Analysis of Smart Home Boutique Marketing Based on Data Mining

In the digital age of the 21st century, the emergence of smart home technology has brought about a profound transformation in human lifestyles, particularly with the advent of the Internet of Things (IoT), artificial intelligence (AI), and wireless communication technologies. Nevertheless, cross-border e-commerce, as one of the important channels for smart home sales, confronts a complex and diverse global market and consumer needs, and there is an urgent need to optimize market positioning and marketing strategies through advanced technologies such as data mining and machine learning to adapt to these challenges and opportunities. Therefore, this paper integrates qualitative and quantitative analysis, thus providing effective business insights and optimization paradigms for cross-border e-commerce operations of smart home enterprises through data mining techniques and economic methods such as K-Means clustering, shopping basket algorithm, and Boston matrix. This not only enhances the market competitiveness of smart home enterprises in the era of big data, but also provides new ideas for their transformation and upgrading. Furthermore, it establishes a feedback loop between theoretical concepts and practical applications, offering both practical value and significant research insights.