In short
Marketing attribution assigns credit for a conversion to the touchpoints that contributed to it. Models range from single-touch, such as first-click or last-click, which give all credit to one interaction, to data-driven attribution, which distributes credit statistically across the path. The choice of model changes which campaigns look successful and therefore where budget flows, so it is a consequential decision rather than a reporting detail. The point often missed is that attribution is bounded by signal, because a platform can only attribute conversions it can see and match, so incomplete or poorly matched data limits every model equally. Improving attribution usually starts with improving the underlying signal, delivering complete, deduplicated, consent-aware conversions, before arguing about which model to apply on top. Attribution and measurement quality are layered: the model decides how credit is shared, but the signal decides how much there is to share in the first place. Fix the signal, then choose the model.
The models, briefly
First-click and last-click give all credit to one touchpoint. Position and time-decay models split it by rule. Data-driven attribution, now the default on Google and Meta, distributes credit statistically from observed conversion paths.
Signal decides the model’s quality
An attribution model can only weigh the touchpoints and conversions it can see. Feeding platforms complete, matched conversion data (including offline outcomes) changes attribution more than switching models does.
What limits every attribution model?
A team debates first-click versus data-driven attribution while a third of conversions never reach the platform because of blocked pixels. Sending those conversions server-side changes the reported picture more than any model switch does.
Reference: Google Analytics Help, get started with attribution