What the main models do with credit
Last-click attribution gives all the credit to the final touchpoint before purchase. It is simple and decisive, but it overvalues brand search and undervalues the channels that created the demand in the first place, because the last click is usually the harvest, not the planting.
Data-driven attribution, the GA4 default, distributes credit statistically across the touches that contributed. It paints a fairer picture of the journey, at the cost of being harder to inspect: the model decides the split, and you see the output rather than the reasoning. Most businesses meet attribution the first time two platforms both claim the same sale, and the store's books show only one order.
Why no model is the truth
Every attribution model is a lens, not a measurement. The customer journey is genuinely messy, and any rule for splitting credit is a simplification of it. That would be manageable on its own, but there is a second problem: every ad platform grades its own homework.
Meta credits Meta, Google credits Google, and each counts conversions by its own rules. This is why the platforms together routinely claim more conversions than your store recorded. None of them is lying by its own definitions; they are just all answering in their own favor. Knowing this is enough to read platform reports correctly: as claims to evaluate, not facts to total.
The practical approach for most businesses
Three habits keep attribution useful without letting it become a research project:
- Keep the attribution setting consistent, so trends stay comparable month to month even if the absolute numbers are imperfect.
- Sanity-check platform claims against backend revenue, because actual orders are the one number with no model behind it.
- Treat blended results as the final scoreboard: total revenue against total marketing cost.
Attribution is for steering budget between channels. The blended number is for knowing whether the whole machine is profitable, and that is the question that pays the bills.