Multidimensional Consumer Segmentation in AI Digital Human Image Adaptation: An Empirical Study of Age, Cultural Preferences, and Consumption Motivations

Abstract

Starting from the perspective of consumer group classification, this paper explores the impact of age, cultural orientation, and consumer motivation on the adaptability of AI digital humans. Using data mining and regression methods, it presents the diverse demands of various consumer groups for digital human images. Empirical analysis reveals that age, cultural background, and consumer motivation significantly influence the formation of digital human image preferences. Younger groups are more receptive to interactive digital human images, while older groups tend to prefer designs that are both functional and practical. Cultural background and consumer motivations significantly influence consumer preferences. Global consumers have a high preference index for cultural identity, and social motivation generally ranks first among all groups. With these insights, this paper injects theoretical impetus into the optimized design of AI digital human images and points out practical paths for brand marketing strategies. The study further reveals that the design of digital human images needs to be personalized for consumers of different ages, cultural backgrounds, and motivations to improve the market acceptance of digital humans.