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

TL;DR

This paper introduces a generalized Vidale-Wolfe model incorporating ad elasticity and word-of-mouth effects, estimated via deep neural networks, validated with real datasets, and shown to outperform traditional econometric models in advertising analytics.

Contribution

The paper presents a novel generalized Vidale-Wolfe model with flexible parameters and a DNN-based estimation method, validated on real data, offering new insights for advertising response analysis.

Findings

Both ad elasticity and WoM significantly influence advertising responses.

The GVW model outperforms econometric models in capturing advertising phenomena.

Deep learning enables effective parameter estimation for complex advertising models.

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 (DNN)-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.