Multi-Touch Attribution Models

Attribution models summarize how observed marketing touchpoints relate to conversions. Last-click assigns all credit to the final recorded interaction. Linear attribution splits credit equally across included touchpoints. Time-decay gives more credit to interactions closer to the conversion according to a chosen decay rule. Data-driven models use observed paths and statistical methods to allocate credit. Shapley approaches estimate each channel’s contribution across combinations, while Markov methods analyze transitions through paths and may use removal effects. These approaches answer different accounting questions and rely on assumptions about observed data. Missing impressions, cross-device behavior, offline sales, privacy restrictions, and selection into advertising can distort paths. A touchpoint that receives credit may correlate with conversion without causing it. Attribution can help compare reporting conventions, but budget decisions should also use incrementality tests, experiments, and business context. Analysts should document the conversion window, eligible channels, identity resolution, and model version. Comparing model outputs can reveal sensitivity to assumptions, but choosing the method that supports a desired narrative is not sound evaluation. A model that allocates 30 percent of credit to a channel does not imply removing it would reduce sales by 30 percent. Attribution is a measurement framework for observed journeys; causal impact requires a counterfactual comparison. Attribution also depends on whether an interaction is observed at all. Privacy restrictions, consent settings, and platform reporting gaps can remove important exposures from a path.