Modeling & Optimizing Plans
Before you change a live comp plan, Earnest lets you see the impact first. The Simulator (under Admin → Simulator) has two ways to model a change and one way to search for the best change automatically. Everything here is read-only — nothing you do on this screen touches a rep's pay. Adopting a change always goes through the normal plan editor, so it schedules forward and stays fully auditable. The Simulator is part of the standard feature set rather than an upgrade.
Forecast: a quick what-if
The Forecast view projects cost at a chosen attainment level. Pick a plan, set an attainment %, and adjust tiers, cap, floor, or base rate. Earnest recomputes every active rep on that plan and shows:
- Total current vs proposed cost, and the delta.
- A cost curve across attainment levels (so you can see where accelerators bite).
- Per-rep impact, so no individual is surprised.
Use it for fast "what if we bumped the top tier to 2×?" questions.
Backtest: replay real history
The Backtest view is the honest version of a forecast. Instead of a synthetic attainment level, it re-runs your actual booked activity for the last few months through the proposed plan, using the real commission engine — the same code that generates payouts. Choose how many months back to replay, and Earnest shows what your reps would have earned under the new plan versus what they actually earned.
Because it uses the real engine over real data, a backtest is the most trustworthy preview of a plan change.
Optimize: search for a plan that hits a budget
The Optimize tab works backwards. Instead of you proposing numbers, you give Earnest a goal and it searches for plan parameters that meet it:
| Input | Meaning |
|---|---|
| Budget | The total comp spend you're targeting (in your base currency). |
| Tolerance ±% | How close to the budget a candidate must land to count. |
| Max rep move % | A guardrail — reject any plan that would swing an individual rep's pay by more than this. |
| Months back | The backtest window the search scores against. |
| Seed | The search is deterministic: the same seed always produces the same result, so a run is reproducible and shareable. |
Earnest scores every candidate on three axes — Budget (how close to target), Alignment (does higher performance still earn more), and Stability (how little it disrupts current pay) — and returns the recommended plan plus a few alternatives, each with its projected cost and largest per-rep moves.
Large organizations: for very large plans the search automatically narrows how many candidates it explores (you'll see a note). The results still cover every rep — only the breadth of the search is reduced, never the accuracy of any single candidate's numbers.
If nothing fits, Earnest tells you so directly ("no feasible candidate found") — loosen the max rep move or widen the tolerance and run again.
Adopting a candidate
The optimizer never writes a plan on its own. When you like a candidate, copy its tier multipliers, cap, and base rate into the plan's Configure rules editor. Saving there uses Earnest's normal forward-supersede flow:
- The change takes effect from the date you choose — it never rewrites a period that already has published payouts.
- It appears under Scheduled changes, where it can be reviewed and retracted (an undo) before it takes effect — a scheduled change you cancel shows up under Recently canceled so there's a clear trail.
That means you can model aggressively here with zero risk: nothing is committed until you deliberately save it in the plan editor, and even then it's reversible until it goes live.