Earnout Scenario Modeler
Earnouts bridge valuation gaps — and generate more post-close litigation than any other deal term. Before signing, see what the seller actually takes home in three worlds. Nothing leaves your browser.
Payout mechanics modeled here
Below the threshold: zero. From threshold to 100% of target: linear interpolation from 0% to 100% of the max earnout. Above target: capped at max (no over-performance kicker in this version). This is the most common structure; if your LOI has a different curve — tiered cliffs, uncapped upside, catch-up provisions — the shape of the answer changes and so should the model. The point of running three scenarios is the delta, not the base case: if downside pays $0 and the seller is counting earnout as purchase price, the deal has a disagreement embedded in it.
Frequently asked questions
What does the Earnout Scenario Modeler do?
It models seller proceeds under a threshold-and-cap earnout across three performance scenarios you define — downside, base, upside. Below the threshold the earnout pays zero; between threshold and target it interpolates linearly; at or above target it pays the cap. You see total consideration in each world plus the spread actually at stake.
What structures does it not cover?
Tiered cliffs, uncapped over-performance kickers, catch-up provisions, multi-metric earnouts, and time-vested tranches. Those change the payout curve's shape, not just its parameters — model them separately before relying on any single-curve output.
Who should use it?
Sellers pressure-testing an LOI, buyers structuring a bid with a valuation gap, and advisors who want a shared screen for the earnout conversation. If the downside scenario pays zero and the seller is mentally booking the earnout as purchase price, the parties have a disagreement worth surfacing before signing.