Media Efficiency Ratio (MER)

Media Efficiency Ratio (MER) is calculated as: MER = Total Revenue / Total Ad Spend. Unlike channel-specific ROAS, which depends on attribution decisions, MER takes a top-down view: every dollar of revenue divided by every dollar of marketing spend, regardless of where the credit is assigned. This makes MER one of the most useful executive-level marketing metrics in the post-privacy attribution environment.

The case for MER comes from the unreliability of per-channel ROAS in 2026. With iOS privacy restrictions, cookie deprecation, and the over-crediting baked into platform-native attribution, the sum of channel ROAS often exceeds actual blended performance — a brand sees 4x ROAS on Meta, 5x on Google, 3x on TikTok, but actual MER is 2.8x because the channels are double-counting overlapping conversions. MER cuts through this by measuring outcomes that cannot be over-attributed: total revenue and total spend are unambiguous.

Benchmark MER varies by industry and stage. Profitable e-commerce brands typically run a blended MER between 2.5x and 4x. Subscription brands with strong retention can sustainably run lower MER (1.8-2.5x) because customer lifetime value extends beyond first-purchase revenue. Pre-profit growth-stage brands may operate at 1.2-2x MER during the CAC payback period.

The most useful application of MER is trend analysis. A declining MER even while channel ROAS appears stable indicates attribution drift — your dashboards are showing the same performance while real economics are degrading. A rising MER during a budget pullback period signals over-spending: revenue did not fall proportionally to spend cuts, meaning some of the eliminated spend was not driving incremental sales.

MER pairs naturally with incrementality testing. Channel ROAS tells you what your attribution platform credits to each channel; MER tells you what is actually happening across your marketing economy; incrementality testing tells you which channels are actually causing the lift. Run all three signals together and you have a much more accurate picture than any single one provides.