A customer sees an Instagram ad on Tuesday, googles the brand on Thursday, clicks a search ad, leaves, comes back Saturday through an abandonment email and buys. Which channel gets the sale? Every one of the marketing attribution models answers differently, every answer is defensible, and none is true. Attribution isn't measurement; it's an opinion about credit, formalised.
This sits under the GA4 pillar and underwrites the budget logic in the paid media budget piece.
| Model | Credit goes to | Systematic bias |
|---|---|---|
| Last click | Final touchpoint | Flatters closers: brand search, email, retargeting |
| First click | First touchpoint | Flatters openers: prospecting social, content |
| Linear | Split evenly | Flatters being present, rewards channel sprawl |
| Position-based | Ends weighted, middle shared | A compromise with two thumbs on the scale |
| Data-driven | Algorithmic, from conversion path patterns | Opaque, and limited to the touches the platform can see |
That last clause is the modern catch. Since iOS App Tracking Transparency arrived in 2021 and browsers tightened cookies, no platform sees the whole journey. Data-driven attribution is a sophisticated model of an incomplete dataset, and each ad platform additionally grades its own homework: add up the conversions Meta and Google each claim and you'll routinely exceed your actual order count.
Three layers, used for different decisions:
A worked example of the stack catching a lie: a retargeting campaign reports a spectacular return at the platform layer. A two-week pause shows total revenue barely moves. The campaign was claiming credit for customers already coming back. The truth layer ruled; the platform layer was redecorating.
Reports still need a setting. GA4 defaults to data-driven attribution and that's fine as the comparative lens, with one discipline: pick it and stop switching. Changing models between months makes trends unreadable, and the trend is the only part of attribution that's reliably informative.
Data-driven or position-based as the default reporting lens, held constant. But govern budget decisions with MER and margin, and settle disputes with holdout tests, not model swaps.
Each platform attributes any conversion it touched within its window. Both touched the journey, both claim it, and the sum exceeds reality. That's why platform numbers compare campaigns, not channels.
Measuring what happens to total revenue when you remove or add a channel, via geo splits or pauses. It's the closest thing to ground truth a small business can run, and it's nearly free.
Qwrki is the operating layer that runs ecommerce delivery for small businesses, so attribution stops being a monthly argument and becomes a standing read. We wire the truth layer, the platform layer and the holdout checks into one place, then hold the model constant so the trend stays legible. Book a call and we'll walk through your current numbers and where the credit is being double-counted.
The GA4 ecommerce events that actually matter, the setup checklist, and why GA4 never matches Shopify. A practical guide for store owners.
Server-side tracking and the Conversions API in plain English: what signal it recovers, what it can't, the real costs, and who actually needs it.
The twelve ecommerce KPIs that matter, organised in three tiers: weekly operating numbers, monthly health checks and quarterly strategy metrics.
