论文标题
具有灵活的广告弹性和口碑的广义Vidale-Wolfe响应模型的学习参数
Learning Parameters for a Generalized Vidale-Wolfe Response Model with Flexible Ad Elasticity and Word-of-Mouth
论文作者
论文摘要
在这项研究中，我们研究了Vidale-Wolfe（GVW）模型的广义形式。我们建模工作的一个关键要素是GVW模型分别包含两个有用的索引，这些索引分别代表广告商的弹性和口碑（WOM）效应。此外，我们讨论了GVW模型的一些理想属性，并提出了基于深层的神经网络（DNN）的估计方法来学习其参数。此外，基于三个Realworld数据集，我们进行了计算实验以验证GVW模型并确定了属性。此外，我们还讨论了GVW模型的潜在优势，而不是计量经济学模型。研究结果表明，AD弹性指数和WOM指数都对广告响应产生了重大影响，而GVW模型在广告的计量经济学模型中具有潜在的优势，这是从实际的广告情况下得出的几种有趣现象。 GVW模型及其基于深度学习的估计方法为支持大数据驱动的广告分析和决策制定提供了基础；同时，这项研究的属性和实验发现阐明了各种广告形式的广告客户的关键管理见解。
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.
