Hierarchical marketing mix models with sign constraints

Area of Science:

• Marketing Science
• Econometrics
• Statistical Modeling

Background:

• Marketing mix models (MMMs) are crucial for evaluating marketing campaign performance.
• Existing MMMs often use multi-stage estimation, which can be suboptimal.
• Accurately capturing marketing effects like carryover and shape is challenging.

Purpose of the Study:

• To propose a comprehensive marketing mix model (MMM) with enhanced capabilities.
• To incorporate hierarchical structures, carryover, shape, and scale effects.
• To implement sign restrictions for business-sense coefficient constraints.

Main Methods:

• Simultaneous parameter estimation using constrained maximum likelihood.
• Application of a Hamiltonian Monte Carlo algorithm for complex model fitting.
• Development of a unified approach, avoiding traditional multi-stage processes.

Main Results:

• The proposed model effectively captures complex marketing dynamics.
• Simultaneous estimation provides more accurate and reliable parameter estimates.
• Demonstrated application on real-world datasets showcasing practical utility.

Conclusions:

• The novel MMM offers a more robust and integrated approach to marketing effectiveness analysis.
• This method improves upon traditional MMMs by estimating all parameters concurrently.
• The findings provide valuable tools for data-driven marketing strategy optimization.