Financial Modeling & Scenario Planning
Build detailed financial projections and scenario models (base, bull, bear cases) showing unit economics, CAC/LTV, runway, and path to profitability with key drivers and sensitivity analysis for fundraising and strategic decisions.
Use This When
Planning, analysis, client strategy sessions, decision support.
Inputs Needed
Business model, goal, constraints, market, competitors, budget, timeline, internal capabilities.
Expected Output
Executive summary, diagnosis, options, risks, recommended path, implementation plan, KPIs.
The Workflow Prompt
You are a business strategist and operator. Objective: Financial Modeling & Scenario Planning Context: Build detailed financial projections and scenario models (base, bull, bear cases) showing unit economics, CAC/LTV, runway, and path to profitability with key drivers and sensitivity analysis for fundraising and strategic decisions. Original task: **Act as a financial analyst specializing in SaaS/[BUSINESS_MODEL] financial modeling for [COMPANY_STAGE] companies.Build a comprehensive financial model for a [PRODUCT_TYPE] business with [INITIAL_ASSUMPTIONS].Create three scenarios:(1) Base Case (most likely outcome)(2) Bull Case (best reasonable outcome)(3) Bear Case (conservative outcome). For each scenario, model: unit economics, customer acquisition costs, lifetime value, churn rates, pricing strategy impact, runway, and path to profitability.I need specific monthly projections for [TIME_PERIOD], including P&L, cash flow analysis, and breakeven analysis. Highlight the key financial metrics that drive profitability. Identify the 3-5 most critical assumptions that would break the model if wrong. Format as: Model Overview → Scenario Comparison Table → Detailed P&L Projections → Key Drivers & Sensitivity Analysis → Recommendations for Financial Optimization. Make it usable for fundraising presentations.** Inputs I may provide: Business model, goal, constraints, market, competitors, budget, timeline, internal capabilities. Operating instructions: - First, restate the objective in one clear sentence. - If critical information is missing, ask up to 5 focused questions. If there is enough information to proceed, make practical assumptions and label them. - Use a Detailed response style. - Be specific to the business, audience, channel, and constraints provided. - Avoid generic AI advice. Give concrete recommendations, examples, templates, copy, or steps I can use. - When current facts, competitors, laws, prices, policies, or market claims matter, use current research and cite sources. - Do not expose hidden chain-of-thought. Provide a concise rationale or decision summary instead. - End with a short QA checklist that helps me verify the output. Required output: Executive summary, diagnosis, options, risks, recommended path, implementation plan, KPIs. Caution: Do not treat output as professional legal, medical, financial, or compliance advice; verify with a qualified expert. Use live web research or source documents before finalizing claims.
QA Follow-Up Checklist
After the AI returns its output, verify against:
- Output is specific to the provided business/context.
- Assumptions are clearly labeled.
- No unsupported claims without source checks.
- Next actions are clear and usable.
Follow-Up Prompt
Now turn the result for 'Financial Modeling & Scenario Planning' into a client-ready version: tighten wording, remove fluff, add missing assumptions, and provide the next 3 actions.
Avoid / Cautions
Do not treat output as professional legal, medical, financial, or compliance advice; verify with a qualified expert. Use live web research or source documents before finalizing claims.
How Different Verticals Use This Workflow
Restaurant & Hospitality
A multi-unit restaurant group raising a $12M growth round builds a 5-year model with monthly granularity in years 1-2 covering 14 new units. Base case shows unit-level payback in 24 months; bull case 18 months; bear case 38 months with two unit closures. Investors approve at base-case terms because the bear case is plausible and the assumptions trace to comp data — closing 25% faster than the previous round.
Retail & E-commerce
A DTC brand at $14M ARR raising a $20M Series B builds a model with monthly granularity covering subscription expansion, two new product launches, and international entry. Bear case shows ARR plateau at $22M; base case $35M; bull case $52M. Investors get comfortable because the build includes channel-level CAC assumptions that match the historical data — leading to a closed round at the upper end of expected valuation.
Professional Services & B2B
A B2B SaaS at $4M ARR fundraising builds a model showing the path to $15M ARR with explicit assumptions on rep ramp, win rate by segment, and NRR. Bear case shows $9M ARR if the SMB segment churns at projected rates; bull case $19M if mid-market expansion lands. Investors invest at the base case because the bear case shows discipline — and the model becomes the operating plan for the next 24 months.
Beauty & Personal Care
A clean beauty brand at $5M ARR seeking a $10M strategic investment builds a model showing wholesale expansion, two product launches, and a planned hair-care category extension. The model includes margin-by-channel splits and a working-capital build for retail expansion. The bear case (35% retail sell-through) shows breakeven; the strategic investor commits because the downside is protected.
Local & Trade Services
A regional contractor preparing for sale to a PE-backed roll-up builds a normalized 3-year financial model showing recurring revenue from maintenance contracts, project revenue stability, and key-person dependencies. The model includes 'what does this business look like under acquirer ownership' adjustments — selling the business at a 1.4x multiple uplift vs. the founder's original ask.
Frequently Asked
What's the right level of detail for a fundraising financial model?
Monthly granularity for the first 24 months, quarterly for years 3-5. Investors want to see your unit economics assumptions, not your line-item OpEx forecast for January 2029. The model should be defensible (every assumption traces to evidence) and editable (a sophisticated investor will want to flex your CAC assumption and watch the model recalculate). If the model breaks when assumptions change, it's not a model — it's a presentation.
What's the most common financial modeling mistake that kills credibility?
Hockey stick growth in year 2-3 with no underlying driver shift to explain it. If you're growing 30% YoY today, a model showing 200% YoY growth in year 2 needs to show what changes — a new channel, a new product, a new geo. Without that, sophisticated investors discount your whole model and assume you don't understand your own business. The fix: explicit growth driver attribution per year.
Should I build base/bull/bear or just a single most-likely case?
Three cases, always, for fundraising. The bear case shows you've thought about what goes wrong; the bull case shows the upside; the base case is what you'll be measured against. Investors expect all three — and they'll use the bear case to size their conviction. A model with only the base case signals naivete. The bear case should be uncomfortable, not aspirational.
When is financial modeling premature?
Pre-revenue or pre-product-market-fit. Modeling 24-month projections when you have 4 customers is fiction — the model gives you confidence in numbers that have no basis. Use a simpler 'unit economics + runway' analysis instead, focused on what it would take to validate the next milestone. Build the full model once you have 6 months of revenue data to calibrate against.