<![CDATA[With the rapid growth of the tourism industry, particularly within the hotel and airline sectors, understanding customer behavior and segmentation has become crucial for driving strategic decisions. However, customer segmentation in these sectors often faces challenges due to the complexity of transactional data and varying service demands. This study addresses these challenges by designing a data warehouse using a Star Schema architecture, aimed at integrating hotel and airline booking data from 2023 to 2024. The methodology follows the Nine-Step Kimball approach, coupled with the Extract, Transform, Load (ETL) process, to transform transactional data from OLTP systems into a format suitable for Online Analytical Processing (OLAP). The data warehouse features fact and dimension tables that support in-depth analysis of customer segments, booking trends, and performance across service categories and time periods. Key findings show that the data warehouse significantly improves the ability to segment customers based on booking behavior, seasonality, and service preferences, leading to more effective decision-making. This integrated system provides a robust foundation for data-driven strategies, allowing businesses in the tourism industry to optimize customer targeting, improve service offerings, and enhance operational efficiency. Keywords: Data Warehouse, Star Schema, ETL, Hotel Booking, Flight]]>
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