Support headcount & budget worksheet
A fill-in-the-blanks worksheet that turns forecast contact volume into a defensible frontline headcount number and a full support budget — labor loaded correctly, plus tooling, management, QA, and training — so you can walk into a planning meeting with a model instead of a guess.
This worksheet builds a support budget from the bottom up: forecast demand, convert it to the headcount that demand actually requires, cost that headcount honestly, then add everything around it. Fill in the [brackets] with your own figures. The numbers in the worked example are illustrative — do not copy them into your model.
The whole exercise exists to answer one question defensibly: how many people, at what total cost, to hold your service level? If you can't show the chain from volume to dollars, finance will set the number for you.
Step 0 — Inputs
Gather these before you start. Every one is something you can measure from your own helpdesk; guessing here poisons everything downstream.
| Input | Your value | Notes |
|---|---|---|
| Forecast contacts per month | [volume] | Per channel if AHT differs a lot by channel |
| Average handle time (AHT), incl. after-call work | [minutes] | Measured, not aspirational |
| Occupancy target | [%] | Share of on-queue time actually spent handling |
| Shrinkage | [%] | Paid time not available for the queue: breaks, PTO, training, meetings, sick |
| Contracted paid hours per FTE / month | [hours] | ~173 for a 2,080-hour year |
| Fully loaded cost per FTE / year | [$] | See Step 4 |
Voice and other real-time channels need Erlang-C math, not a flat average, to hit a service level — this worksheet is a planning approximation. See the linked staffing pieces before you commit a voice roster.
Step 1 — Volume to handling hours
Handling hours = Contacts x AHT (in hours)
- Convert AHT to hours (
[minutes]÷ 60) - Multiply by monthly
[volume] - Result:
[handling hours]— the raw productive work
Step 2 — Handling hours to on-queue hours
Nobody is productive 100% of the time they're logged in. Occupancy above ~85% leaves no slack for volume spikes and burns people out.
On-queue hours = Handling hours ÷ Occupancy target
- Divide by your occupancy target
- Result:
[on-queue hours]
Step 3 — On-queue hours to FTEs
Shrinkage is the paid time agents aren't available to the queue at all. It is not the inverse of occupancy — it's a separate, larger bucket.
Paid hours needed = On-queue hours ÷ (1 − Shrinkage)
Frontline FTEs = Paid hours needed ÷ Contracted hours per FTE
- Divide on-queue hours by (1 − shrinkage)
- Divide by contracted hours per FTE
- Round up — you can't schedule 0.4 of a person
- Result:
[frontline FTEs]
Step 4 — FTEs to labor cost
Base salary is not what an employee costs. For U.S. private-industry workers, benefits average about 30% of total compensation — wages and salaries were 70.1% of employer costs in December 2025 (BLS ECEC). So a first-pass fully loaded figure is:
Fully loaded ≈ Base salary ÷ 0.70 (≈ 1.43x base, benefits only)
That covers payroll taxes, insurance, paid leave, and retirement — not equipment, software seats, facilities, or the management overhead you'll add in Step 5. If you have no local salary data, the U.S. median wage for customer service representatives was $20.59/hour in May 2024, about $42,800/year (BLS OEWS 43-4051).
- Set base salary per role:
[$] - Apply your benefits load (÷ 0.70, or your actual rate)
- Multiply by
[frontline FTEs] - Frontline labor / year:
[$]
Step 5 — The rest of the budget
Frontline labor is usually 60–80% of a support budget, not all of it. Add every line below or the number will be wrong on day one.
| Line | Your value | How to size it |
|---|---|---|
| Team leads / managers | [$] | ~1 lead per 8–12 reps; loaded the same way |
| QA / training / WFM | [$] | Salaried roles or an allocation |
| Helpdesk + core tooling | [$] | Per-seat x FTEs; see total-cost-of-support-tooling |
| AI / automation platform | [$] | Often usage- or resolution-priced, not per seat |
| Telephony / channel costs | [$] | Per-minute or per-contact |
| Recruiting + onboarding | [$] | Cost per hire x expected hires (attrition-driven) |
| Overhead (facilities, IT, equipment) | [$] | Per-head allocation from finance |
| Total support budget | [$] | Sum of all lines |
Worked example (illustrative — use your own numbers)
- Volume: 8,000 contacts/month; AHT 12 min (0.2 hr) → 1,600 handling hours
- Occupancy 85% → 1,600 ÷ 0.85 = 1,882 on-queue hours
- Shrinkage 30% → 1,882 ÷ 0.70 = 2,689 paid hours
- ÷ 173 contracted hours → 15.5 → round up to 16 frontline FTE
- Base $43,000 ÷ 0.70 ≈ $61,400 loaded; x 16 ≈ $982k frontline labor/year
- Non-labor typically adds 25–65% on top → plan a total well above the labor line.
Sanity checks before you submit
- Does the FTE number hold your target service level in your busiest week, not just the monthly average?
- Did you use measured AHT and shrinkage, or hopeful ones? Optimism here shows up as a blown SLA.
- Is attrition funded? If annual attrition is
[%], you are hiring and onboarding that many replacements every year on top of growth. - Did you model automation's effect on volume (fewer human contacts) separately from its cost (the platform line)? Deflection that doesn't lower staffed volume isn't a saving.
- Is management, QA, and tooling in the number? A budget that's only frontline salaries understates true cost-to-serve.
- Are you rebuilding this from demand each cycle, rather than adding a percentage to last year? Customer service roles are projected to decline ~5% through 2034 as tasks automate (BLS OOH) — the flat-plus-inflation habit will not survive scrutiny.
Sources
- Base pay anchor: BLS Occupational Employment & Wage Statistics, Customer Service Representatives (43-4051) — median $20.59/hr, May 2024.
- Benefits load: BLS Employer Costs for Employee Compensation — private-industry benefits ≈ 30% of total compensation, December 2025.
- Employment trend: BLS Occupational Outlook Handbook, Customer Service Representatives.
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