Abstract

Basket analytics is a powerful tool in the retail context for acquiring knowledge about consumer shopping habits and preferences. In this paper, we propose a clustering-based artifact that mines customer visit segments from basket sales data. We characterize a customer visit by the purchased product categories in the basket and identify the shopping intention or mission behind the visit e.g. ‘breakfast’ visit to purchase cereal, milk, bread, cheese etc. We demonstrate the utility of the artifact by applying it to a real case of a major fast-moving consumer goods (FMCG) retailer. Apart from its theoretical contribution, the proposed approach extracts knowledge that may support several decisions ranging from marketing campaigns per customer segment, redesign of a store’s layout to product recommendations.

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