Identifying Demand Effects in a Large Network of Product Categories

TL;DR

This paper introduces a method to identify demand effects across a large network of product categories using sparse VAR modeling, revealing asymmetric influences and roles of different categories in retail demand dynamics.

Contribution

It proposes a novel sparse estimation approach for the VAR model to uncover a parsimonious, data-driven product category network without prior structural assumptions.

Findings

Cross-category effects extend beyond simple substitutes and complements.

Destination categories are highly influential in the network.

Convenience and occasional categories are most responsive.

Abstract

Planning marketing mix strategies requires retailers to understand within- as well as cross-category demand effects. Most retailers carry products in a large variety of categories, leading to a high number of such demand effects to be estimated. At the same time, we do not expect cross-category effects between all categories. This paper outlines a methodology to estimate a parsimonious product category network without prior constraints on its structure. To do so, sparse estimation of the Vector AutoRegressive Market Response Model is presented. We find that cross-category effects go beyond substitutes and complements, and that categories have asymmetric roles in the product category network. Destination categories are most influential for other product categories, while convenience and occasional categories are most responsive. Routine categories are moderately influential and moderately responsive.