What Is a Good ROAS?
There is no universal “good ROAS.” A number that is excellent for a high-margin subscription business may be unprofitable for a low-margin ecommerce store.
Start with gross margin
Higher margins allow a lower breakeven ROAS. In practice, document your assumptions and compare the reported number with the business outcome you can verify. Measurement is most useful when the team understands what the data includes, what it misses and which decision the metric is meant to support.
Include variable costs
Shipping, fulfillment, fees and commissions affect unit economics. In practice, document your assumptions and compare the reported number with the business outcome you can verify. Measurement is most useful when the team understands what the data includes, what it misses and which decision the metric is meant to support.
Consider repeat value
If customers reliably buy again, first-order ROAS can understate the value of acquisition. In practice, document your assumptions and compare the reported number with the business outcome you can verify. Measurement is most useful when the team understands what the data includes, what it misses and which decision the metric is meant to support.
Know your cash constraints
A profitable 12-month LTV model can still create a cash-flow problem if payback is too slow. In practice, document your assumptions and compare the reported number with the business outcome you can verify. Measurement is most useful when the team understands what the data includes, what it misses and which decision the metric is meant to support.
Set your own thresholds
Build targets from your economics rather than copying a benchmark from another business. In practice, document your assumptions and compare the reported number with the business outcome you can verify. Measurement is most useful when the team understands what the data includes, what it misses and which decision the metric is meant to support.
What to do next
Choose the next guide based on the decision you are trying to make. If your main problem is collection quality, start with server-side tracking. If the data exists but channels disagree, study attribution models. If you are evaluating software, compare implementation, integrations and decision value—not marketing claims alone.