Methodology
How we calculate the value
Every number in the calculator is either your input or a stated assumption. Here is the full model — derived, not guessed, and backed by sources.
Back to the calculatorThe value has two parts: time saved (①) and additional revenue from faster quoting (②). Part ① is a direct time calculation. Part ② is deliberately conservative and rests on stated, editable assumptions.
① Time & labour saved
A quote takes a base time plus a time per BOM line. nuqo cuts both. We value the time saved at a loaded estimator cost.
t_manual = 45 + 3 × lines · t_nuqo = 15 + 0.4 × lines · saved = (t_manual − t_nuqo) × quotes ÷ 60 × hourly cost
All four time constants and the hourly rate are editable in the calculator.
② Additional revenue from faster quoting
Two effects, both counted as margin contribution rather than gross revenue: a higher win rate from speed, and a conservative capacity effect on existing revenue.
Deriving the win-rate effect
Faster quotes create an edge: buyer research shows responsiveness and an early place on the shortlist matter. We express that as an editable win-share for the fastest quote and derive the uplift from it — the structure is derived, the exact size is an assumption. It needs two inputs: how many vendors quote an RFQ, and how often nuqo makes you the fastest quote.
Formula
uplift = (p₁ − 1/N) × (f − g)
- 1/N
- your win probability with a random response order (N = competing quotes)
- f
- assumed win-share of the fastest quote (editable), decaying toward 1/N on large orders
- g
- your win probability when you are not fastest = (1 − f)/(N − 1)
- p₁
- how often nuqo makes you the fastest quote
Derived uplift by order value
| Order value | Win-rate uplift |
|---|---|
| €5,000 | +6.8 pp |
| €10,000 | +4.8 pp |
| €25,000 | +3 pp |
| €50,000 | +2.2 pp |
| €100,000 | +1.5 pp |
On larger orders more stakeholders decide over longer cycles — speed matters less, so the effect falls.
Large orders: more decision-makers, longer cycles — hence the decline. Grounded in Gartner data on B2B buying groups.
Capacity effect
The time you free up lets you run a few extra — and typically less attractive — quotes. Rather than double-count that time, we conservatively apply a small percentage to existing revenue.
Sources
Ordered by strength. Honest caveat: no independent study measures quote turnaround directly against win rate. Buyer research does show responsiveness matters — and that the effect is smaller on large, committee-driven orders.
Authoritative buyer research
Independent, large-sample studies of B2B buying behaviour.
B2B buying: 6–11 stakeholders, long cycles; responsiveness and a low-friction purchase matter — but rarely decide alone.
Gartner — B2B Buying JourneyBuyers shortlist ~4 of 5 vendors on day one and buy from that list 85–95% of the time — being early and responsive pays off.
6sense — B2B Buyer Experience 2025
Sales effectiveness
What top-performing vendors do differently on proposals.
Top performers bring more opportunities to proposal and win them more often; they deliver strong proposal responses far more frequently (benchmark: 472 sellers & executives).
RAIN Group — Center for Sales ResearchB2B buyers expect speed and accuracy across many channels; a slow experience makes them more likely to switch suppliers.
McKinsey — Five Fundamental Truths (B2B)
Scope & limits
What we use the evidence for — and what we don’t.
Lead-response-time studies (minutes to first contact) establish the speed → conversion link in principle, but are not equivalent to quote turnaround (days). We use them for direction, not magnitude.
Harvard Business Review (2011) — context
All defaults are generic and illustrative — not derived from any customer engagement. Because no study measures quote turnaround directly against win rate, the win-rate effect is a deliberately conservative, editable assumption. Adjust it to your deal mix.