Business case

Business Case

Turns a proposed investment into the one-page artifact you bring to a funding decision: what it costs to do nothing, the options on the table, an expected-value benefit model, payback and ROI over a stated horizon, the input the verdict hinges on, and a clear fund-or-kill ask. Built for the PM who has to defend a bet to a skeptical exec or finance partner — the model is the argument, not a vibe.

This is the investment-justification skill, and it's deliberately narrow. It justifies one bet against its own cost and the do-nothing line. It does not rank a backlog (that's prioritize), set a price (that's pricing), or choose product direction (that's product-strategy). If you're sequencing five features, you want prioritize; if you're deciding whether this one clears its cost, you're in the right place.

Grounded in: classic cost-benefit analysis (expected value, payback, ROI) + Monetizing Innovation — Ramanujam & Tacke: size the value before you authorize the build. (We borrow only the "quantify value first" discipline, not the willingness-to-pay machinery — that's pricing.) Go deeper (The Product Channel): The Product Channel

When to use this

• Leadership asks "is this worth building?" and you need a defensible yes/no with numbers, not a pitch.
• You're going into planning or a funding review and must justify why this bet earns headcount/budget.
• A big or hard-to-reverse investment (a new product line, a platform rebuild, a major hire) needs a written case before commit.
• Finance or an exec sponsor wants payback, ROI, and the cost of not doing it — the do-nothing baseline.
• You suspect a loud-favorite feature won't actually clear its cost, and you want the model to say so before the team burns a quarter on it.

Before you start (gather these)

• The investment — the one feature/bet being justified, in a sentence. One bet per case.
• The problem & who it hurts — what's broken today and the evidence it's real (tickets, churn reasons, lost deals, a metric).
• Reach — how many users/accounts/events the change can touch per period (a real count, not "lots").
• Value per unit — what one successful outcome is worth: incremental ARR, retained revenue, hours saved × loaded cost, support deflection. The conversion from "it worked" to dollars. Be explicit whether this is per-user or per-account — mixing the two is the most common silent 10x error in these models.
• Cost to build + run — eng/design person-months × loaded cost, plus ongoing infra/support/maintenance per period.
• The decision context — who funds it, the budget/horizon, and what it's competing against.

If 2+ of these are missing or vague, ASK 2-4 sharp questions before modeling — e.g., "How many accounts does this realistically touch per quarter?", "What's one successful outcome worth in dollars — new revenue, retained revenue, or cost saved, and is that per user or per account?", "Roughly how many eng person-months?", "What does doing nothing cost us if we skip this?" If you already hold the inputs, proceed and state every estimate as a labeled assumption so finance can challenge the number, not the narrative.

Process

1. State the bet and the ask up front. One sentence on the investment, one on what you're asking for (fund $X / N person-months, or kill). Lead with the answer; the model is the justification, not the suspense.
2. Quantify the cost of inaction (the do-nothing baseline). Every option is measured against not acting. Put a number on the status quo: revenue lost to churn, deals slipping, support load, opportunity cost. If doing nothing is genuinely fine, the honest case may be to not build — say so.
3. List the options, including do-nothing. A business case with one option is an ultimatum. Show 2-4 real alternatives (build full, build a thin v1, buy/partner, do nothing) so the decision is a choice, not a rubber stamp.
4. Build the benefit model as expected value. Benefit per period = Reach × Adoption rate × Value-per-unit, then apply a confidence/probability-of-success discount to turn a best case into an expected case. Keep each factor an explicit, sourced number, and make the arithmetic reproduce: a reviewer should multiply the factors and land on your total. An unhedged top-line is a tell that no one stress-tested it.
5. Total the cost: build + run. One-time build (person-months × loaded cost, plus any licensing) plus recurring run cost per period (infra, support, maintenance). ROI math that omits run cost is how "profitable" features quietly lose money.
6. Compute payback and ROI on one stated convention. Net benefit/period = expected benefit − run cost (run is now baked into net). Payback = build cost ÷ net benefit/period. ROI over horizon = (cumulative net benefit − build cost) ÷ build cost — do not re-subtract run cost in the ROI line, or you double-count it. State the horizon explicitly; ROI with no horizon is meaningless.
7. Run sensitivity on the load-bearing input. Find the one number the verdict hinges on (usually adoption or value-per-unit) and show conservative / base / optimistic. Name the break-even threshold: how low can that input go before the case flips to kill (payback slips past the horizon)?
8. List risks and assumptions, each with a number attached. Not "adoption is uncertain" — "if adoption lands at 15% instead of 30%, payback doubles to 14 months." Make the risk move the model.
9. Make the call. FUND / FUND A SMALLER VERSION / KILL / REVISIT-WHEN — with the single reason. Then state what evidence would flip it. A business case that can't say "kill" isn't analysis, it's advocacy.

Output template

Fill in every [bracket]. Show the arithmetic — a reviewer should be able to redo your math from the factor tables. Round sensibly and label every estimate. Mark soft estimates with ⚠.

# Business Case: [Investment name]

**Author:** [name] · **Date:** [date] · **Decision owner:** [who funds this] · **Horizon:** [e.g. 12 months]

## TL;DR — the ask
**Recommendation: [FUND / FUND A SMALLER VERSION / KILL / REVISIT WHEN ___].**
[1-2 sentences: what we'd build, what it costs, what it returns, and the payback. The exec should be able to decide from this block alone.]
- **Ask:** [$X / N person-months over N weeks]
- **Expected return:** [$Y net benefit / period] · **Payback:** [N months] · **[Horizon] ROI:** [N%]
- **Confidence:** [High / Med / Low] — load-bearing on [the one assumption].

## Problem & cost of inaction
[What's broken, for whom, with evidence. Then the do-nothing baseline as a number.]
- **Evidence:** [churn reason / lost deals / ticket volume / metric]
- **Cost of doing nothing:** [$X/period or trajectory] — [e.g. "we lose ~$Z/quarter to this; it compounds as ___"]

## Options considered
| Option | What it is | One-time cost | Run cost/period | Expected benefit/period | Verdict |
|---|---|---|---|---|---|
| A — Do nothing | accept the status quo | $0 | $0 | −[cost of inaction] | [baseline] |
| B — [thin v1] | [smallest version that tests the value] | [$ / N pm] | [$] | [$] | [recommended?] |
| C — [build full] | [the complete version] | [$ / N pm] | [$] | [$] | [ ] |
| D — [buy / partner] | [vendor or integration instead of build] | [$] | [$] | [$] | [ ] |

**Chosen:** [Option X] — [one line on why it beats the others, including do-nothing].

## Value model (expected value)
**Benefit / period = Reach × Adoption × Value-per-unit, then × confidence discount.**
[State the unit explicitly: per-account or per-user. State whether benefit is new revenue, retained revenue, or cost saved.]

| Factor | Value | Source / basis |
|---|---|---|
| Reach (units/period) | [N] | [real count — analytics, CRM, market data] |
| Adoption rate | [%] | [comparable feature / benchmark / estimate ⚠] |
| → Successful units/period | [N] | = Reach × Adoption |
| Value per successful unit | [$] | [incremental ARR / retained ACV / hours × loaded cost — state per-unit basis] |
| Gross benefit/period | [$] | = successful units × value-per-unit |
| Confidence discount | [×0.X] | [probability the link holds ⚠] |
| **Expected benefit / period** | **[$Y]** | = gross × discount |

## Cost model
| Cost | Amount | Basis |
|---|---|---|
| Build (one-time) | [$ = N pm × loaded cost] | [eng-sized, not PM-guessed] |
| Run (per period) | [$] | [infra + support + maintenance] |
| **Net benefit / period** | **[$Y − run]** | expected benefit − run cost |

## Payback & ROI
- **Payback:** build cost ÷ net benefit/period = [$build] ÷ [$net] = **[N months]**
- **[Horizon] ROI:** (cumulative net benefit − build cost) ÷ build cost = **[N%]**
  *(run cost is already inside net benefit — do not subtract it again here)*
- **Cumulative net at horizon:** [$]

## Sensitivity — what the verdict hinges on
Load-bearing input: **[the one number]**.

| Scenario | [Load-bearing input] | Net benefit/period | Payback | Verdict |
|---|---|---|---|---|
| Conservative | [low] | [$] | [N mo] | [fund/kill] |
| Base | [mid] | [$] | [N mo] | [fund] |
| Optimistic | [high] | [$] | [N mo] | [fund] |

**Break-even:** the case flips to KILL if [input] falls below **[threshold]** (payback past the [horizon]) — [how plausible that is].

## Risks & assumptions (each with a number)
| # | Risk / assumption | If it's wrong | Mitigation / cheapest test |
|---|---|---|---|
| 1 | [assumption, stated as a number] | [quantified impact on payback/ROI] | [the test that de-risks it] |
| 2 | [assumption] | [impact] | [test] |

## Decision ask
**[FUND / FUND SMALLER / KILL / REVISIT WHEN ___].** [The single most important reason.]
**What would change this call:** [the evidence — a test result, a real adoption number — that flips the verdict.]

Avoid (anti-patterns)

• A value model whose arithmetic doesn't reproduce. If the factor table multiplies to $2.6K but the "Expected benefit" cell says $24K, the whole case is dead on arrival — every reviewer who multiplies the row catches it. Make Reach × Adoption × Value × discount actually equal your headline number.
• Per-user value priced as per-account (or vice-versa). "$4/mo" is a per-user number; an account with 6 users is worth ~$288/yr. Stating value-per-unit on the wrong basis is a silent 10x error that flips fund to kill or back. Label the unit.
• Double-counting run cost in ROI. If net benefit already subtracts run cost, the ROI line uses build cost as the denominator — re-subtracting run there understates ROI and contradicts the payback line. Pick one convention (the template's) and hold it.
• Top-line benefit with no confidence discount. "Reach × value = $4M" with 100% adoption baked in is a fantasy. Discount for the probability it lands, and show the factor.
• Forgetting run cost entirely. A feature with great build-cost ROI can bleed money on infra and support forever. Always model recurring cost, not just the one-time build.
• No do-nothing option. A case with a single option isn't a decision — it's a demand. The status quo is always a real alternative and must be costed.
• PM-invented effort. Engineering sizes the build; a PM who guesses person-months rigs the entire ROI. Get the cost from eng or flag it as an estimate.
• A model that can't say "kill." If every input is tuned until the answer is "fund," you've written advocacy. A real case has a break-even threshold and is willing to cross it.
• Doing prioritize's job. This justifies one bet against its own cost and the do-nothing line — it doesn't rank a list. Comparing five features for sequencing is prioritize.

Quality bar

Before delivering, verify every box:

• A reviewer reading only the TL;DR can make the fund/kill call — it states the ask, return, payback, and confidence.
• The do-nothing baseline is costed as a number, and "do nothing" is a row in the options table.
• There are 2-4 real options, not one.
• The value-model arithmetic reproduces: Reach × Adoption × Value-per-unit × discount equals the stated expected benefit. A reviewer can redo it from the table.
• Value-per-unit states its basis (per-user vs per-account) and its type (new / retained / cost-saved).
• A confidence/probability discount is applied and shown — no bare best-case top-line.
• Both build (one-time) and run (recurring) costs are modeled.
• Payback and ROI use the one stated convention; run cost is not double-counted in ROI; the horizon is explicit.
• The build cost is eng-sized or flagged as a PM estimate.
• Sensitivity covers conservative/base/optimistic on the load-bearing input, with a named break-even threshold.
• Every risk/assumption carries a quantified impact and a cheapest test.
• The recommendation is one of FUND / FUND SMALLER / KILL / REVISIT-WHEN, with a single reason and a stated flip condition — and the case is genuinely capable of landing on KILL.

Tips

• Lead with payback, not ROI. Execs feel "pays for itself in 7 months" faster than "75% ROI." Payback answers "when do I get my money back?" — the first question a funder asks.
• Retained revenue counts as much as new revenue. A feature that stops churn has a real, modelable benefit — don't undersell defensive bets by only counting new ARR.
• One load-bearing input usually decides everything. Find it (almost always adoption or value-per-unit), put your homework there, and run sensitivity on it rather than spreading false precision across ten cells.
• The thin-v1 option is often the winning move. When the value is uncertain, the strongest case is rarely "build the whole thing" — it's "spend a little to learn whether the big benefit is real," then re-decide. Price the learning, not just the feature.