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

Yanwu Yang, Baozhu Feng, Daniel Zeng

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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

Original languageEnglish (US)
Pages (from-to)69-79
Number of pages11
JournalIEEE Intelligent Systems
Volume36
Issue number5
DOIs
StatePublished - 2021

Keywords

  • Vidale Wolfe model
  • advertising models
  • budget decisions
  • deep neural network
  • response models

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Artificial Intelligence

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