financial-analyst

name	financial-analyst
description	Build financial models from natural language with sensitivity analysis. Use when: feature ROI, business case, revenue projection, pricing analysis, TAM SAM SOM, unit economics, NPV, payback period, sensitivity analysis, ship or no ship.

Financial Analyst

Build rigorous financial models from natural language inputs. Fills gaps with SaaS benchmarks, runs sensitivity analysis, and produces ship/no-ship/de-risk recommendations.

Output

Save to outputs/financial-[topic]-[YYYY-MM-DD].md

When to Use

• Building a business case for a new feature
• ROI analysis for build vs. buy decisions
• TAM/SAM/SOM market sizing
• Pricing change impact modeling
• Any situation where you need numbers to justify a decision

What You'll Get

Output	Description
Assumptions Table	Every input labeled by source (PM Input, SaaS Benchmark, Estimated)
Key Metrics Dashboard	Visual metric cards with color-coded status
Full Model	Unit economics, revenue projections, NPV, payback period
Sensitivity Matrix	Multi-variable sensitivity showing break-even boundaries
Decision Framework	Ship / Do Not Ship / De-risk with specific conditions

Process

Step 1: Describe the Feature or Initiative

I'll ask:

"What are you modeling? Describe the feature, initiative, or pricing change. Include any numbers you have — costs, expected users, pricing, timeline. I'll fill gaps with SaaS benchmarks."

Step 2: Build Assumptions Table

Every model input gets a source label:

• 📌 PM Input — You provided this number
• 📊 SaaS Benchmark — Industry standard (sourced and cited)
• 🔮 Estimated — My best estimate (flagged for validation)

| Assumption | Value | Source | Confidence |
|---|---|---|---|
| Development cost | $180,000 | PM Input | High |
| Monthly active users (Year 1) | 2,400 | SaaS Benchmark (avg adoption 12%) | Medium |
| Conversion rate to paid | 5% | SaaS Benchmark (B2B freemium avg) | Medium |
| Average revenue per user | $45/mo | PM Input | High |
| Churn rate | 4%/mo | Estimated from category avg | Low |

Step 3: Build the Model

Core calculations:

• Unit Economics: CAC, LTV, LTV/CAC ratio, payback period
• Revenue Projections: Monthly and annual with growth assumptions
• Cost Structure: Development, infrastructure, support, opportunity cost
• NPV Analysis: 3-year net present value at standard discount rate
• Break-even Analysis: When does cumulative revenue exceed cumulative cost?

Step 4: Sensitivity Analysis

I'll vary the 2-3 most uncertain assumptions and show how the model changes:

| Conversion Rate → | 3% | 5% (base) | 7% | 10% |
|---|---|---|---|---|
| Monthly Revenue | $3,240 | $5,400 | $7,560 | $10,800 |
| Annual Revenue | $38,880 | $64,800 | $90,720 | $129,600 |
| Payback Period | 55 months | 33 months | 24 months | 17 months |
| NPV (3yr) | -$42,000 | $68,400 | $178,800 | $356,400 |

Step 5: Decision Framework

Based on the model:

• 🟢 Ship — if base case NPV > 0 AND payback < 18 months AND LTV/CAC > 3
• 🔴 Do Not Ship — if even optimistic case doesn't break even in 24 months
• 🟡 De-risk — if base case is marginal; specify what assumptions to validate first

Demo Scenario: Feature ROI Analysis

Input:

"We want to build an AI assistant feature for our project management SaaS. It'll cost about $200K to build (2 engineers × 3 months + infra). We have 20,000 MAU, pricing is $49/user/month. We think it could increase conversion from free to paid by 2 percentage points and reduce churn by 0.5%."

Sample Key Metrics:

Metric	Value	Status
Development Cost	$200,000	📌 PM Input
Incremental Annual Revenue	$235,200	🟢 Strong
Payback Period	10.2 months	🟢 Under 12 months
3-Year NPV	$412,000	🟢 Positive
LTV/CAC Improvement	3.2x → 4.1x	🟢 Healthy
Break-even Users	340 paid conversions	🟡 Achievable but monitor

Sample Decision:

🟢 SHIP — Base case is strong (10-month payback, $412K NPV). Even at 50% of projected conversion lift, payback stays under 18 months. De-risk by: running a 30-day beta with 500 users to validate the conversion lift assumption before full rollout.

Tips

1. Share what you know — Even rough numbers help. I'll benchmark the rest.
2. Flag your biggest uncertainty — I'll stress-test that variable first
3. Include opportunity cost — "These 2 engineers could be working on X instead"
4. Ask for scenarios — "What if pricing is $29 instead of $49?"

Framework Reference

SaaS Benchmarks Used:

• B2B freemium conversion: 2-5% (OpenView Partners)
• Monthly churn: 3-7% for SMB, 1-2% for enterprise (ProfitWell)
• LTV/CAC ratio target: 3:1+ (Bessemer)
• CAC payback target: <18 months (SaaS Capital)
• Discount rate for NPV: 10-15% (standard SaaS)

⚠️ All benchmarks are industry averages. Your actual performance depends on product, market, and execution. Always validate with your own data.