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 business analytics approach 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. a ‘breakfast’ visit to purchase cereal, milk, bread, cheese etc. We also suggest a semi-supervised feature selection approach that uses the product taxonomy as input and suggests customized categories as output. This approach is utilized to balance the product taxonomy tree that has a significant effect on the data mining results. We demonstrate the utility of our approach by applying it to a real case of a major European 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.