Multi-touch attribution (MTA) distributes credit for a deal across all the touchpoints that influenced it, from the first ad click to the final sales meeting, instead of crediting one touch alone. It reflects how B2B buying actually happens: many interactions, many people, over months.
Different models split the credit differently. Linear gives every touch equal weight. W-shaped weights the first touch, the lead-conversion touch, and the opportunity-creation touch most heavily. AI-driven models learn from your historical data which touches genuinely correlate with closed revenue. No single model is objectively correct, which is why it helps to see credit shift across models and judge which story to trust.
MTA's weakness is data. If touches are tied to fragmented, duplicated records, the model splits credit across ghosts. RevSure resolves identity first on its context layer, then runs several MTA models on the clean result, so the comparison is meaningful rather than noise.
For the broader picture see marketing attribution, and for the top-down complement see marketing mix modeling. RevSure's attribution platform runs multi-touch, mix modeling, and incrementality together.