Ebsta × Pavilion reported a 19% new-logo win rate in its 2025 benchmark. The comparison below is an illustration, not a prediction for your team.
The figures below start from a twenty-rep example using that 19% new-logo benchmark as an input. It may not match your mix, deal values or win rate. Change any input to test your own assumptions.
In this example, one front-line manager covers 8.5 reps. At 19.7 open deals each, that is a 168-deal book to inspect. Change the span above to match your team.
Two minutes per deal produces the review time above. At the example win rate, of those deals would not be won; the calculation cannot tell you which ones. The manager still needs evidence to prioritise a 168-deal book.
Managers compress those books into one number, and a revenue leader takes that number upward. Coverage alone does not explain which deals support the call. A weekly inspection needs to test the evidence beneath it.
Only the 19% new-logo rate is sourced from an external benchmark. The other starting inputs are editable examples, and the outputs below are arithmetic on those inputs.
The Ebsta and Pavilion 2025 benchmark reports this rate for new-logo opportunities. It is a comparison input, not an overall B2B average or a claim about your team. Put your own rate in and the illustration re-runs against it.
This is an editable illustration, not a measured average for your organisation. If your managers cover six, or twelve, change it and the book size and hours move with it.
Quota divided by average deal gives what a rep must win. That divided by the win rate gives what they must carry. One divided by the win rate is the coverage the number needs. Coverage times the win rate is where you actually land. The working sits under each line so you never have to take the result on trust.
No uplift is assumed, no efficiency gain, no compounding, and no outcome from buying anything. Change an input and the only thing that moves is the arithmetic that depends on it. If a figure cannot be shown, it is not on the page.
One rounding note, so the numbers reconcile if you check them by hand. Deal counts are carried unrounded between lines, so a manager's book may differ by one or two from multiplying the displayed figures yourself.
Every open deal scored on the deals you have actually won, not an industry average. You see what the coverage is worth in week two, not week eleven.
The whole book ordered by exposed revenue, with the read on each deal inline. Monday is an inspection, not a reconstruction.
No economic buyer. No compelling event. Stalled past your own days-in-stage baseline. A close date nobody has defended. Each one priced, with the rep who owns it attached. Scored on the commercial activity that actually happened, not the picklist a rep set last month.
Every commit call graded against what actually landed. The board pack exports from the same scored data the team works from, so the operating number and the board number cannot drift apart.
Every figure is derived from your own data and carries the model version and score date it was produced under. The AI layer explains the maths in plain language. It never produces a number and it never overrides one. Salesforce access is read-only. Nothing writes back, and your reps do not change how they work.
The 30-minute walkthrough uses demo data to show a first forecast review. Bring your own win rate if you want to test the coverage assumption, but no pipeline export or Salesforce connection is required.
Source. The 19% new-logo win rate comes from the Ebsta × Pavilion 2025 GTM Benchmarks. Rep count, quota, deal value, manager span, and review time are illustrative defaults. Coverage ratios, book size and hours recompute against your inputs.
Brian, Founder, CommitControl. CommitControl is a product of ZeusGlobal Nexus Limited, a private company limited by shares registered in Ireland (company no. 820109).
Application and CRM storage are in Frankfurt. Limited US processing by the explanation layer is disclosed in the data processing terms.