Both Bayesian and varying coefficient models are very useful tools in practice as they can be used to model parameter heterogeneity in a generalizable way. Motivated by the need of enhancing Marketing Mix Modeling at Uber, we propose a Bayesian Time Varying Coefficient model, equipped with a hierarchical Bayesian structure. This model is different from other time varying coefficient models in the sense that the coefficients are weighted over a set of local latent variables following certain probabilistic distributions. Stochastic Variational Inference is used to approximate the posteriors of latent variables and dynamic coefficients. The proposed model also helps address many challenges faced by traditional MMM approaches. We used simulations as well as real world marketing datasets to demonstrate our model superior performance in terms of both accuracy and interpretability.
翻译:贝叶斯和不同系数模型实际上都是非常有用的工具,因为它们可以用来以可概括的方式模拟参数的异质性。出于加强Uber市营销混合模型的需要,我们提议采用一个贝叶斯时代不同系数模型,配有贝叶斯等级结构。这一模型不同于其他不同时间的系数模型,因为根据某些概率分布,系数加权高于一套地方潜在变量。斯托克变异推论用于近似潜在变量和动态系数的后遗体。拟议的模型还有助于应对传统的MMMM方法所面临的许多挑战。我们利用模拟以及真实世界营销数据集来展示我们的模型在准确性和可解释性方面的优异性。