Financial analyst
Unified suite of AI agents for product managers — a 6-agent orchestrated pipeline that turns customer feedback into strategy, PRDs, and stakeholder comms, plus 3 standalone agents for decisions, financial modeling, and audience-tailored communication.
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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.
SKILL.md
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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
- Share what you know — Even rough numbers help. I'll benchmark the rest.
- Flag your biggest uncertainty — I'll stress-test that variable first
- Include opportunity cost — "These 2 engineers could be working on X instead"
- 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.