Sparse Regularization in Marketing and Economics

Abstract

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.

Feature Selection for Explaining Yellowfin Tuna Catch per Unit Effort Using Least Absolute Shrinkage and Selection Operator Regression

“…In psychology and social sciences, it is used to study individual behavior, decision-making processes, and key influencing features in social networks [15]. In marketing and consumer research, it helps identify key features influencing consumer purchasing decisions [16]. The application scope of Lasso regression technology is extensive, not limited to the aforementioned fields, and it spans multiple research directions.…”