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Both Bayesian and varying coefficient models are very useful tools inpractice as they can be used to model parameter heterogeneity in ageneralizable way. Motivated by the need of enhancing Marketing Mix Modeling atUber, we propose a Bayesian Time Varying Coefficient model, equipped with ahierarchical Bayesian structure. This model is different from other timevarying coefficient models in the sense that the coefficients are weighted overa set of local latent variables following certain probabilistic distributions.Stochastic Variational Inference is used to approximate the posteriors oflatent variables and dynamic coefficients. The proposed model also helpsaddress many challenges faced by traditional MMM approaches. We usedsimulations as well as real world marketing datasets to demonstrate our modelsuperior performance in terms of both accuracy and interpretability.

Bayesian Time Varying Coefficient Model with Applications to Marketing Mix Modeling