A new model for assessment fast food customer behavior case study An Iranian fast-food restaurant

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 analysis. The result of this analysis shows that there is no significant difference between the behavior of the most valuable customer and customers who have left the restaurant. Therefore, restaurant managers should seek other reasons for detecting churn behavior. Originality/value - This paper helps managers in the fast food industry to readily analyze their customer behavior in order to understand their needs and establish strong relationships.