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arXiv · 2111.06057

Characterization of Frequent Online Shoppers using Statistical Learning with Sparsity

Rajiv Sambasivan·Mark Burgess·Jörg Schad·Arthur Keen·Christopher Woodward·Alexander Geenen·Sachin Sharma
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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.

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Rajiv Sambasivan, Mark Burgess, Jörg Schad, Arthur Keen, Christopher Woodward, Alexander Geenen, Sachin Sharma. 2021-11-11. Characterization of Frequent Online Shoppers using Statistical Learning with Sparsity. https://arxiv.org/abs/2111.06057

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