Costing Section

Standalone costing module that you can drop into a page. Defaults match the Resume Processing calculator. Edit inputs to recalc the table below.

Market information

United States: ~50k companies (conservative floor: 19.7k; plausible range: 20k–77k).
Europe (EU-27): ~53k companies (large enterprises, ≥250 employees).
Asia: rough order-of-magnitude 60k–140k companies (includes Australia’s 5.2k ≥200-employee firms, plus Japan/Korea/SEA/India/China large-firm cohorts).

Benchmark Ratios (“Resumes per Hire”)

Industry / Function	Average Resumes Reviewed per Hire	Notes
High-volume BPO / Contact Center	15–25	Screening automation & skills filtering common; higher throughput roles.
IT / Software Engineering	50–100	Technical tests & cultural fit add friction; often <2% hire rate.
Data Science / AI Roles	100–150	Complex multi-stage evaluation, limited qualified pool.
Finance / Banking	40–60	Conservative selection; higher compliance & background checks.
Sales / Marketing	30–50	Balance between experience and behavioral alignment.
Executive Search	150–300	Passive candidates dominate; manual curation remains high.
Average across industries	≈ 40–60

Inputs (edit to recalc)

Resumes per client per month (NR)

Human time per resume (minutes) (TPR)

Human cost per hour (USD) (CPR)

Unit Cost — Resume Parsing (USD) (RC)

Unit Cost — Resume Scoring (USD) (RS)

Markup (Selling) % (MP)

Formulas: Human Time = TPR × TotalResumes (minutes); Human Cost = ((TPR/60) × CPR) × TotalResumes; Cost Parse = RC × TotalResumes; Cost Score = RS × TotalResumes; Selling = (1 + MP/100) × Cost; Gross Profit = SellingTotal − CostTotal.

Output

Costing Table by Number of Clients

Clients	Resumes / month	Human time (HH:MM)	Human cost (USD)	Cost — Parsing (USD)	Cost — Scoring (USD)	Selling — Parsing (USD)	Selling — Scoring (USD)	Gross Profit (USD)