business

Financial Model

Build financial models for business cases — with revenue projections, cost structures, unit economics, DCF analysis, and scenario modeling that make assumptions explicit and outcomes testable.

financemodelingDCFunit-economicsprojectionsbusiness-case

Works well with agents

CTO Advisor AgentFinancial Analyst AgentPricing Strategist Agent

Works well with skills

Metrics FrameworkPRD Writing
financial-model/
    • saas-expansion-business-case.md5.2 KB
  • SKILL.md6.5 KB
SKILL.md
Markdown
1 
2# Financial Model
3 
4## Before you start
5 
6Gather the following from the user. If anything is missing, ask before proceeding:
7 
81. **What is the business case?** (New product launch, expansion, investment decision, fundraising)
92. **What is the time horizon?** (12 months, 3 years, 5 years)
103. **What is the revenue model?** (Subscription, transactional, marketplace, usage-based, hybrid)
114. **What are the key cost drivers?** (Headcount, infrastructure, CAC, COGS)
125. **What assumptions exist?** (Growth rates, conversion rates, churn, pricing, market size)
136. **Who is the audience?** (Board, investors, internal leadership, lending institution)
14 
15If the user says "just give me a spreadsheet," push back: a model without documented assumptions is a fiction generator. Every number must trace to an assumption the reader can challenge.
16 
17## Financial model template
18 
19### 1. Assumptions Table
20 
21List every assumption explicitly. Each must have a source and confidence level.
22 
23```
24| Assumption | Value | Source | Confidence |
25|--------------------------|-----------|------------------------------|------------|
26| Monthly growth rate | 8% | Last 6 months average | High |
27| Gross margin | 72% | Current P&L | High |
28| CAC (blended) | $340 | Marketing spend / new custs | Medium |
29| Monthly churn rate | 3.2% | Cohort analysis (Q3-Q4) | High |
30| Average contract value | $1,200/yr | Sales data | High |
31```
32 
33Rules: assumptions with "Low" confidence must appear in sensitivity analysis. Never bury assumptions inside formulas.
34 
35### 2. Revenue Projections
36 
37Build revenue bottom-up from unit economics, not top-down from market share.
38 
39```
40| Metric | Month 1 | Month 6 | Month 12 | Month 24 | Month 36 |
41|---------------------|----------|----------|----------|----------|----------|
42| New customers | 50 | 85 | 145 | 310 | 525 |
43| Churned customers | 8 | 22 | 48 | 95 | 155 |
44| Active customers | 200 | 420 | 780 | 1,650 | 2,850 |
45| ARPU (monthly) | $100 | $105 | $112 | $120 | $128 |
46| MRR | $20,000 | $44,100 | $87,360 | $198,000 | $364,800 |
47```
48 
49Show the formula for each row. MRR = Active customers x ARPU. Active customers = prior active + new - churned.
50 
51### 3. Cost Structure
52 
53Break costs into fixed and variable. Variable costs must link to a driver.
54 
55```
56| Cost Category | Type | Driver | Month 1 | Month 12 | Month 36 |
57|--------------------|----------|--------------------|----------|----------|----------|
58| Engineering team | Fixed | Headcount plan | $85,000 | $120,000 | $200,000 |
59| Cloud infra | Variable | Per active customer| $4,000 | $15,600 | $57,000 |
60| Sales & marketing | Variable | CAC x new custs | $17,000 | $49,300 | $178,500 |
61| G&A | Fixed | Baseline ops | $15,000 | $22,000 | $35,000 |
62```
63 
64### 4. Unit Economics
65 
66```
67| Metric | Current | Month 12 | Healthy Benchmark |
68|--------------------------------|---------|----------|-------------------|
69| CAC | $340 | $340 | < LTV/3 |
70| LTV (gross margin / churn) | $2,250 | $2,625 | > 3x CAC |
71| LTV:CAC ratio | 6.6x | 7.7x | > 3x |
72| CAC payback (months) | 3.4 | 3.2 | < 12 months |
73| Gross margin | 72% | 74% | > 65% (SaaS) |
74```
75 
76Flag any metric outside healthy benchmarks. If LTV:CAC is below 3x, the business case is weak regardless of revenue projections.
77 
78### 5. Scenario Analysis
79 
80Model three scenarios minimum. Vary the assumptions with lowest confidence.
81 
82```
83| Metric (Month 36) | Bear Case | Base Case | Bull Case |
84|---------------------|------------|------------|------------|
85| Growth rate | 5%/mo | 8%/mo | 12%/mo |
86| Churn rate | 4.5% | 3.2% | 2.0% |
87| Active customers | 1,400 | 2,850 | 5,200 |
88| ARR | $2.15M | $4.38M | $7.98M |
89| Cash position | -$800K | $1.2M | $4.5M |
90```
91 
92Name what changes between scenarios. "Bear case" is not useful — "bear case: growth drops to 5% and churn increases to 4.5%" tells the reader what to watch for.
93 
94### 6. Cash Flow and Runway
95 
96Highlight the month cash reaches zero under bear case. If runway is under 6 months in any scenario, flag it as a critical risk. Include quarterly revenue, costs, net cash flow, cumulative cash balance, and remaining runway in months.
97 
98## Quality checklist
99 
100Before delivering a financial model, verify:
101 
102- [ ] Every number traces to a named assumption with a source
103- [ ] Revenue is built bottom-up from unit economics, not top-down from TAM
104- [ ] Cost structure separates fixed from variable with explicit drivers
105- [ ] Unit economics include LTV, CAC, LTV:CAC ratio, and payback period
106- [ ] At least 3 scenarios are modeled with named assumption changes
107- [ ] Cash flow projection includes runway calculation
108- [ ] The model audience (investors, board, internal) is reflected in the level of detail
109 
110## Common mistakes
111 
112- **Top-down revenue.** "We will capture 1% of a $10B market" is not a model. Build from units: customers x price x retention.
113- **Static assumptions.** Growth rates, churn, and costs change over time. A model with constant 10% monthly growth for 5 years is fantasy.
114- **Ignoring cash timing.** Revenue recognized is not cash received. Annual contracts paid monthly and net-60 invoices create cash gaps the P&L hides.
115- **Single scenario.** One projection is a guess. Three scenarios with named variables show you understand the risk space.
116- **Vanity unit economics.** Calculating LTV with gross revenue instead of gross margin inflates the numbers. Use gross-margin-based LTV.
117- **Missing the "so what."** A model without a recommendation is a data dump. State the decision it supports.
118 

©2026 ai-directory.company

·Privacy·Terms·Cookies·