Utility-frequency analysis using customer market basket data in the retail sector

Gökçe Baysal Türkölmez and İpek Deveci Kocakoç

International Journal of Business Information Systems, 2022, vol. 41, issue 1, 69-81

Abstract: In the retail sector, customers' shopping preferences are effective in determining the sales policies of stores. Due to the space problem, especially in small stores, it is not possible to store all the products as in big stores and to find places on shelves. The paper aims to make recommendations about product mix to small stores of a retail market chain in the Aegean region (Turkey). Six-month basket data of customers who have a membership card and who make purchases in regional stores of this market chain have been investigated by using frequent itemset mining and high utility itemset mining algorithms. Not only the data of frequency but also the utility data for items give useful information to determine the product mix of the small stores.

Keywords: data mining; customer market basket analysis; frequent itemset mining; high utility itemset mining. (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:ids:ijbisy:v:41:y:2022:i:1:p:69-81

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