Revenue divided by headcount — a cross-industry productivity metric.

A consulting firm with 10m revenue and 50 staff runs at 200,000 per employee — middle-of-the-road for professional services. Software companies typically post 250,000 to 500,000+. Retail is far lower: 100,000 per employee is common. Industry matters enormously — cross-industry comparisons mislead more than they inform.

How to use it

Enter annual revenue (total, not recurring) and total headcount (include full-time equivalents). For seasonal businesses, use an annual average headcount rather than peak.

Why it's a rough metric

Businesses with different mixes of full-time, part-time, and contract staff aren't directly comparable. Nor are companies with different outsourcing intensity — a firm that outsources ops has artificially high revenue per employee. Use it for trend-tracking within your own business over time, not for absolute judgments.

A worked example

Try the defaults: annual revenue of 10,000,000, headcount of 50. The tool returns 200,000.00. You can adjust any input and the result updates as you type — no submit button, no reload. That's the real power here: seeing how sensitive the output is to one or two assumptions.

What moves the number most

The result responds to Annual Revenue and Headcount (FTE). Not every input has equal weight. Flip one at a time toward extreme values to feel which ones move the needle most for your situation.

The formula behind this

Annual revenue divided by full-time equivalent headcount. Monthly equivalent is annual figure divided by 12. The metric is simple but sensitive to the outsourcing boundary — treat a firm with 80% outsourced production very differently from one with all staff in-house. Everything the calculator does is shown in the formula box below, so you can check the math against your own spreadsheet if you want.

What to do with a low result

A disappointing result is information, not a judgement. Pick the single input that dragged the figure down most and focus the next quarter on that one factor. Breadth-first improvement rarely works; depth-first on the worst input usually does.

What this doesn't capture

The score is a composite of the inputs you provide. Life context — job security, family obligations, health, housing — doesn't appear in the math but shapes the real picture. Use the number as a prompt, not a verdict.