Five Revenue per Head Mistakes
The errors that make every hire look justified on paper.
RPH math approves too many hires when these five inputs are wrong.
Hiring managers who love a candidate find the inputs that justify the role. The five errors below are how RPH math gets talked into approving hires the business can't support.
Quick answer
RPH math approves too many hires when these five inputs are wrong.
Key points
- ▸ Optimistic revenue projections. "This hire will generate $200k of new revenue" is aspirational. Use realistic numbers, not sales-pitch numbers.
- ▸ Zero management tax. Every hire takes 15-20% of someone's management time. Add it — it's the bloat that compounds over years.
- ▸ Skipping ramp-up penalty. New hires produce ~50% in months 1-3, ~70% in months 4-6. Full productivity by month 6-9 is typical. Zero ramp makes RPH look better than reality.
- ▸ Using only base salary. Loaded cost (base × 1.35-1.55) is what leaves the bank. Benefits, payroll tax, equipment, workspace, training.
- ▸ Ignoring existing unused capacity. If current team is at 65% utilization, the real question is "can we redistribute?" not "can we hire?". Run RPH after redistribution.
- ▸ Not modeling departure risk. If the new hire leaves at month 9, the rest of the model collapses. Factor attrition risk into year-one revenue contribution.
Examples
- The optimistic-revenue errorProjection: hire enables $300k new revenue. Reality: $120k realized. RPH math approved the role based on 2.5× too-optimistic number. Now team has bloat.
- The zero-ramp errorModeled $110k loaded cost as full productivity month 1. Reality: first 3 months at 50% = $41k of the $110k was unproductive. Projected RPH was 15% too high.
- The base-salary errorSalary $95k. Treated as total cost. Real loaded: $95k × 1.45 = $138k + $23k management tax = $161k. Projected RPH overstated by 40%.
When to use which tool
- Revenue per HeadUse loaded cost (1.45× base), non-zero management tax, and 50% ramp for 3 months.Estimate whether the next hire raises or lowers revenue per employee after management time and ramp-up.
- Tech Debt InterestSometimes the right answer isn't "hire" but "fix the debt slowing the existing team down."Quantify the compounding hours to fix a shortcut as the codebase grows on top of it. Maintenance heatmap.
Related
- Revenue per HeadEstimate whether the next hire raises or lowers revenue per employee after management time and ramp-up.
- Tech Debt InterestQuantify the compounding hours to fix a shortcut as the codebase grows on top of it. Maintenance heatmap.
- What Revenue per Head CalculatesWhether your next hire raises profit-per-employee or quietly adds bloat.
- When to Run Revenue per HeadFive hiring moments where the RPH delta changes the answer.
Frequently asked questions
› How do I know realistic revenue projection? How-to
Look at comparable past hires. What revenue did they actually generate in year 1? That's the baseline, not the hiring manager's pitch.
› What if the hire is for a new business line with no history?
Use external benchmarks and discount heavily. First-hire-in-a-new-area productivity is usually 50-70% of steady-state because systems and pipelines don't exist yet.
› How should I use a decision framework in real life? How-to
Use a decision framework to expose the tradeoff, not to outsource the decision. Write down the inputs, compare the output with your constraints, then ask what would change the answer. The strongest use is scenario testing: base case, conservative case, and failure case.
› Is this financial, legal, or tax advice? Trust & accuracy
No, this is not legal, financial, tax, medical, or professional advice unless the page explicitly says that use case is supported. It organizes assumptions so you can inspect them. Verify high-stakes choices with qualified people who can review facts, contracts, regulations, and downside risk.
› What assumption matters most in a decision model? Edge case
The most important assumption is usually the one you are least certain about and most emotionally attached to. Change that input first. If the recommendation flips after a small change, the decision is fragile and needs more evidence before you treat the model as useful.