  This paper examines and proposes several attribution modeling methods that
quantify how revenue should be attributed to online advertising inputs. We
adopt and further develop relative importance method, which is based on
regression models that have been extensively studied and utilized to
investigate the relationship between advertising efforts and market reaction
(revenue). Relative importance method aims at decomposing and allocating
marginal contributions to the coefficient of determination (R^2) of 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 model to additive model. We claim that our new
approaches are more flexible and accurate in modeling the underlying
relationship and calculating the attribution values. We use simulation examples
to demonstrate the superior performance of our new approaches over traditional
methods. We further illustrate the value of our proposed approaches using a
real advertising campaign dataset.
