Capacity-Based Sales Modeling

Building the number from the bottom up

The pattern sets an early sales target when there is no history to extrapolate from by deriving it from a single rep's physical capacity and the funnel's conversion rates rather than picking a top-down goal. Shuo Wang, describing how the first sales quotas were set at Deel, recounts an explicit calculation: "We did basic calculations on their capacity. You can take about 6 meetings per day, 5 days per week, that is 30 meetings, four weeks per month that is 120 meetings. Conversion to interested is 60%, that is 72 SQLs. Close rate bare minimum 30%, that is around 22 clients. That is purely capacity based."¹

The chain runs from a physical limit to a defensible number. Six meetings a day is a capacity ceiling, roughly forty minutes each with prospecting time around them. Across five days and four weeks that is a hundred and twenty meetings a month. Applying a sixty percent rate of converting a meeting into an interested, self-qualified opportunity yields seventy-two of them, and a thirty percent close rate taken as a floor yields about twenty-two closed logos. Those twenty-two logos become the account executive's starting quota. Every input is a measurable capacity or a rate rather than an aspiration, which is what makes the resulting number one Wang treats as defensible rather than invented.

An engineer's instinct applied to a quota

Wang frames the approach as the same instinct she brings to decisions generally: model a system from its primitives before reaching for more resources. In her account the capacity model is the inverse of setting a goal first and then hiring to hit it. The number is assembled from what one rep can physically do and what the funnel actually converts, so it stays tied to real unit economics rather than to a fundraising-driven headcount plan. She describes the method as the way to get a principled first figure precisely when you cannot yet extrapolate from past reps, because there are no past reps to extrapolate from.

Wang also describes the model maturing rather than being discarded as the company grew. Deel expanded from a single account-executive segment serving small and mid-sized businesses to selling into the Fortune 500 across many countries, and the organization grew toward roughly a thousand people. On her telling the early capacity calculation matured into a full quota, capacity, and data-design function, but the underlying logic stayed continuous: outputs are still modeled from measured capacity and conversion rather than assigned from the top.

What the model rests on

The reliability of the method depends on the rate inputs, and Wang is direct that early on they are hypotheses rather than constants. She characterizes Deel's early outbound reality as one where the vast majority of emails reached people who did not care, which means the sixty percent interest rate and thirty percent close rate in the worked example function as a floor to be measured and calibrated, not a fixed truth. The discipline the pattern asks for is treating those conversion rates as numbers to test against reality as real pipeline accumulates, so the first quota is a starting estimate the team tightens rather than a target it defends.