A new model for assessment fast food customer behavior case study

Abstract

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. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. 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.

Customer Segmentation as a Revenue Generator for Profit Purposes

“…At the same time, they might also target health-conscious consumers seeking specific menu choices [21]. These distinct customer segments have varying needs and preferences, demanding tailored marketing strategies, menu offerings, and pricing structures [22].…”

Understanding Customer Loyalty in Indonesia Quick Service Restaurant Industry

“…Fast food businesses provide food and service to their customers in a competitive market. As a result, fast food restaurants must foster positive customer relationships (Mason et al, 2016;Momtaz et al, 2013). Customer loyalty is essential for establishing positive customer connections (Al-Ansi et al, 2018).…”

Delta model application for developing customer lifetime value

“…The model was developed to ameliorate the probable low response rates (typically 2 percent or less) in this industry, while targeting specific customers through marketing programs, such as sending them direct mails. Before the model application, the targeting objectives typically included the customers’ demographic profiles of the companies (Jafari Momtaz et al , 2013). Chen et al (2009) defined RFM values as the period of a customer’s last purchase, the number of his/her purchases made within a certain period, and the amount of money spent by him/her during a certain period, respectively.…”

“…Data mining is a popular tool for analyzing customer data. Using this tool, organizations can find valuable information, make better decisions and generate new opportunities based on customer characteristics (Jafari Momtaz et al , 2013). Actual data mining in based on selecting the primary data and breaking them down into the following tasks.…”