Revenue-based Attribution Modeling for Online Advertising
This work addresses attribution modeling for online advertising, providing more flexible and accurate methods for marketers, but it is incremental as it builds on existing regression-based approaches.
The paper tackles the problem of attributing revenue to online advertising inputs by proposing new attribution modeling methods based on relative importance, extending decomposition techniques like dominance analysis and relative weight analysis from linear to additive models, and demonstrates superior performance over traditional methods through simulations and a real dataset.
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.