Sales Capacity Model (Productivity, Not Quota)
When you need this method
Your sales plan is built top down: a revenue goal is divided into a quota per head, and that quota then quietly doubles as the performance expectation. Ramp time, unfilled seats and departures appear nowhere in the model. When the year is missed, nobody can say whether the team underdelivered or whether the plan was never reachable with the headcount that actually existed. In portfolio settings with ambitious growth targets, this is exactly where leadership discussions go off the rails.
Approach
- 1Derive steady-state productivity per role and segment from your own history, meaning ARR per fully ramped seller, explicitly not the quota.
- 2Measure the ramp curve as a vector, the share of steady-state productivity per quarter of tenure, and record the annual attrition rate.
- 3Lay out the existing team by hiring cohort, add the hiring plan per quarter, age the cohorts quarter by quarter and subtract departures.
- 4Convert the cohorts into fully ramped rep equivalents and multiply them by productivity per head to get capacity.
- 5Add a judgment row: deliberate corrections by leadership appear as their own visible line rather than hidden inside the drivers.
- 6Derive quota capacity from productivity capacity through over-assignment, separately per channel, because inside and field sellers differ in ramp length and quota size.
- 7Calibrate the quota multiple of on-target earnings against the sales efficiency you are aiming for, and check whether compensation and quota have drifted apart over the years.
- 8Translate the new business target into a pipeline requirement per demand source, so capacity does not sit idle for lack of opportunities.
- 9For assessment, re-run the model with the headcount and cohort mix you actually had and compare actual performance against that, not against the original plan.
Typical application
A typical B2B SaaS in the logistics space plans twelve million euros of new business, because twenty sellers times a 600,000 euro quota produces exactly that number. The year ends at 7.4 million, and the discussion turns to performance and compensation. The re-run capacity model tells a different story: on average 14.5 of the twenty seats were filled, four of them in their first two quarters of ramp, and measured steady-state productivity was 520,000 euros rather than 600,000. On the numbers, this team could carry roughly 6.9 million. Sales therefore delivered above capacity and below plan. The consequence is not a performance debate but earlier hiring, a shorter ramp, and a plan that is built from headcount going forward.
Limits and counter-indications
The model needs your own history. With fewer than roughly eight fully ramped sellers or less than two years of data you are mostly measuring noise; single large deals distort productivity per head more than any driver in the model. It computes supply, not demand: capacity without pipeline stays idle, which is why deriving the pipeline requirement is mandatory rather than optional. The ramp curve holds only while product, target segment and sales process stay stable; after a change of target customer it has to be measured again. The customary over-assignment of around twenty percent and the efficiency corridor of 0.75 to 0.8 are a convention and a benchmark from predominantly US datasets, not a law. And the older modelling literature counsels modesty: across a wide range of sales force sizes, profitability is flat. The model is good for order of magnitude and for assessment, not for the third decimal place. About the performance of any individual seller it says nothing.
How to measure impact
Track ARR per fully ramped seller, the ramp vector by quarter of tenure, annual attrition, fully ramped rep equivalents, and over-assignment. What you judge is the gap between actual new business and the capacity re-computed with the headcount you actually had, not the gap to plan.
Related methods
Sources
- 1.Sinha, Zoltners: Sales-Force Decision Models, Insights from 25 Years of Implementation, Interfaces 31(3 Supplement), S. 8-44, 2001 (opens in a new tab) · INFORMS / Interfaces · 2001 · academic and scholarly literature · supports the underlying mechanismCarries the methodological foundation of model-based sales force sizing, drawn from more than 2,000 projects across several hundred organisations. Two findings also bound what such models can claim: profitability is flat across a wide range of sales force sizes, and the authors state that models provide insights while people make decisions. That supports the judgment row and the modesty about decimal places. The paper predates subscription software and addresses neither ARR nor ramp cohorts.
- 2.How to Make and Use a Proper Sales Bookings Productivity and Quota Capacity Model, Kellblog (Dave Kellogg) (opens in a new tab) · Kellblog · 2020-02-15 · practitioner source · describes the methodCarries the procedure step by step: productivity as ARR per fully ramped head and explicitly not as quota, ramp as a vector across quarters of tenure, attrition, the construction of fully ramped rep equivalents, over-assignment of quota, an explicit judgment row for leadership overrides, and the assessment rule of comparing actual performance against the model re-run with real headcount rather than against the plan. It is a practitioner account; the text contains no empirical proof of effectiveness.
- 3.The Balderton Founder's Guide to B2B Sales, Kapitel Planning (Driver-Based Planning, Sales Booking Capacity Model) (opens in a new tab) · Balderton Capital · 2023 · practitioner source · describes the methodA second, independent rendering of the same procedure with concrete figures: one model block per seller type, an example ramp of roughly 10, 31, 75 and 91 percent of target productivity across the first four quarters against the smooth textbook assumption of 0, 25, 75 and 100 percent, and the convention of setting quota about twenty percent above expected productivity. The figures are illustrative values from an investor guide, not a measured distribution.
- 4.Is Your Sales Model Sustainable? Here's a Tool for That, Dale Chang, Scale Venture Partners (opens in a new tab) · Scale Venture Partners (Scale Studio) · 2019 · practitioner source · provides benchmark figuresSupplies the calibration between quota level and efficiency. In the worked example, net sales efficiency falls from 0.75 at a 5x quota to 0.59 at 4x and rises to 0.91 at 6x on-target earnings. The text cites a median corridor of 0.75 to 0.8 for companies up to roughly 50 million dollars in revenue from the Scale Studio dataset. This is a benchmark from predominantly US software companies and an arithmetic relationship, not causal evidence that a given quota level produces efficiency.
Origin: Kellogg / Chang