SMART HOME CONSUMER NEEDS BASED ON K-MEANS CLUSTERING AND KANO MODEL

With the rapid development and market expansion of smart home technology, the diversification and differentiation of consumer needs have become increasingly prominent. Based on survey data from 431 consumers, this study employs the K-means clustering algorithm to segment smart home consumers, using the silhouette coefficient method to determine the optimal number of clusters as 4, dividing consumers into bottom-tier frugal, middle-class pleasure-seeking, upper-middle comfort-seeking, and high-net-worth groups. The research further analyzes consumer need characteristics using the KANO model, identifying home security and product appropriateness as must-be attributes, entertainment and media control and installation services as expected attributes, and product reputation as an attractive attribute. The results indicate that smart home consumers across different income and age levels exhibit distinctly different need characteristics and purchasing power, while product safety and appropriateness remain fundamental needs across all groups. This research provides data support and theoretical guidance for smart home product development and marketing strategies, offering practical significance for promoting the healthy development of the smart home industry.