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.
Why this matters
This term matters because it affects how you interpret performance and make budget decisions. If you use inconsistent definitions or windows, ROAS/CPA can look "better" while profit gets worse.
Practical checklist
- Write a 1-line definition for "MER (Marketing Efficiency Ratio)" that your team will use consistently.
- Keep the time window consistent (weekly/monthly/quarterly) when comparing trends.
- Segment results (channel/plan/cohort) before drawing big conclusions from blended averages.
- Use a calculator that references this term (e.g., MER Calculator) to sanity-check assumptions.
- Read the related guide (e.g., MER (blended ROAS): how to use it without fooling yourself) for context and common pitfalls.
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.