Revenue-at-Risk Calculator Technology

An open seat in tech is bookings you're not closing or a roadmap you're not shipping. What's it costing you?

Technology runs on two engines, and an empty seat stalls one of them. A quota-carrying rep you have not hired is bookings that never land. An engineer you have not hired is a roadmap that slips, and the slip compounds, because in software the revenue behind every head is among the highest in any industry. Either way, an open req is not a saving on payroll. It is output the plan already counted on and is not getting.

At your quotas and your revenue per engineer, what does each unfilled seat cost? This calculator answers it role type by role type.

Built on published technology benchmarks and the two attribution methods finance teams use to value sales and engineering capacity. It is intended for educational purposes. Estimates are directional and do not represent the actual practices or results of any specific company.

$300K-$400Krevenue per employee at scaled software companies¹

$800K-$1Mannual new-business quota carried by an enterprise AE²

40-50 daystypical time to fill an engineering role, longer for senior and specialized talent³

Your numbers

Pick the role type. We'll switch the method and show the math.

Quota-carrying roles are valued by the bookings they would close; engineering and product roles by an output multiplier. Defaults reflect the published benchmarks cited below. Input your own figures for a more accurate estimate.

Unfilled roles (revenue-critical)

Open seats that should be generating output right now in the role type selected below.

Average days a role stays unfilled (time-to-fill)

Default: 45 days. Engineering and specialized tech roles run 40-50 days; senior roles longer. The model converts this to business days for the revenue math.

Role type

Quota-carrying roles use direct bookings attribution (Path B). Engineering and product roles use the output multiplier (Path A).

Average annual quota per rep, ACV ($)

Default: $1M. SaaS AE quotas run a median of about $740K-800K ACV, higher at enterprise and at scale.²

Average fully-loaded salary, monthly ($)

Base, benefits, and overhead. Used for the contract-labor floor and the net-of-comp check, and for the output multiplier on engineering and product roles.

Hires per year (this role type)

Annual hires into the selected role type. Drives the yearly run-rate of revenue at risk.

Scenario

Conservative setting: time-to-fill improves 30% (below hireEZ's measured 50%), and the revenue-conversion rate is held at 70%. Deliberately cautious.

Quota attainment, expected (%): 70

30%95%

For quota-carrying roles, the share of quota a filled seat realistically books. The median rep achieves 75-90% of quota, and only about half of reps fully hit their number, so 70% is deliberately conservative.² This is the single most important honesty control in the model.

Revenue-per-employee multiplier (Path A, engineering)

Converts a role's loaded comp into the revenue it is associated with. Revenue per employee at scaled software firms runs about $300-400K, and 55% of tech leaders value a strong engineer at 3x their comp.¹ Default 2.5x.

Premium contractor cost multiplier (floor)

The cost to bridge an open seat with a contract engineer or agency, relative to a loaded employee. Contractors cost more per hour and capture less leverage. Default 1.5x.

Share of roles covered by contract labor (%)

How often the gap is bridged with premium contract labor rather than left open. Sets the size of the hard-dollar floor. Quota-carrying roles are harder to backfill, so set this lower for sales.

Revenue at risk right now

$0revenue protected per year by filling 30% faster

$0hard costs avoided per year (premium contract labor)

0days saved per hire

$0revenue protected per hire

How the math works

Two attribution paths, and a hard-dollar floor

Technology roles do not all create revenue the same way, so the calculator does not value them the same way. Quota-carrying roles get direct bookings attribution. Engineering and product roles get an output multiplier. And a hard-dollar floor needs no revenue assumptions at all. Every figure is gated by a conversion rate you set.

Path B · Quota-carrying roles

Direct bookings attribution

For AEs and other quota-carriers, the seat's product is bookings, sold against a known number, so the bookings an empty seat fails to close are directly knowable.

annual quota / 260 × attainment × days unfilled

Path A · Engineering & product

Output multiplier

Engineers do not carry a number, so revenue is not attributed to one person. Revenue per employee per working day, scaled to the firm's output.

(monthly salary × 12 × multiplier) / 260 × output rate × days unfilled

Floor · Hard dollars

Premium contractor cost

No revenue assumptions. Just the cost of bridging an open seat with a contract engineer or agency at a premium over a loaded employee.

daily premium cost × days unfilled × share covered by contract labor

Common questions

When we work with technology talent leaders, these are the questions that come up most

An open seat is not always lost revenue. Some roles do not map to revenue directly.

Exactly, and that is why the calculator splits into two paths and gates everything by a conversion rate you control. Quota-carrying roles use direct bookings attribution; engineering and product use an output multiplier; and you set the quota-attainment or revenue-linked-output rate yourself. Open Show the math and you will see the chosen method applied at every step.

Only about half our reps hit quota. Are you assuming every seat books full quota?

No. The honesty control is the average individual attainment, defaulted to 70%. The median rep achieves 75-90% of quota, and only about half of all reps fully hit their number, so 70% is deliberately conservative. Forgone bookings are calculated as quota times attainment, never the full quota. If your attainment runs lower, dial it down.

Forgone bookings are not profit. New bookings carry COGS and commissions.

Correct. For quota-carrying roles the headline is new bookings (top-line ARR) at risk, gross of COGS and commissions, so treat it as top-line and not margin. For engineering and product roles the output figure is shown net of the comp you do not pay while the seat is empty, which is closer to a contribution view. The model never presents bookings as bottom-line profit.

Where do the quota and revenue-per-employee figures come from?

Enterprise AE quotas run about $800K-$1M ACV (the SaaS median is roughly $740-800K, higher at scale),² and revenue per employee at scaled software firms runs about $300-400K (the public-SaaS median is roughly $395K).¹ Strong engineers are widely valued at about 3x their comp. The defaults sit at the conservative end; replace them with your own numbers.

A new hire is not productive on day one, and reps ramp for months.

It strengthens the case rather than weakening it. Ramp means the true revenue gap runs well past the start date, especially for sales, where full productivity can take two or three quarters. This calculator counts only days-to-fill and ignores ramp entirely, which keeps the estimate conservative. Filling sooner shortens both the empty-seat period and the climb to full productivity.

Couldn't we cover engineering gaps with contractors?

Sometimes, and that is precisely the hard-dollar floor the calculator shows as the Method C path. Contract engineers and agencies cost more per hour and capture less leverage than an employee on the team, so bridging the gap is real money even when the work gets done. Quota-carrying roles are much harder to backfill, which is why the bookings exposure is the larger risk there.

How reliable is the 30 to 50% time-to-fill improvement?

The conservative default of 30% sits below hireEZ's measured benchmark of about 50% across customers, and you can set it to whatever you believe is real for your roles and markets. You also do not have to take it on faith: the analytics layer measures the actual reduction in time-to-fill from your own hiring data, so the assumption here can be replaced with your number.

Methodology and sources

References

Revenue per employee (Benchmarkit SaaS 100, 2025; Meritech; SaaS Capital). Public-SaaS median revenue per employee of about $395K in 2025; scaled private-SaaS median about $300K; a broad tech-universe median about $284K. Separately, 55% of technology leaders value a strong engineer at three times their total compensation. benchmarkit.ai
AE quota and quota attainment (The Bridge Group 2024 SaaS AE Metrics Report; ICONIQ State of GTM 2025; RepVue). Median SaaS AE new-business quota of roughly $740-800K ACV, higher at enterprise and at scale; about 50-52% of reps fully hit quota in 2024-2025, down from 66% in 2022; median individual attainment of roughly 75-90% of quota. everstage.com
Time-to-fill for technology roles (Workable; Josh Bersin Company; State of the Software Engineering Jobs Market 2025). Engineering and technology roles typically 40-50 days, with a median around 35-41 days and senior or specialized roles longer (the slowest 10% up to about 82 days). workable.com
U.S. Bureau of Labor Statistics; market compensation data. Loaded technology compensation, used for the output multiplier, the contractor floor, and the net-of-comp check. bls.gov
hireEZ customer benchmarks. About 50% time-to-fill reduction; 60%+ hiring cost reduction; sourcing across 45+ platforms surfacing roughly 7x more qualified talent; about 2.5x more candidates resurfaced from the existing ATS. Clearly labeled as vendor benchmarks, which is why the calculator defaults below them.

Methodology note: This tool provides directional estimates for educational purposes and is not financial advice. It uses two attribution paths: direct bookings attribution for quota-carrying roles and an output multiplier for engineering and product roles. Every figure is gated by a conversion rate set by you (quota attainment or revenue-linked output), because an open seat only costs revenue to the extent its output would have converted. Bookings figures are top-line and gross of COGS and commissions; engineering figures are shown net of the comp not paid during the vacancy. All defaults are published benchmarks drawn from the sources above and are intended to be replaced by your own figures. The model does not represent the actual practices or results of any specific company.

An open seat in tech is bookings you're not closing or a roadmap you're not shipping. What's it costing you?

Pick the role type. We'll switch the method and show the math.

Two attribution paths, and a hard-dollar floor

Direct bookings attribution

Output multiplier

Premium contractor cost

When we work with technology talent leaders, these are the questions that come up most

References

In tech, the cost of a slow hire is measured in quarters, not weeks.