Characterization of Frequent Online Shoppers using Statistical Learning with Sparsity
This work addresses the need for businesses to tailor shopping experiences for frequent online shoppers, but it is incremental as it applies existing sparse statistical learning methods to a specific retail domain.
The authors tackled the problem of understanding frequent online shoppers' preferences by developing a method that combines retail analytics with sparse statistical learning on a bipartite graph representation of shopping activity, resulting in interpretable insights into customer preferences and revenue-driving products.
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.