Basics

Ad-performance terms, decoded: ROAS, CPA, CTR, holdout, incrementality

The handful of terms you keep seeing in ad reports — ROAS, CPA, CTR, conversion rate, holdout, incrementality, attribution. One line each on what they mean, why they matter, and why incrementality is the one that actually tells you if your ads worked.

·5 menit baca·Tim engineering DEXUN AdWhiz

Konten ini hanya tersedia dalam bahasa Inggris dan bahasa Tionghoa Sederhana, sehingga ditampilkan dalam bahasa Inggris.

Ad reports are full of jargon, but only a handful of terms matter day to day. Here’s one line each — plus the one concept most people skip and shouldn’t: incrementality.

ROAS (return on ad spend)

= revenue from ads ÷ ad spend. ROAS 4 means $4 back per $1 spent. Caveat: it measures correlation, not necessarily causation — some conversions would happen even without the ad.

CPA (cost per acquisition)

= ad spend ÷ conversions. Lower is better, but read it against order value: high-AOV businesses can tolerate a higher CPA.

CTR (click-through rate) & conversion rate

CTR = clicks ÷ impressions (how appealing the creative/targeting is); conversion rate = conversions ÷ clicks (how well the landing page/product converts). High CTR + low conversion rate usually points at the landing page or an audience mismatch.

Attribution

Which ad touch a conversion gets "credited" to. Different models (last-click, data-driven, …) give different numbers — which is why platform self-reported ROAS tends to look optimistic.

Holdout (control group)

A randomly held-out slice of audience or campaigns that gets NO ads / no optimization, as a control. It’s the key tool for turning correlation into causation.

Incrementality

The conversions the ads actually added — the "ran vs. didn’t-run" difference, measured with a holdout. This is the gold standard for whether ads worked: a campaign with a great-looking ROAS can have low incrementality (those people would have bought anyway).

DEXUN AdWhiz applies the same incrementality logic to its own work: it holds out a random ~15% of your campaigns from its recommendations as a control group and compares the two groups on your dashboard, with an uncertainty range — and no number until there is enough data. Per-change savings are labelled as estimates.

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