Learning Parameters for a Generalized Vidale-Wolfe Response Model With Flexible Ad Elasticity and Word-of-Mouth

In this research, we investigate a generalized form of Vidale-Wolfe (GVW) model. One key element our modeling work is that the GVW model contains two useful indexes representing advertiser's elasticity and word-of-mouth (WoM) effect, respectively. Moreover, discuss some desirable properties model, present deep neural network-based estimation method to learn its parameters. Furthermore, based on three realworld datasets, conduct computational experiments validate identified properties. addition, also potential advantages over econometric models. The research outcome shows both ad index WoM have significant influences advertising responses, has models advertising, in terms several interesting phenomena drawn from practical situations. learning-based provide basis support big data-driven analytics decision makings; meanwhile, experimental findings illuminate critical managerial insights for advertisers various forms.