Customer data mining for lifestyle segmentation

Highlights? We propose a method for segmentation in retailing, based on customers' lifestyle. ? We identify typical shopping baskets by clustering the transactional records. ? We infer the lifestyle corresponding to each typical shopping basket. ? Customers are assigned to a segment based on the similarity with the typical baskets. ? We identify actions to reinforce the relationship between companies and customers. A good relationship between companies and customers is a crucial factor of competitiveness. Market segmentation is a key issue for companies to develop and maintain loyal relationships with customers as well as to promote the increase of company sales. This paper proposes a method for market segmentation in retailing based on customers' lifestyle, supported by information extracted from a large transactional database. A set of typical shopping baskets are mined from the database, using a variable clustering algorithm, and these are used to infer customers lifestyle. Customers are assigned to a lifestyle segment based on their purchases history. This study is done in collaboration with an European retailing company.

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