Linear Attribution

Linear attribution splits a conversion's credit equally among all the touchpoints in the user's path; every channel gets an equal share.

Linear attribution is the simplest multi-touch model, and its founding assumption is that every touch mattered equally. Its advantage over single-click models is that it zeroes no channel and sees the whole path; its weakness comes from that very equality assumption, because in reality not all touches are equal — a passing visit differs from a deep interaction, yet linear counts both the same. So linear attribution is a good starting point when you do not yet know which stage of the journey matters most, but if you know discovery or closing plays a larger role, a model like U-shaped or time-decay that assigns unequal weights is more honest. Like any multi-touch model, running it requires stitching a user's sessions together.