Research on Consumer Behavior Portrait and Market Segmentation Strategy Under Data-Driven Background

This study focuses on the discussion of consumer behavior portrait and market segmentation strategy under the background of data-driven. With the rapid development of big data technology, enterprises can obtain and analyze massive consumer behavior data, which provides new possibilities for accurately portraying consumer portraits and implementing effective market segmentation. This paper first reviews the literature related to consumer behavior theory and market segmentation, and sorts out the application status of data-driven methods in this field. Subsequently, based on multi-source heterogeneous data, machine learning and artificial intelligence technologies are used to construct a multidimensional consumer behavior portrait model, including key dimensions such as demographic characteristics, consumption preferences, purchasing behavior, and lifestyle. On this basis, the cluster analysis method is used to segment consumers and identify consumer groups with similar characteristics. The research results show that the data-driven method can more accurately portray consumer characteristics and effectively improve the accuracy and refinement of market segmentation. Finally, this paper explores the practical application of data-driven consumer portraits and market segmentation strategies in product development, pricing strategies, marketing communications, etc., and puts forward suggestions for future research directions. This study provides theoretical guidance and practical reference for enterprises to better understand and serve consumers in the digital age.