A revenue forecast predicts what will close. Unit economics measure what closing it costs and what it is worth afterward. The two models share most of their inputs, and teams that keep them in separate files end up planning spend against revenue assumptions that the forecast has already abandoned.
The inputs both models share
| Input | Effect on the forecast | Effect on unit economics |
|---|---|---|
| Average deal size at close | Sets quarter revenue | Sets CAC per dollar of new ARR |
| Win rate | Sets pipeline conversion | Sets selling cost per win |
| Sales cycle length | Sets timing of revenue | Delays payback and consumes selling capacity |
| Churn and contraction | Sets net new ARR | Sets LTV and the ratio built on it |
| Gross margin | Minor effect on top line | Direct effect on payback and LTV |
Forecast at the value deals actually close for
Pipeline value and closed-won value are different numbers in most CRM instances, and most deals close for less than the value the CRM carries. Pete Furseth illustrates the gap this way: a pipeline carrying an $80,000 average deal size against $40,000 in average closed-won value. A forecast built on the pipeline figure overstates revenue, and a CAC per dollar of new ARR computed on the same assumption understates cost by the same factor. Both errors point the same way, which is why they compound rather than offset.
The correction is to model the close-value distribution from history rather than trusting the amount field, then use that adjusted value in the forecast and the economics together.
When conditions shift, both models break at once
Forecasts miss when the business or the market changes and the model still runs on old assumptions. A new competitor creates pricing pressure and average deal size falls. Rates rise, buyers slow down, and win rates drop. Uncertainty stretches the time from qualified to closed. A territory change distracts reps while pipeline coverage still reads healthy.
Each of those moves revenue and unit economics in the same direction. Deal size falling raises CAC per dollar of ARR. Cycles stretching pushes payback out. Win rates dropping raises cost per win. A quarterly re-forecast that leaves payback untouched hands the board a revenue number and a spending plan that no longer belong to the same scenario.
Wire them to the same cuts
Run sales forecasting and unit economics on identical segment definitions, identical periods, and identical close-value assumptions. When a segment's forecast moves, its payback should move in the same review. That is what makes the plan a single model instead of two documents that agree by coincidence, and it is the same discipline behind a repeatable process for how to forecast revenue.
Frequently Asked Questions
Why do the forecast and the unit economics model usually disagree?
They are built by different teams on different assumptions. Sales forecasts from pipeline value and rep judgment. Finance computes CAC and payback from closed revenue and actual spend. When the forecast assumes deals close at pipeline value and history says they close lower, the two models describe different businesses.
Which forecast input moves unit economics the most?
Average deal size at close. It sets revenue in the forecast and sets CAC per dollar of ARR at the same time, so a decline shows up twice. Win rate is second, because a lower win rate means the same selling cost is spread across fewer wins.
How often should unit economics be re-run against the forecast?
Every time the forecast assumptions change materially, which in practice means monthly. A company that re-forecasts revenue without re-running payback keeps spending against economics that stopped being true.
Should unit economics be forecast by segment?
Yes, and on the same segment cuts the revenue forecast uses. A blended payback across a profitable segment and an unprofitable one produces a plan that funds the wrong motion while the company-level number still looks acceptable.
Put these metrics to work
ORM builds custom revenue forecast models that turn concepts like unit economics in revenue forecasting into prescriptive action for your team.
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