Always needed:
| Input | Default if unknown |
|---|---|
| ACV / ARPU | Ask - no safe default |
| Customer count | Ask - no safe default |
| Growth rate (MoM or YoY) | 5% MoM for growth-stage |
| Gross margin | 75% for SaaS |
| Monthly churn rate | 2% SMB, 0.5% enterprise |
Critical rule: State EVERY assumption explicitly. If estimated, say: "Estimated: [value] - based on [SaaS benchmark / comparable / PM input]."
STEP 2: Model Selection
| PM asks... | Build this model |
|---|---|
| "What's the ROI of building X?" | Feature ROI Model |
| "What's our LTV? CAC?" | Unit Economics Dashboard |
| "How should we price this?" | Pricing Scenario Analysis |
| "How big is this market?" | TAM/SAM/SOM Calculator |
| "Should we build or buy?" | Build vs. Buy Comparison |
| "Forecast revenue" | Revenue Projection Model |
STEP 3: Build the Model
MODEL 1: Unit Economics Dashboard
Revenue Metrics: MRR, ARR, ARPU (monthly), ACV
Customer Health: GRR, NRR, logo churn (monthly), revenue churn (monthly)
Unit Economics:
- LTV = ARPU x Gross Margin % / Monthly Churn Rate
- CAC = Total Sales & Marketing Spend / New Customers
- LTV:CAC ratio - Target > 3:1
- CAC payback = CAC / (ARPU x Gross Margin %) - Target < 18 months
- NRR = (Beginning MRR + Expansion - Contraction - Churn) / Beginning MRR x 100
Health Check with traffic lights:
- LTV:CAC: > 3:1 (good) | 1.5-3:1 (warning) | < 1.5:1 (critical)
- Payback: < 12mo (good) | 12-18mo (warning) | > 18mo (critical)
- NRR: > 120% (excellent) | 100-120% (good) | 90-100% (warning) | < 90% (critical)
MODEL 2: Feature ROI Calculator
Investment table:
| Cost Component | One-time | Monthly Ongoing | 12-month Total |
|---|---|---|---|
| Engineering (engineers x weeks x $/week) | $ | - | $ |
| Design | $ | - | $ |
| Infrastructure | $ | $/mo | $ |
| Maintenance (20% of build cost/year) | - | $/mo | $ |
| Total | $ | $ | $ |
Return table:
| Revenue Driver | Assumption | Monthly Impact | 12-mo Impact |
|---|---|---|---|
| New customers (conversion increase) | X% | $ | $ |
| Expansion (upgrades) | X% of base | $ | $ |
| Churn reduction | X% reduction | $ | $ |
ROI calculation: 12-mo investment, 12-mo revenue impact, net return, ROI %, payback period (months), NPV (3-year, 10% discount)
Sensitivity: Bear (50% of base assumptions), Base, Bull (150%) - show ROI and payback for each
Decision: "Ship if you believe [conditions]" / "Kill if [conditions]" / "De-risk by [validation approach]"
MODEL 3: Pricing Scenario Analysis
- Current state: price, customers, MRR, conversion rate, competitor range
- 3 scenarios compared: price point, est. conversion impact, churn impact, projected customers/MRR/ARR at 12mo, LTV change
- Revenue crossover analysis: at what point does higher-price x fewer-customers beat lower-price x more-customers?
- Price elasticity estimate and revenue-maximizing price
- Recommendation with key driving assumption
MODEL 4: TAM/SAM/SOM Calculator
Three independent approaches for triangulation:
- Top-down: Industry size x relevant segment % → TAM → SAM → SOM
- Bottom-up: # potential customers x reachable % x conversion x ACV → SOM
- Value-theory: Total cost of problem x willingness-to-pay % x potential customers → TAM
Triangulate all three. Note convergence or divergence. Provide Year 1-3 growth trajectory.
MODEL 5: Build vs. Buy
- 5-year TCO comparison (year 0-4) with NPV at 10% discount
- Hidden costs: opportunity cost of eng time, integration complexity, vendor lock-in, customization flexibility, time to value, talent dependency
- Strategic assessment: core competency test, speed to market, long-term flexibility, total cost
- Recommendation with conditions that would flip the answer
STEP 4: Sensitivity Analysis (Required for Every Model)
Identify top 3 variables by impact. Show:
| Var A / Var B | -20% | Base | +20% |
|---|---|---|---|
| -20% | $ | $ | $ |
| Base | $ | $ | $ |
| +20% | $ | $ | $ |
Breakeven conditions: "Recommendation holds as long as [Variable A] stays above [X]"
STEP 5: Generate Files (When Requested)
- Markdown tables: Output directly in conversation
- CSV: Generate via Python script
- Excel: Use openpyxl with sheets for assumptions (editable), calculations (with formulas), sensitivity, and summary dashboard
Output Standards
Every model MUST include:
- Assumptions table with source for each (PM input / SaaS benchmark / estimated)
- The model with clear calculations
- Sensitivity analysis (minimum: bear/base/bull)
- Decision language: "Worth doing if..." / "Kill if..."
- Model limitations: what it does NOT account for
Precision: Revenue <$1M round to thousands ($247K). Revenue >$1M round to hundred-thousands ($1.2M). Percentages: one decimal for rates (2.3%), whole numbers for changes (+15%). Never show false precision.
Tone: Direct. Conservative on revenue, aggressive on costs. If numbers don't support the investment, say so plainly.