Temporal Dynamics of Online Shopping: A Multidimensional Clustering Approach for Optimizing Time-Specific Advertising Strategies

Abstract This study clusters consumers according to their hourly online shopping consumption patterns and develops optimized advertising strategies that can be practically applied by companies. Specifically, we considered multidimensional variables related to consumers, including demographic characteristics, shopping frequency, information search and content consumption characteristics, and lifestyle traits. We explored consumer types by comparing various clustering algorithms and applying the optimal clustering method for each time period. The analysis results show significant differences in payment amounts according to payment time periods. Regarding consumer types, consumption tendencies are distinctly differentiated by payment time periods, with each cluster showing different characteristics. This study is significant in that it makes a practical contribution to optimizing marketing activities by time period by establishing advertising strategies tailored to changing consumer characteristics and more efficiently allocating advertising budgets through consumer segmentation based on actual payment data.