Sales Commission Plan What-If Modeling
RevOps proposes a new accelerator tier or a change to how multi-year deals get credited, everyone in the room nods that it sounds reasonable, and it goes live at the start of the new plan year. Three months in, someone notices that the change accidentally doubles payout on a deal type that used to be common, or guts earnings for a rep segment nobody was thinking about when the plan was drafted, and now it's a mid-year plan amendment and an uncomfortable conversation instead of a caught mistake. The plan was never run against real historical deals before rollout — it was reasoned about in the abstract.
STARTING PRICE
From €799
Complex tier · Multi-system orchestration, custom logic, and higher-volume or higher-risk processing.
Get a quote →Saves roughly 8-15 hrs per plan design cycle for RevOps and finance.
How the automation works
We take a proposed commission plan change and run it against a full year or more of actual closed deals, calculating what every rep would have earned under both the old and new plan side by side, broken out by rep, deal type, and quota attainment band. This surfaces exactly which deal patterns benefit or lose under the change and by how much, before the plan is finalized rather than after paychecks go out. Multiple proposed variants can be modeled against the same historical set so RevOps and finance can compare tradeoffs directly instead of debating hypotheticals, and the final recommendation goes to sales leadership with the actual payout delta attached, not just a description of the mechanic.
Process flow
- 01
Proposed plan change drafted trigger
RevOps or sales leadership drafts a proposed change to accelerator tiers, credit rules, or quota mechanics for the upcoming plan year.
- 02
Pull historical closed-deal data integration
A full prior period of closed-won deals is pulled with rep, deal size, deal type, and close date, matched to who was assigned quota credit at the time.
- 03
Calculate payout under old and new plan ai
Every historical deal is run through both the current plan's rules and the proposed plan's rules, producing a per-rep, per-deal payout comparison rather than an aggregate estimate.
- 04
Surface the biggest winners and losers ai
Reps, deal types, or quota bands with the largest payout swing under the new plan are surfaced explicitly, so the plan's real behavior is visible before rollout instead of discovered after.
- 05
Compare multiple plan variants output
If more than one plan design is under consideration, each variant runs against the same historical set so leadership compares actual payout tradeoffs side by side.
- 06
Attach payout modeling to plan approval output
The finalized plan proposal goes to approval with the historical payout comparison attached, so the sign-off reflects modeled impact rather than a description of the mechanic alone.
Inputs
- Proposed commission plan rules and accelerator tiers
- Historical closed-won deal data with rep attribution
- Current plan rules for baseline comparison
- Quota assignments for the historical period
Outputs
- Per-rep payout comparison under old vs new plan
- Ranked list of largest payout winners and losers
- Multi-variant plan comparison
- Historical-data-backed plan approval package
Works with
Prefer a fully custom build instead of an off-the-shelf integration? We scope both options during your free consultation — most jobs like this one work fine on standard connectors, but higher-volume or non-standard systems sometimes need bespoke API work, reflected in the complex tier.
Where this goes wrong if you get it wrong
- Modeling against too short a historical window — one quarter instead of a full plan year — misses seasonal deal patterns that a plan year actually needs to handle, like a Q4-heavy close pattern that behaves very differently under a new accelerator than a flat quarter does.
- A plan that looks fine in aggregate can still badly under- or over-pay a specific rep segment — new reps still ramping, or reps who work almost exclusively on renewals rather than new business — and the modeling needs to break out by segment, not just report a single blended payout delta.
- Historical deals reflect the old plan's incentives, so reps may have structured deals differently than they would under the new plan's rules — modeling shows what the new plan would have paid on old behavior, not necessarily what behavior the new plan will actually produce, and that gap needs to be stated explicitly rather than presented as a precise forecast.
- This tool models payout mechanics; it does not decide whether a plan change is fair, competitive, or the right call for the business — that's a compensation design and leadership decision informed by the modeling, not replaced by it.
Frequently asked questions
Does this replace the compensation design decision?
No — it gives the team modeled payout data to inform the decision, but choosing the plan structure itself remains a leadership and RevOps call informed by more than payout mechanics alone.
How much historical data does it need?
At minimum a full plan year of closed deals to capture seasonal patterns; more history improves confidence, especially for teams with long sales cycles or lumpy deal timing.
Can it model changes mid-year, not just at plan rollout?
Yes — the same modeling applies to any proposed amendment, run against deals closed so far in the current period plus historical data for context.
Does it account for reps who joined or left partway through the historical period?
Yes, payout is calculated only against the deals and quota period each rep was actually active for, rather than assuming a full year of tenure for everyone.