How does the Data-Driven Attribution (DDA) model differ from models that assign all credit to a single touchpoint?

Data-driven attribution differs because it uses machine learning to calculate the actual contribution of each touchpoint. Single-touchpoint models apply a fixed rule: last click gives everything to the final interaction, first click gives everything to the first. Those rules are easy to explain but they are assumptions, not findings. DDA instead compares the paths of users who converted with those who did not, and works out how much each touchpoint genuinely shifted the likelihood of conversion, distributing fractional credit accordingly. The practical consequence is that channels which assist conversions without closing them — generic search, video, upper-funnel activity — stop being invisible. Under last click they often look worthless and get cut; under DDA their real contribution is measurable, which usually changes where budget should go.