Abstract
Purpose – Nowadays, because of more availability of products, there is an increasing need for companies to establish a strong relationship with their customers. As the fast food industry is not an exception and has a competitive environment, analyzing customers' behavior helps bridge this gap. Data mining techniques help to segment customers as well as to drive improved customer relationship management. This paper seeks to address these issues.Design/methodology/approach – This study proposes a new model based on RFM model for defining customers' value as well as using K‐means algorithm to segment restaurants' customers. In addition, the authors combine a new category in the account portfolio analysis in order to analyze the behavior of each cluster.Findings – A real dataset of an Iranian fast food restaurant chain is employed to show the procedure of the authors' model. The customers are segmented into four clusters. The clusters are analyzed and named based on categories in the account portfolio analysi...
Published Version
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