Kaizen events

Business case builder

Business case builder is licensed CC BY 4.0. Attribution: Katafacts (katafacts.com).

1 · What it is

What it is

A business case builder is the document your champion carries into their own budget conversation, without you in the room — the same discipline as the canonical business case, pointed at a deal instead of an internal investment. Cost of the status quo, what the deal actually costs, and a quantified value driver for each benefit, with payback period and 3-year ROI computed from those numbers rather than asserted. Every value driver carries how the number was derived and how confident you actually are in it, the same honesty discipline as a value hypothesis's proof points — a driver with nothing real behind it is named as a gap, not smoothed over to make the total look stronger.

2 · When to use it

When to use it — and when not to

Use it when

The deal is heading into a procurement or finance approval step, and your champion needs real ammunition to defend it internally.
You've already built a value hypothesis and want to turn its proof points into an itemized, dollar-denominated case.
You want to know honestly which value drivers are backed by real evidence and which are still an assumption worth flagging before finance finds it first.

Not when

You don't have real discovery yet — build a value hypothesis first; this tool assumes the buyer's needs and proof points already exist, not that you're inventing them here.
The purchase is small enough that a formal business case is pure overhead — forcing this process onto a simple deal adds friction without adding a real decision-maker who needs it.
You're tempted to round a soft estimate up to look more decisive — a business case that can't survive a skeptical CFO's first question isn't actually ready yet.

3 · How to fill it in

How to fill it in

Cost of inaction: What does staying with the status quo keep costing the buyer, and why does that matter before their next budget cycle closes?
Investment: Describe the deal roughly — license, implementation, services — and a first-pass itemized estimate gets drafted for you to correct with real numbers.
Value drivers: Drafted from your description of the deal — each with how the number was derived and how confident it actually is. Ground these in real discovery or value-hypothesis findings wherever you can.
ROI summary: Computed for you from investment and value drivers — nothing to enter here.
Assumptions: Drafted alongside the value drivers — what has to stay true for the champion's numbers to hold up under questioning.
Narrative: Drafted executive summary — the shape of the case in words, never restating the computed numbers itself.
Next steps: What needs firming up before this case goes to the buyer's own finance team, ranked by impact and effort, owned and dated.

4 · What good looks like

What good looks like

The example below builds Northline Freight's business case directly on top of this catalogue's own value hypothesis for the same deal — rather than presenting the pilot's headline $310K figure as guaranteed value, it applies a named, conservative 25% recovery assumption and states that assumption explicitly. A third driver honestly stays at low confidence: it borrows the exact 'business case never quantified' failure pattern Beacon Analytics' own deal A3 found costing its own pipeline, applied speculatively to Northline's future renewals.

Same example, as a downloadable xlsx workbook.

Download .xlsx

Business case · Northline Freight

Northline Freight — business case for Beacon Analytics

Jordan Ellis, Sales Manager · 2027-02-26

Team: Priya Anand, RevOps · Sponsor: Renee Okafor, VP Sales

The business case Northline Freight's VP RevOps will carry into their own budget conversation to justify a Beacon Analytics subscription over continuing with spreadsheet-based pipeline review.

Cost of inaction

Northline's RevOps team currently finds out about stalled deals at the next QBR — an average of three weeks after a deal has already gone quiet. In the trial cohort, that delay corresponded to $310K in pipeline that could have been flagged and worked two or more weeks earlier. That gap doesn't close on its own; it recurs every quarter Northline keeps reviewing pipeline manually instead of in real time.

Investment

Description	Amount	Timing
Beacon Analytics annual subscription — RevOps tier, 40 seats	$96,000	annual
Implementation and historical data migration	$15,000	one-time

Value drivers

Earlier at-risk deal detection reduces pipeline value lost to late discovery
revenue growth · $77,500/yr · Confidence: medium
Basis: 25% of the $310K at-risk pipeline figure from Northline's own pilot data (the same reference figure named in Northline's value hypothesis) — a conservative recovery assumption, since not every early-flagged deal is actually saveable. The assumption itself is named explicitly below, not buried in this number.
RevOps team time no longer spent manually compiling the weekly pipeline review
productivity · $24,000/yr · Confidence: high
Basis: Northline's RevOps lead estimated 4 hours/week currently spent manually compiling the pipeline review from spreadsheets, at a fully loaded $115/hr, across 50 working weeks.
Reduced risk of a repeat 'business case never quantified' stall on future renewals or expansions
Low confidence
risk reduction · $15,000/yr · Confidence: low
Basis: Directional estimate of expansion revenue at risk if Northline's own future internal budget conversations stall the same way Beacon Analytics' own Q1 deal A3 found happening in its pipeline — not yet validated against an actual renewal cycle.

ROI summary

Payback period

11.4 months

3-year ROI

15.3%

3-year net value

$46,500

Total annual value

$116,500

1st-year investment

$111,000

Low-confidence, not yet proven

Reduced risk of a repeat 'business case never quantified' stall on future renewals or expansions

Assumptions

The 25% pipeline-recovery assumption holds — not every early-flagged deal is actually saveable, and this hasn't been tested against a full year of Northline's own usage yet.
Northline's RevOps lead's 4-hours/week estimate for manual pipeline compilation stays accurate as the team grows.
The expansion-risk-reduction driver is directional, not measured, and should be revisited once Northline has a real renewal cycle under Beacon Analytics.

Narrative

The case pays back in under a year — 11.4 months — and the two well-evidenced drivers, the discounted pipeline-recovery figure and the RevOps time savings, already account for 87% of the total annual value on their own. The expansion-risk-reduction driver is honestly the weakest of the three and a small addition, not what the case depends on: it borrows the same 'business case never quantified' pattern Beacon Analytics' own Q1 deal A3 found costing its own pipeline, applied speculatively to Northline's future renewals, and shouldn't be presented as more than directional until there's a real cycle to measure it against.

Next steps

Confirm the 25% pipeline-recovery assumption directly with Northline's VP RevOps before this case goes to their finance team.
Linked finding: Earlier at-risk deal detection reduces pipeline value lost to late discovery — medium confidence
Impact: high · Effort: low · Owner: Jordan Ellis · Due: 2027-03-03
Get a real hours estimate from the full RevOps team, not just the team lead, before finalizing the productivity driver.
Linked finding: RevOps team time no longer spent manually compiling the weekly pipeline review — high confidence, but based on one person's estimate
Impact: medium · Effort: low · Owner: Priya Anand · Due: 2027-03-01

5 · Common mistakes

Common mistakes

Presenting a pilot or reference figure as guaranteed value instead of applying a real, named assumption to it.
A prospect's own finance team will ask what makes this deal different from the reference case — a number with a stated, defensible assumption behind it survives that question; a borrowed headline figure doesn't.
Padding the case with a low-confidence driver dressed up as certain.
One number a skeptic can disprove gives them permission to doubt every other number in the case, including the strong ones.
Omitting the cost of inaction and only pitching the upside.
Without a real cost to staying put, there's no urgency in the case — the champion needs both halves of the argument, not just the half that flatters your product.

6 · What it connects to

What it connects to

upstream

Value hypothesis
A proof point already quantified in a value hypothesis is exactly what turns into a value driver here, with its basis already half-written.
Discovery call standard work
The compelling event and cost-of-inaction evidence gathered in discovery is what a business case's cost-of-inaction section is built from.

downstream

Deal A3 — win-rate root cause analysis
When a business case doesn't land and the deal stalls anyway, that's exactly what an eventual deal A3's root cause analysis would trace back to.

7 · Where AI helps

Where AI helps

Judgement — stays yours

Deciding whether a drafted value driver's basis would actually survive a skeptical finance team
Deciding which low-confidence driver to firm up before the case goes to the buyer

Analysis — AI helps

Drafting itemized investment and value drivers from a description of the deal
Drafting the narrative from the case actually built

Drudgery — automated

Computing payback period and 3-year ROI from the numbers
Tallying which value drivers are still low-confidence
Exporting to xlsx in the house format

9 · Rate this kata