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Purpose: This study was conducted to segment the delivery food market and to develop customized products and services. Methods: This study analyzed 636 responses collected from customers who ordered delivery food. Statistical analyses were conducted using the SPSS program (ver. 25.0) for frequency analysis, χ 2 -test, one-way analysis of variance, factor analysis, and cluster analysis. Results: Four factors were extracted by exploratory factor analysis (safety-orientation, convenience-orientation, taste-orientation, and economy-orientation) to explain the consumers’ food-related lifestyles. The results of cluster analysis indicated that the ‘low-interest group’, ‘convenience and economy-oriented group’, and ‘gourmet and economy-oriented group’ should be regarded as the target segments. Characteristic analysis of each cluster showed that lowinterest group had higher rates of married (67.1%) and living with family (85.4%) than other clusters. The convenience and the economy-oriented group had higher rates of living alone (28.9%) than others. The gourmet and the economy-oriented group had a higher percentage of unmarried (62.0%) than the others. In addition, the average age of convenience and economy-oriented group (32.3 years) and gourmet and economy-oriented group (32.5 years) were significantly lower than the safety seeker (40.0 years) (p < 0.001). Difference analysis of the consumption practice according to the cluster, revealed significant differences in the order frequency (p < 0.001), main day to order (p < 0.05), source of information about delivery food (p < 0.001), order method (p < 0.001), and co-consumer (p < 0.01). In addition, the convenience and the economy-oriented group had significantly higher overall satisfaction than the others (p < 0.001). Conclusion: These findings suggest that customer segmentation based on a food-related lifestyle can be used to build a successful marketing strategy. Therefore, restaurant managers and delivery platform operators should consider developing products and services according to the segmentation to maximize customer satisfaction.
