Financial Analyst

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

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:

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.