2017/10/18 by Kaifeng Zhao, Zhao, Kaifeng, Seyed Hanif Mahboobi +3 · 3 citations
Business, Management and Accounting · Economics, Econometrics and Finance · Mathematics · #Consumer Behavior in Brand Consumption and Identification #Consumer Market Behavior and Pricing #Econometrics (econ.EM) #FOS: Computer and information sciences #FOS: Economics and business #Machine Learning (stat.ML) #Wine Industry and Tourism #econ.EM #stat.ML
paper · pdf · doi:10.48550/arxiv.1710.06561
arxiv created 2017/10/18 · openalex publication_date 2017/10/18 · arxiv updated 2017/10/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 (R2) 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.