You built an ROI calculator. You put it on the pricing page. And your buyers took one look at the suspiciously round "347% ROI!" it produced and mentally filed it next to "as seen on TV".
They're not being cynical. They're being rational.
Two-thirds of your market assumes your maths is marketing. And most vendor calculators earn that assumption honestly: generic black-box formulas, inputs you can't adjust, assumptions you can't see, and an output engineered to flatter the vendor. One-size-fits-all logic applied to a buyer who knows their business is not one-size.
Meanwhile the audience got harder. Purchases now run through committees of 6.8-10+ stakeholders led increasingly by finance. The people reading your number are the people professionally trained to distrust it.
The spreadsheet isn't saving you either
The usual fallback - a bespoke Excel business case - fails from the other direction. Your best AE's beautiful model can't be replicated by the rest of the floor. It looks homemade because it is, and finance teams routinely reject models lacking assumption transparency, citable benchmarks, and any risk analysis. It lives outside the CRM, so nobody learns which value claims actually close deals. Flexible, yes. Scalable and credible, no.
The five components of a number a CFO will accept
Across the research on what survives finance scrutiny, credible business cases share five mandatory parts:
1. Quantified value drivers. Concrete formulas, not adjectives. "Recovers 1,200 analyst hours annually at $94/hour loaded" - never "saves time and improves efficiency." 2. Transparent, editable assumptions. Every input visible, adjustable, and tied to either the customer's own data or a citable third-party benchmark. The moment a buyer can push your assumptions down and *still* see a payback they like, you've won - it's their model now, not yours. 3. Payback and timeline, not just multiple. A huge ROI with no break-even date is dismissed as marketing. Finance thinks in payback months. 4. Risk and sensitivity analysis. Show conservative, baseline and optimistic scenarios. Admitting uncertainty is a trust-building move, not a weakness - it's exactly what Forrester's own TEI methodology does (modelling costs, benefits, flexibility *and risk*), which is why TEI studies (363% ROI for OutSystems, 314% for impact.com, both sub-six-month paybacks) get read instead of binned. 5. Cost of inaction. Price the status quo so "do nothing" stops being free.
Two disciplines the list implies but sellers skip: margin-adjust revenue claims (a CFO reads "$1M new revenue" as ~$300-700k depending on gross margin - do that maths before they do) and haircut your own case studies (if your website says "40% faster", model 24% and say why - conservatism is the most persuasive number in the deck).
Transparency scales. Databricks proved it.
When Databricks replaced static claims with interactive, adjustable business value assessments its reps could build themselves, adoption hit 80% of the global sales force - and win rates in value-assessed deals went from 8% to 55%. Okta did the version of this with real-time value tools and added 30% to global win rates while influencing $140M in pipeline. The pattern is identical: let the buyer touch the assumptions, and the number becomes believable; make it easy for reps, and it actually gets used.
That last clause matters because the industry-wide execution rate is dismal: 89% of enterprises have a value engineering team, 75% bought value tooling - and only 19% of reps consistently use any of it. Credibility isn't just a maths problem; it's a friction problem.