Characterization of Frequent Online Shoppers using Statistical Learning with Sparsity

TL;DR

This paper presents a method combining retail analytics and sparse statistical learning to interpret shopping preferences of frequent online gift store customers, revealing key preferences and revenue-driving products.

Contribution

It introduces a novel approach that uses sparsity-based statistical learning on bipartite graphs to understand customer preferences in online shopping.

Findings

Identifies key customer preferences and product drivers.

Provides interpretable insights into shopping behavior.

Enhances understanding of revenue-impacting products.

Abstract

Developing shopping experiences that delight the customer requires businesses to understand customer taste. This work reports a method to learn the shopping preferences of frequent shoppers to an online gift store by combining ideas from retail analytics and statistical learning with sparsity. Shopping activity is represented as a bipartite graph. This graph is refined by applying sparsity-based statistical learning methods. These methods are interpretable and reveal insights about customers' preferences as well as products driving revenue to the store.