Definition
MER (also called blended ROAS) is total revenue divided by total marketing spend over the same period. It's useful for top-down health checks.
Formula
MER = total revenue / total marketing spend
Example
If total revenue is $500k and total marketing spend is $100k, MER = $500k / $100k = 5.0.
Common mistakes
- Using MER alone to optimize channel budgets (it hides what's working).
- Not adjusting for seasonality, promos, and pricing changes.
Measured as
MER = total revenue / total marketing spend
Misused when
- Using MER alone to optimize channel budgets (it hides what's working).
- Not adjusting for seasonality, promos, and pricing changes.
Operator takeaway
- Use MER (Marketing Efficiency Ratio) only inside a stable attribution rule, conversion definition, and time window so campaign comparisons stay honest.
- If performance changes, check whether the metric moved for a real business reason or because the measurement setup changed underneath you.
Next decision
- Quantify the impact with MER Calculator if you need to turn the definition into an operating assumption.
- Read MER (blended ROAS): how to use it without fooling yourself if the decision depends on interpretation, policy, or trade-offs beyond the raw formula.
Where to use this on MetricKit
Calculators
- MER Calculator: Calculate MER (Marketing Efficiency Ratio / blended ROAS) and estimate break-even and target MER from margin assumptions.
- ROAS Calculator: Calculate Return on Ad Spend (ROAS) and estimate contribution profit after ad spend.
Guides
- MER (blended ROAS): how to use it without fooling yourself: A practical guide to MER: what it is, how it differs from ROAS, how to compute break-even/target MER, and common pitfalls.
- Paid ads measurement hub: ROAS, MER, marginal ROAS, and incrementality: A practical hub for paid ads measurement: connect ROAS to profit, use MER for top-down truth, watch marginal ROAS for scale, and validate incrementality with holdouts.
- Attribution vs incrementality: what to trust, when, and how to test: A practical guide to attribution vs incrementality: common attribution models, window pitfalls, how MER/marginal ROAS fit in, and how to run holdout/geo tests.