Papers › Characterization of Frequent Online Shoppers using Statistical Learning with Sparsity
Characterization of Frequent Online Shoppers using Statistical Learning with Sparsity
Rajiv Sambasivan, Mark Burgess, Jörg Schad, Arthur Keen, Christopher Woodward, Alexander Geenen, Sachin Sharma
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.
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