The Division and Characteristic Analysis of Selfsatisfied Consumption Groups Based on Clustering Algorithm

This research employs clustering algorithms to meticulously segment and analyze consumer groups in selfgratification consumption, aiming to uncover distinct patterns of consumption behavior and the underlying driving forces. Initially, a substantial amount of consumer purchase data is collected and cleaned, encompassing demographic information, consumption categories, purchase frequency, and amounts among other variables. Subsequently, various clustering algorithms (such as K-means, hierarchical clustering, DBSCAN, etc.) are utilized for modeling and analysis of the data to identify consumer segments with significant differences. Performance evaluation and optimization of each clustering model are conducted through metrics such as silhouette coefficient and SSE (Sum of Squared Errors), ensuring optimal segmentation results. The findings of this research highlight the pronounced heterogeneity among self-gratification consumer groups across multiple dimensions. By identifying and understanding these group characteristics, businesses can achieve more precise market segmentation, develop differentiated marketing strategies, and offer personalized product recommendations, thereby enhancing market competitiveness and customer satisfaction. In essence, this research not only offers practical implications for businesses seeking to better understand and engage with their target consumers but also enriches the academic discourse on consumer behavior, demonstrating the utility of advanced analytics in uncovering nuanced consumption patterns.