Customer Segmentation Based on Dining Preferences in Full-Service Restaurants

Abstract

This study was designed to identify the factors affecting five groups pruned by customer decision factors (menu, atmosphere, price, health, and brand reputation) that customers consider when selecting a full-service restaurant. The analytical method applied in this study was Decision Tree Analysis. As Decision Tree Analysis enables a researcher to identify and target desirable customer groups, it is an ideal analysis to identify differences between groups. A total of 390 questionnaires were used for the data analysis. The result indicated that depending on the group, classifying variables were different. That is, five groups have their own unique attributes. The results of this research would be valuable for marketers or managers in the restaurant industry to understand targeting customer segmentation.

Keywords:

• segmentation
• customer attributes
• classifying variable
• Decision Tree Analysis
• CHAID