This paper aims to analyze sequential patterns in customer behavior across different states in customer journey. Understanding these patterns can provide valuable insights into purchasing decisions, customer engagement, and retention. Our approach focuses on transaction-based behavior sequence which consists of different behavior objects that a user interacts with but with the same behavior type. Proposed methodology not only aids in revealing buying behavior but is also applicable to various types of sequential data, including historical purchasing trends, life event progressions, and cohort-based analyses. By uncovering hidden structures within behavioral sequences, businesses can enhance personalization, optimize marketing strategies by market segments with similar behavior, and improve user experience. The findings contribute to the broader field of data-driven decision-making, offering computational tools that could be implemented in diverse business situations.