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Metrics·6 Jul 2026·9 min read

ROAS, MER, contribution margin: which number should run your brand?

Platform ROAS is the number everyone quotes and the worst one to steer by. Here's how the three levels of ad-profitability math fit together — with the break-even formula and a worked example in rupees.

TL;DR
  • Platform ROAS is self-reported by the platform that spent your money — it inflates under double-counting, view-through and modeled conversions.
  • MER (total revenue ÷ total ad spend) is the honest executive headline. Your break-even MER ≈ 1 ÷ contribution-margin %.
  • Contribution margin is the only number that knows whether each order made money. Steer budgets with MER, kill campaigns with contribution math.

Every D2C team has lived this meeting: Meta's dashboard says 4.1× ROAS, the founder is looking at the bank account, and the two stories don't rhyme. Neither side is lying, exactly — they're reading different numbers that answer different questions. The problem is that most brands steer with the weakest of the three.

The three levels, in one table

MetricFormulaWho reports itWhat it's good for
Platform ROASplatform-attributed revenue ÷ platform spendMeta / Google, about themselvesDirectional creative & campaign comparison inside one platform
MER (blended ROAS)total store revenue ÷ total ad spendYour store + your ad accountsThe executive headline: is the whole machine profitable?
Contribution marginrevenue − COGS − shipping − fees − returnsOnly you (needs cost data)Whether each order, product and campaign actually makes money

Why platform ROAS flatters itself

Ad platforms grade their own homework. Three mechanics push the number up: double-counting (Meta and Google both claim the same order when a customer touched both), view-through attribution (someone scrolled past your ad, bought via search two days later — Meta counts it), and modeled conversions (post-iOS-14.5, a chunk of "conversions" are statistical estimates, not observed purchases). Sum every platform's claimed revenue and you'll routinely get 130–180% of what your store actually took.

A real example from our own audits
A Bengaluru food brand we audited spent ₹1.71L on Meta in 30 days. Meta's attributed view said things were fine. The Shopify join told the truth: ₹85.5k of total store sales — a blended MER of 0.50×. The account was spending ₹2 to make ₹1, before product costs. No platform dashboard will ever show you that number.

MER: the honest headline

Marketing Efficiency RatioMER = total store revenue ÷ total ad spend (same period)

MER can't be gamed by attribution because it doesn't use attribution. All revenue over all spend. Its weakness is the flip side of its strength: it won't tell you which campaign to cut — it tells you whether the machine as a whole earns its fuel.

Your break-even MER is a formula, not a feeling

Break-evenbreak-even MER ≈ 1 ÷ contribution-margin %

A brand with 60% contribution margin (after COGS, shipping, fees) breaks even at MER ≈ 1.7×. At 40% margin, break-even is 2.5×. This is why "we're at 2× blended, we're fine" can be true for one brand and a slow bleed for its neighbour. Compute yours before you judge any ad number.

Contribution margin: where campaigns go to be judged

Worked example. Average order value ₹586. COGS at 42% takes ₹246, leaving ₹340 before acquisition. If your cost per purchase is ₹1,746, every paid first order loses roughly ₹1,406 — and no ROAS above 1 changes that. The order only turns profitable if the customer comes back. That's not a marketing insight; it's arithmetic most dashboards never do, because the cost data lives in a spreadsheet the ad platform never sees.

LevelQuestion it answersCadence
Platform ROASWhich creative/audience is relatively better inside this platform?Daily, media buyer
MERIs total ad spend earning its keep against break-even?Weekly, founder / growth lead
Contribution marginWhich products, campaigns and cohorts actually make money?Weekly–monthly, finance + growth

When platform ROAS is the right tool

Inside one platform, comparing two creatives launched the same week to the same audience — platform ROAS is fine, because the attribution bias applies roughly equally to both. It breaks the moment you compare across platforms, across time (attribution windows shift), or against money in the bank. Use it as a ranking signal, never as a P&L.

The operating rule

  • Daily: media buyer ranks creatives on platform metrics — knowing they're inflated, caring only about relative order.
  • Weekly: growth lead reads MER against the brand's computed break-even, and moves budget.
  • Weekly–monthly: contribution margin per campaign decides what lives, dies, or scales.

The brands that get this wrong aren't innumerate — their data is just fragmented. Spend sits in two ad accounts, revenue in Shopify, costs in a sheet, returns in the 3PL portal. Reconciling them weekly by hand takes hours, so it doesn't happen, so platform ROAS wins by default. Fixing the plumbing is the whole game.

FAQ

Is MER the same as blended ROAS?
Yes — same formula, different name. "MER" is the common term in D2C finance circles; "blended ROAS" in media teams.
Should MER use total revenue or new-customer revenue?
Pick one and hold it. Total revenue is simpler; new-customer revenue is stricter (it stops retention from subsidising bad acquisition). Strong operators track both: blended MER for health, new-customer MER (aMER) for acquisition truth.
What's a good MER for Indian D2C?
There's no universal benchmark — it's your break-even (1 ÷ CM%) plus the profit you demand. Food brands at ~60% margin need 1.7×+ just to break even; fashion with heavy RTO often needs 3×+.
My platform ROAS went up but MER went down. How?
Classic sign of retargeting cannibalisation: platforms claim credit for orders (often from returning customers) that would have happened anyway. Spend shifted toward "provable" audiences, total efficiency fell.
Do I need a data team to compute contribution margin per campaign?
No — you need order-level attribution (which click drove which order) joined to a per-SKU cost sheet. That's plumbing, not data science. It's exactly what Meerkats automates.

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