Revenue-based attribution modeling for online advertising

Abstract

This article examines and proposes several attribution models that quantify how revenue should be attributed to online advertising inputs. We adopt and further develop relative importance methods, which are based on regression models that have been extensively studied and utilized to investigate the relationship between advertising efforts and market reaction (revenue). The relative importance methods aim at decomposing and allocating marginal contributions to the coefficient of determination ( R2) of the regression models as attribution values. In particular, we adopt two alternative submethods to perform this decomposition: dominance analysis and relative weight analysis. Moreover, we demonstrate an extension of the decomposition methods from standard linear models to additive models. We claim that our new approaches are more flexible and accurate in modeling the underlying relationship and quantifying the attribution values. We use simulation examples to demonstrate the superior performance of our new approaches to traditional methods. We further illustrate the value of our proposed approaches using a real advertising campaign data set.