Article information:To cite this document: Niyoosha Jafari Momtaz Somayeh Alizadeh Mahyar Sharif Vaghefi, (2013),"A new model for assessment fast food customer behavior case study", British Food Journal, Vol. 115 Iss 4 pp. 601 -613 Permanent link to this document: http://dx.If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information.
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AbstractPurpose -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.
In the last decade, online social networks have become an integral part of life. These networks play an important role in the dissemination of news, individual communication, disclosure of information, and business operations. Understanding the structure and implications of these networks is of great interest to both academia and industry. However, the unstructured nature of the graphs and the complexity of existing network analysis methods limit the effective analysis of these networks, particularly on a large scale. In this research, we propose a simple but effective node embedding method for the analysis of graphs with a focus on its application in online social networks. Our proposed method not only quantifies social graphs in a structured format but also enables user preference identification, community detection, and link prediction in online social networks. We demonstrate the effectiveness of our approach using a network of Twitter users. The results of this research provide valuable insights for marketing professionals seeking to target personalized content and advertising to individual users as well as social network administrators seeking to improve their platform through recommendation systems and the detection of outliers and anomalies.
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