Business Case Builder

Business Case Builder

When to use

Use whenever an initiative needs a funding decision and the "case" so far is a single optimistic revenue line with no downside scenario, no discount rate, and no stated conditions for success. Also use to stress-test someone else's business case before approving it.

What it does

Builds a driver-based (not top-line-only) economic model: revenue, cost structure, and investment broken into their actual drivers, run through the bundled NPV/IRR calculator, and stress-tested with a real sensitivity grid across revenue and discount-rate assumptions — producing not just a number, but the conditions under which that number holds.

Method

1. Decompose revenue into real drivers, not a single top-line growth assumption: volume × price, or customers × ARPU × retention — whichever actually governs this business. A one-line revenue forecast is not a model, it's a guess with decimals.
2. Decompose cost into COGS (variable, scales with revenue) and opex (largely fixed in the near term) by year, and be explicit about which costs actually scale and which don't — this is where most hand-built cases quietly overstate margin improvement.
3. State the initial investment fully: capex, working capital, and one-time launch costs, not just the visible capex line.
4. Run the bundled calculator (scripts/npv_case.py) with a JSON config of discount rate, initial investment, and per-year revenue/COGS%/opex. It returns annual cash flows, NPV, IRR (via bisection, no external dependencies), and payback period.
5. Generate the sensitivity grid (built into the same script): NPV across a range of revenue swings and discount-rate swings. This is the step that turns a single confident number into an honest range.
6. Read the grid for the breakeven condition: find the combination of revenue shortfall and discount rate at which NPV turns negative — that is the real risk boundary of the case, and it should be stated explicitly, not buried in an appendix.
7. State the conditions required for the case to hold in plain language: "this case requires revenue to reach at least X% of plan by year 2, and holds at discount rates up to Y%" — tied directly to the sensitivity grid, not a vague "assumptions may vary" disclaimer.

Inputs

• Revenue drivers (volume, price, or customers/ARPU/retention) by year
• COGS % and opex by year
• Initial investment (capex + working capital + one-time costs)
• Discount rate (company's cost of capital or hurdle rate)
• Config saved as JSON matching the format documented at the top of scripts/npv_case.py

Output format

Annual cash flow table; NPV, IRR, payback period; the revenue × discount-rate NPV sensitivity grid; a plain-language statement of the conditions required for the case to hold, anchored to where the grid turns negative.

Example

An initiative shows a headline NPV of $3.35M at a 10% discount rate. The sensitivity grid shows NPV stays positive even at -20% revenue and +2pt discount rate — a genuinely robust case. A second initiative shows a similar headline NPV but turns negative at -10% revenue, meaning it only works if the plan is hit almost exactly — a much weaker case that looked identical before the grid was run.

Common pitfalls

• Presenting a single-point NPV with no sensitivity, which hides how fragile the case actually is.
• Modeling revenue as one growth-rate line instead of real volume/price or customer/ARPU drivers.
• Leaving out working capital or one-time costs from the initial investment, overstating the return.
• Choosing a discount rate that flatters the case instead of using the company's actual cost of capital.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: Natan-Mohart
• Source: Natan-Mohart/24-strategy-skills-for-claude
• License: MIT

Install and usage instructions live in the source repository linked above.