What this calculator does
Downtime cost is the most-quoted, least-understood number in IT economics. Vendors publish an average — Ponemon's famous $8,851/min from 2016, or ITIC's $300K/hr from 2024 — and everyone treats it as gospel. The problem: those numbers come from data-centre incidents at large enterprises. A 25-person professional services firm losing power for two hours doesn't lose $1,062,000. This calculator scales the methodology down to small and mid-size businesses by computing your customer's actual exposure, then placing the result in the right benchmark band.
The math, in plain English
Two cost streams add together. Productivity loss is your headcount times their fully-loaded hourly rate times a 75% utilization factor (people stay clocked in but can't produce). Revenue loss is your hourly revenue (annual revenue ÷ 2,000 working hours) times a real-time-dependency factor — 90% for e-commerce/SaaS where every minute offline is a lost transaction, 40% for everyone else where most revenue is recoverable. Then we multiply by an industry coefficient (healthcare 2.5×, finance 2.2×, retail 0.9×, etc.) that captures opportunity costs, regulatory exposure, and brand damage you can't bill but still pay. Finally we add a recovery cost per incident — data restoration, overtime, SLA credits — that scales linearly, not by duration.
Why dealers care
UPS sales close on one of two arguments: compliance (you need to keep the lights on because the regulator says so) or business case (you save money by not going dark). Most dealers lead with feature lists — kVA, runtime, online vs line-interactive — and then wonder why deals stall in procurement. The business case is harder to make from spec sheets. A number like “your downtime currently costs you $32,400 per incident” reframes the conversation: the UPS isn't a $12,000 capital outlay, it's a $20,400 net positive per incident avoided. Print this calculator's output and clip it to the front of your quote.
Caveats and limits
The industry multipliers in this calculator are Power Stack heuristics aligned with the broad bands reported by Ponemon, ITIC, and Uptime Institute — they're not derived from a single peer-reviewed paper. The productivity utilization factor of 75% comes from generic knowledge-worker studies; if your customer's workforce is mostly machine operators on a production line, the real figure is closer to 95%. The 90% / 40% real-time revenue split is also a heuristic. For high-stakes decisions, ask the customer's finance team to validate the underlying inputs (revenue, headcount, outage frequency) before relying on the output number.