Hierarchical Marketing Mix Models with Sign Constraints
This work addresses the need for more accurate and interpretable marketing mix models for businesses, though it appears incremental by building on existing statistical methods with specific constraints.
The authors tackled the problem of measuring marketing effectiveness by proposing a hierarchical marketing mix model that incorporates business constraints and simultaneous parameter estimation, demonstrating its application on real datasets.
Marketing mix models (MMMs) are statistical models for measuring the effectiveness of various marketing activities such as promotion, media advertisement, etc. In this research, we propose a comprehensive marketing mix model that captures the hierarchical structure and the carryover, shape and scale effects of certain marketing activities, as well as sign restrictions on certain coefficients that are consistent with common business sense. In contrast to commonly adopted approaches in practice, which estimate parameters in a multi-stage process, the proposed approach estimates all the unknown parameters/coefficients simultaneously using a constrained maximum likelihood approach and solved with the Hamiltonian Monte Carlo algorithm. We present results on real datasets to illustrate the use of the proposed solution algorithm.