Sparse Regularization in Marketing and Economics

TL;DR

This paper explores the use of alpha-norm regularization for demand forecasting in marketing, demonstrating its ability to produce sparse, accurate models in high-dimensional settings.

Contribution

It introduces the application of alpha-norm regularization to marketing demand estimation, highlighting its advantages over traditional methods in high-dimensional problems.

Findings

Alpha-norm regularization yields accurate out-of-sample demand forecasts.

It effectively identifies key predictors like price and promotion.

The method outperforms many common machine learning techniques.

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.