Marginal ROAS and scaling calculator
Blended ROAS falls slowly as you scale. Marginal ROAS, the return on the extra spend, falls fast, and it is the number that decides whether the next increase makes or loses money.
How it is calculated
Enter two or more budget levels you have actually run, ideally on first-party order data, each held long enough to exit the learning phase. The calculator sorts them by spend and compares each step with the one before.
Blended ROAS (level) = Revenue ÷ Spend Marginal ROAS (step) = (Revenue₂ − Revenue₁) ÷ (Spend₂ − Spend₁) Profit after ads = Revenue ÷ Break-even ROAS − Spend Scale while Marginal ROAS ≥ Break-even ROAS
Profit after ads uses your break-even ROAS as the conversion from revenue to contribution: at break-even, each unit of revenue carries 1 ÷ break-even ROAS of contribution. The level with the highest profit after ads is where the curve peaks.
How to use the result
Report blended ROAS to leadership for planning. Move budgets on marginal ROAS. When a step falls below break-even, do not cut to the previous level straight away: the fix is often new creative or a new audience that shifts the whole curve up, not less money.
Questions
What is marginal ROAS?
Marginal ROAS is the revenue generated by the additional spend divided by that additional spend. If raising spend from ₹10 lakh to ₹15 lakh lifts revenue from ₹30 lakh to ₹39 lakh, marginal ROAS is 9 ÷ 5 = 1.8, even though blended ROAS is still 2.6.
Why does ROAS fall when I scale?
Every channel has diminishing returns. As budget grows, platforms reach less responsive audiences, frequency rises and auctions get more expensive, so each extra unit of spend buys less revenue than the last.
When should I stop scaling ad spend?
Stop increasing spend when marginal ROAS falls below your break-even ROAS. At that point the extra spend loses money even if blended ROAS still looks healthy.
How much should I increase budgets at a time?
Steps of about 20% are a practical default. They are large enough to measure and small enough to keep campaigns stable. Hold each step long enough to exit the learning phase before reading the result.
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