A rep with twelve open opportunities makes a prioritization decision this morning.
They know which are the largest. They know which have been in the pipeline the longest. They have an intuition about which are easiest to close. But they don't know which one pays them the most — right now, given their current attainment, their plan rules, and the time left in the period.
That information doesn't exist in any screen in their current stack.
Why prioritization without data is a structural problem
This isn't a rep discipline problem. It's an information problem.
Compensation platforms show reps how much they've accrued. Some show progress toward quota. But none connect that current state to the specific opportunities they have in front of them and calculate which one is worth working now.
The result: every rep optimizes with incomplete information. They work the biggest deals, the ones that have been open the longest, the ones that feel most urgent. Not necessarily the ones that pay the most in the specific context of their period.
What the payout gradient is
The payout gradient is the expected incremental return from working a specific opportunity, calculated in the context of that rep's current state.
It's not the value of the opportunity. It's the value of that opportunity for this rep, now, given their current attainment, their plan rules, the time left in the period, and the other opportunities in their pipeline.
The difference matters. A $100K opportunity may pay less than a $40K one if the rep is already in a high commission tier and the $40K deal pushes them into an accelerator. Or it may pay far more if the $100K deal lands after the period closes.
The five factors of the calculation
Boundary value: How close the rep is to the next commission threshold. Crossing a tier can have a multiplier effect that completely changes the opportunity ranking.
Effort-normalized value: The return adjusted for likely closing effort. Not all opportunities of equal size require the same work.
Time feasibility: With the real time left in the period, which opportunities have a realistic chance of closing within that window? The calculation doesn't ignore the calendar.
Protect risk: What already-accrued commission is at risk if a prior sale reverses. Sometimes the priority isn't closing more — it's protecting what's already accrued.
Component headroom: How much room is left in each plan component before caps or tier changes modify the return on the next sale.
Three moves, not a list
The output of the calculation isn't a ranked list of twelve opportunities. It's Three Moves: the three specific actions that pay the most for this rep right now.
The manager sees an additional dimension: the wayfinder heatmap of their team — a concentration risk map showing how much of the period's revenue depends on how many reps, and which reps are near important thresholds.
A design rule
The system has one rule worth noting: it prefers silence over approximation.
If the data isn't sufficient to calculate the gradient with confidence, the system doesn't show an approximate number. It shows nothing. A rep who sees Three Moves can trust that calculation is correct.
Why it requires Revenue and Compensation together
The payout gradient cannot be calculated with compensation data alone. It needs to know what's in the pipeline, at what velocity it's closing, what the period pace looks like. That data comes from the Revenue Agent.
And it can't be calculated with revenue data alone. It needs to know the plan rules, the tiers, the accelerators, the current attainment. That data comes from the Compensation Agent.
It's the calculation that emerges when the loop is closed.