Sparse Regularization in Marketing and Economics
This addresses demand estimation challenges in marketing and economics, but it is incremental as it applies an existing regularization method to a specific domain.
The paper tackled the problem of demand forecasting in marketing and economics using sparse alpha-norm regularization, achieving accurate out-of-sample estimates for promotion lift effects in a grocery store sales dataset.
Sparse alpha-norm regularization has many data-rich applications in Marketing and Economics. Alpha-norm, in contrast to lasso and ridge regularization, jumps to a sparse solution. This feature is attractive for ultra high-dimensional problems that occur in demand estimation and forecasting. The alpha-norm objective is nonconvex and requires coordinate descent and proximal operators to find the sparse solution. We study a typical marketing demand forecasting problem, grocery store sales for salty snacks, that has many dummy variables as controls. The key predictors of demand include price, equivalized volume, promotion, flavor, scent, and brand effects. By comparing with many commonly used machine learning methods, alpha-norm regularization achieves its goal of providing accurate out-of-sample estimates for the promotion lift effects. Finally, we conclude with directions for future research.