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Glossary · Returns and budgets

What is marginal ROAS?

Marginal ROAS is the return on the next unit of spend in a channel at its current level, read off the slope of the saturation curve. It is lower than the channel's average ROAS whenever returns are diminishing, and it is the figure budget decisions belong on.

Reviewed November 2026

By the Odins team · Updated November 2026 · About 3 minutes

Definition

Average ROAS answers "what did this channel earn per unit over the period?". Marginal ROAS answers "what would one more unit earn right now?". The platforms report the first; no platform reports the second, because it can only be read off a response curve, and that is what a marketing mix model estimates.

On a channel with diminishing returns the two diverge. The early units were cheap, the later ones expensive, and the average mixes them. The marginal figure is the slope at today's spend: the price of the next customer, or the next unit of revenue, in that channel.

How it works

Picture a curve of budget in against sales out. A channel delivering a thousand customers at an average cost of a thousand has in fact delivered many of them far cheaper, and the latest ones far dearer; the average lands at a thousand. The marginal cost is what the next customer would cost, and it is what the model uses to say how budgets should move.

Across markets the same logic applies. A customer may cost less on average in one market than another, and still the next customer may be cheaper in the second, because the first market is further along its curve. Marginal figures, not averages, decide where the money goes.

The response curve, and the two returns it separates
Where you have actually spentWeekly spend on the channelIncremental salesCeiling: the most the channel can addHalf-saturation pointhalf the ceiling is reached hereToday's spendMarginal return: the slope at today's spend, what the next unit earnsAverage return: total sales over total spend, what a report showsSame channel, two returns. Reports use the average. Budget decisions have to use the marginal.

In practice

Two uses. For allocation: move spend from channels where the marginal return is low to channels where it is high, until they are roughly level. The total does not change; the result does, typically 5 to 15% more effect from the same budget. For the budget level: set the return the business requires on its last unit of marketing, as a marginal ROAS or a maximum marginal CAC, and find the total where every extra unit still clears it. Above that total, money is spent below the bar.

The marginal figures also show where a plan has over- and under-invested against the optimum. That reading is useful without debating the exact number; the direction and the order of magnitude are what matter.

  • ROAS: Return on ad spend: revenue per unit of spend. Useful, as long as you know which ROAS you are looking at.
  • Marginal CAC: The cost of the next customer in a channel at today's spend. The CAC version of marginal ROAS, for businesses that count customers.
  • Saturation curve: The response curve per channel: how sales respond to spend, and where the next unit stops paying.
  • Diminishing returns: Each extra unit of spend in a channel earns a little less than the one before. Why the average is the wrong number to plan on.
  • Budget optimization: Finding the split and the total where the next unit works hardest, under the constraints the business has.
  • Scenario planning: Forecasting what a budget would return before committing it, and comparing several futures side by side.

Go one level deeper: Set the right marketing budget. Allocate budget across channels. Or go back to all terms in the glossary.

FAQ

Frequently asked questions.

Why doesn't my ad platform show marginal ROAS?

Because it cannot. Marginal return is the slope of a response curve estimated from spend and sales over time, and a platform only sees its own conversions. A marketing mix model estimates the curve, so it can read the slope.

Should a channel with a high average ROAS get more budget?

Only if its marginal ROAS is still high. A strong average with a flat curve is the classic trap: it looks like the best performer and gives little back for more money.

How precise is the marginal number?

It carries a range, like every estimate in a Bayesian model. The direction and the order of magnitude are usually clear even when the exact figure is not, and where the range is too wide to act on, a structured test narrows it.

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